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"cells": [ { "cell_type": "markdown", "metadata": { "id": "uKVSNlMlKsF3" }, "source": [ "# IIS - Deep Learning Lab\n", "\n", "Welcome to the deep learning IIS lab! In this lab we will explore some basic deep learning techniques, some known pitfalls and possible applications. \n", "This lab is based on the official Keras documentation and the following book: https://www.deeplearningbook.org.\n", "First, let's install the required libraries." ] }, { "cell_type": "code", "metadata": { "id": "p6_txW5rKupE" }, "source": [ "!pip -q install --user tensorflow\n", "!pip -q install --user keras\n", "!pip -q install --user sklearn\n", "!pip -q install --user numpy\n", "!pip -q install --user seaborn\n", "!pip -q install --user matplotlib" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Asf3oOaLLQ19" }, "source": [ "# Warming up with XOR\n", "The first part of the exercise will focus on feedforward neural networks. As a warm-up let's consider a simple XOR problem.\n", "\n", "![image.png](data:image/png;base64,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)" ] }, { "cell_type": "code", "metadata": { "id": "lPo-E1ALKszB" }, "source": [ "# Required imports first\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import normalize\n", "import numpy as np\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.datasets import load_iris\n", "from tensorflow.random import set_seed\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "from sklearn.metrics import accuracy_score\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "np.random.seed(42)\n", "sns.set_style(\"whitegrid\")\n", "\n", "\n", "def plot_history(history):\n", " \"\"\"\n", " A simple function that plots the loss history of a trained neural network.\n", " Args:\n", " history: A Keras loss object\n", " \"\"\"\n", " sns.lineplot(list(range(len(history.history['loss']))), history.history['loss'])\n", " plt.xlabel(\"Epoch\")\n", " plt.ylabel(\"Loss\")\n", " _=plt.title(\"Loss during training\")\n", " plt.show()" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4C-ifugyLKEI", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "65424249-6e98-4a54-d919-19414af7f936" }, "source": [ "# Let's create the data set next.\n", "# Assuming Bernoully-distributed variables A,B, we draw 1k samples.\n", "# by computing XOR(A,B) we get the target.\n", "\n", "A = np.random.randint(0,2, 1000)\n", "B = np.random.randint(0,2, 1000)\n", "\n", "C = np.logical_xor(A,B)\n", "C = np.array(C, dtype = int)\n", "\n", "# Let's joint the arrays into a single numpy matrix\n", "whole_space = np.vstack([A,B,C]).T\n", "print(whole_space)\n", "\n", "# Let's make a split of the data\n", "train_data = whole_space[:750,[0,1]] # Training instances\n", "train_target = whole_space[:750, 2] # Training targets\n", "test_data = whole_space[750:,[0,1]] # Testing instances\n", "test_target = whole_space[750:, 2] # Testing targets" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[0 1 1]\n", " [1 0 1]\n", " [0 0 0]\n", " ...\n", " [1 0 1]\n", " [1 0 1]\n", " [0 0 0]]\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "tr1HOHkZTeAp" }, "source": [ "The loss function considered will be the _binary cross-entropy_;\n", "$$\\textrm{BCE}(\\theta) = - \\frac{1}{n}\\sum_{i = 1}^{n}\\underbrace{y_i - \\log{(p(y_i; \\theta))}}_{\\textrm{positive class}} + \\underbrace{(1-y_i) \\log{(1 - p(y_i; \\theta))}}_\\textrm{Negative class}.$$\n", "Here, $p(y_i; \\theta)$ is the probability of the positive label given the parameter space $\\theta$, $y_i$ the ground truth label (either 0 or 1) and $n$ the number of samples." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "iq-uL2DJLKL-", "outputId": "6f28f89f-e01c-44a4-ad18-ab8960075592" }, "source": [ "# We will use the Keras API for constructing the neural networks\n", "model = keras.Sequential() # Initialization of the Sequential object. Homework: What other types of APIs are possible?\n", "model.add(keras.Input(shape=(2,))) # First layer is just an input wrapper.\n", "model.add(layers.Dense(2, activation=\"elu\")) # Dense + activation\n", "model.add(layers.Dense(1, activation=\"sigmoid\")) # Dense + final activation. Discussion time: why sigmoid?\n", "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Model compilation. Optimizer and loss are needed here.\n", "\n", "# Discussion time: Let's discuss the loss.\n", "\n", "history = model.fit(train_data, train_target, epochs=1000, batch_size=16)\n", "plot_history(history)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/1000\n", "47/47 [==============================] - 2s 4ms/step - loss: 0.7887 - accuracy: 0.4613\n", "Epoch 2/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7763 - accuracy: 0.4787\n", "Epoch 3/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7660 - accuracy: 0.4787\n", "Epoch 4/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7576 - accuracy: 0.4787\n", "Epoch 5/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7503 - accuracy: 0.4787\n", "Epoch 6/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7440 - accuracy: 0.4787\n", "Epoch 7/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7386 - accuracy: 0.4787\n", "Epoch 8/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7338 - accuracy: 0.4787\n", "Epoch 9/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7296 - accuracy: 0.4787\n", "Epoch 10/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7259 - accuracy: 0.4787\n", "Epoch 11/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7223 - accuracy: 0.4787\n", "Epoch 12/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7191 - accuracy: 0.4787\n", "Epoch 13/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7161 - accuracy: 0.4787\n", "Epoch 14/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7135 - accuracy: 0.4787\n", "Epoch 15/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7111 - accuracy: 0.4787\n", "Epoch 16/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7091 - accuracy: 0.4787\n", "Epoch 17/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7069 - accuracy: 0.4787\n", "Epoch 18/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.7051 - accuracy: 0.4787\n", "Epoch 19/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7035 - accuracy: 0.3853\n", "Epoch 20/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7017 - accuracy: 0.4787\n", "Epoch 21/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.7002 - accuracy: 0.4787\n", "Epoch 22/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6988 - accuracy: 0.4787\n", "Epoch 23/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6971 - accuracy: 0.4787\n", "Epoch 24/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6957 - accuracy: 0.4787\n", "Epoch 25/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6940 - accuracy: 0.4787\n", "Epoch 26/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6926 - accuracy: 0.4787\n", "Epoch 27/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6910 - accuracy: 0.4787\n", "Epoch 28/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6894 - accuracy: 0.4787\n", "Epoch 29/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6879 - accuracy: 0.4787\n", "Epoch 30/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6863 - accuracy: 0.4787\n", "Epoch 31/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6848 - accuracy: 0.4787\n", "Epoch 32/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6834 - accuracy: 0.4787\n", "Epoch 33/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6818 - accuracy: 0.7013\n", "Epoch 34/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6805 - accuracy: 0.7507\n", "Epoch 35/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6790 - accuracy: 0.5920\n", "Epoch 36/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6770 - accuracy: 0.7507\n", "Epoch 37/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6752 - accuracy: 0.7507\n", "Epoch 38/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6734 - accuracy: 0.7507\n", "Epoch 39/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6714 - accuracy: 0.7507\n", "Epoch 40/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6694 - accuracy: 0.7507\n", "Epoch 41/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6672 - accuracy: 0.7507\n", "Epoch 42/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6650 - accuracy: 0.7507\n", "Epoch 43/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.7507\n", "Epoch 44/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6601 - accuracy: 0.7507\n", "Epoch 45/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6575 - accuracy: 0.7507\n", "Epoch 46/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6548 - accuracy: 0.7507\n", "Epoch 47/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6518 - accuracy: 0.7507\n", "Epoch 48/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6487 - accuracy: 0.7507\n", "Epoch 49/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6455 - accuracy: 0.7507\n", "Epoch 50/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6421 - accuracy: 0.7507\n", "Epoch 51/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6387 - accuracy: 0.7507\n", "Epoch 52/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6352 - accuracy: 0.7507\n", "Epoch 53/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6312 - accuracy: 0.7507\n", "Epoch 54/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6272 - accuracy: 0.7507\n", "Epoch 55/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6232 - accuracy: 0.7507\n", "Epoch 56/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6189 - accuracy: 0.7507\n", "Epoch 57/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.6145 - accuracy: 0.7507\n", "Epoch 58/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6099 - accuracy: 0.7507\n", "Epoch 59/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6051 - accuracy: 0.7507\n", "Epoch 60/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.6002 - accuracy: 0.7507\n", "Epoch 61/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5952 - accuracy: 0.7507\n", "Epoch 62/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5904 - accuracy: 0.7507\n", "Epoch 63/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5848 - accuracy: 0.7507\n", "Epoch 64/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5793 - accuracy: 0.7507\n", "Epoch 65/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5737 - accuracy: 0.7507\n", "Epoch 66/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5680 - accuracy: 0.7507\n", "Epoch 67/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5621 - accuracy: 0.7507\n", "Epoch 68/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5564 - accuracy: 0.7507\n", "Epoch 69/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5503 - accuracy: 0.7507\n", "Epoch 70/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5440 - accuracy: 0.7507\n", "Epoch 71/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5379 - accuracy: 0.7507\n", "Epoch 72/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5318 - accuracy: 0.7507\n", "Epoch 73/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5251 - accuracy: 0.7507\n", "Epoch 74/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5186 - accuracy: 0.7507\n", "Epoch 75/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5122 - accuracy: 0.7507\n", "Epoch 76/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.5056 - accuracy: 0.7507\n", "Epoch 77/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4986 - accuracy: 0.7507\n", "Epoch 78/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4920 - accuracy: 0.7507\n", "Epoch 79/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4851 - accuracy: 0.7507\n", "Epoch 80/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4783 - accuracy: 0.8360\n", "Epoch 81/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4716 - accuracy: 0.7800\n", "Epoch 82/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4643 - accuracy: 0.9013\n", "Epoch 83/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4576 - accuracy: 1.0000\n", "Epoch 84/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4505 - accuracy: 1.0000\n", "Epoch 85/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4434 - accuracy: 1.0000\n", "Epoch 86/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4366 - accuracy: 1.0000\n", "Epoch 87/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4294 - accuracy: 1.0000\n", "Epoch 88/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4223 - accuracy: 1.0000\n", "Epoch 89/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4155 - accuracy: 1.0000\n", "Epoch 90/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4082 - accuracy: 1.0000\n", "Epoch 91/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.4012 - accuracy: 1.0000\n", "Epoch 92/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.3941 - accuracy: 1.0000\n", "Epoch 93/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3872 - accuracy: 1.0000\n", "Epoch 94/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3802 - accuracy: 1.0000\n", "Epoch 95/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3732 - accuracy: 1.0000\n", "Epoch 96/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3663 - accuracy: 1.0000\n", "Epoch 97/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3595 - accuracy: 1.0000\n", "Epoch 98/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3526 - accuracy: 1.0000\n", "Epoch 99/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3457 - accuracy: 1.0000\n", "Epoch 100/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3389 - accuracy: 1.0000\n", "Epoch 101/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3322 - accuracy: 1.0000\n", "Epoch 102/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3256 - accuracy: 1.0000\n", "Epoch 103/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3188 - accuracy: 1.0000\n", "Epoch 104/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3122 - accuracy: 1.0000\n", "Epoch 105/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.3057 - accuracy: 1.0000\n", "Epoch 106/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2993 - accuracy: 1.0000\n", "Epoch 107/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2926 - accuracy: 1.0000\n", "Epoch 108/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2861 - accuracy: 1.0000\n", "Epoch 109/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2798 - accuracy: 1.0000\n", "Epoch 110/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2734 - accuracy: 1.0000\n", "Epoch 111/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2670 - accuracy: 1.0000\n", "Epoch 112/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2608 - accuracy: 1.0000\n", "Epoch 113/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2545 - accuracy: 1.0000\n", "Epoch 114/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2485 - accuracy: 1.0000\n", "Epoch 115/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2424 - accuracy: 1.0000\n", "Epoch 116/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2364 - accuracy: 1.0000\n", "Epoch 117/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2304 - accuracy: 1.0000\n", "Epoch 118/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2246 - accuracy: 1.0000\n", "Epoch 119/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2187 - accuracy: 1.0000\n", "Epoch 120/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2131 - accuracy: 1.0000\n", "Epoch 121/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.2075 - accuracy: 1.0000\n", "Epoch 122/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.2021 - accuracy: 1.0000\n", "Epoch 123/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1966 - accuracy: 1.0000\n", "Epoch 124/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1912 - accuracy: 1.0000\n", "Epoch 125/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1860 - accuracy: 1.0000\n", "Epoch 126/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1808 - accuracy: 1.0000\n", "Epoch 127/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1758 - accuracy: 1.0000\n", "Epoch 128/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1709 - accuracy: 1.0000\n", "Epoch 129/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1660 - accuracy: 1.0000\n", "Epoch 130/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1613 - accuracy: 1.0000\n", "Epoch 131/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1566 - accuracy: 1.0000\n", "Epoch 132/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1520 - accuracy: 1.0000\n", "Epoch 133/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1477 - accuracy: 1.0000\n", "Epoch 134/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1432 - accuracy: 1.0000\n", "Epoch 135/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1390 - accuracy: 1.0000\n", "Epoch 136/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.1349 - accuracy: 1.0000\n", "Epoch 137/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1309 - accuracy: 1.0000\n", "Epoch 138/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1269 - accuracy: 1.0000\n", "Epoch 139/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1230 - accuracy: 1.0000\n", "Epoch 140/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1193 - accuracy: 1.0000\n", "Epoch 141/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1157 - accuracy: 1.0000\n", "Epoch 142/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1121 - accuracy: 1.0000\n", "Epoch 143/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1086 - accuracy: 1.0000\n", "Epoch 144/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1053 - accuracy: 1.0000\n", "Epoch 145/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.1020 - accuracy: 1.0000\n", "Epoch 146/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0989 - accuracy: 1.0000\n", "Epoch 147/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0958 - accuracy: 1.0000\n", "Epoch 148/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0928 - accuracy: 1.0000\n", "Epoch 149/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0899 - accuracy: 1.0000\n", "Epoch 150/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0871 - accuracy: 1.0000\n", "Epoch 151/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0844 - accuracy: 1.0000\n", "Epoch 152/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0817 - accuracy: 1.0000\n", "Epoch 153/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0791 - accuracy: 1.0000\n", "Epoch 154/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0766 - accuracy: 1.0000\n", "Epoch 155/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0742 - accuracy: 1.0000\n", "Epoch 156/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0719 - accuracy: 1.0000\n", "Epoch 157/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0696 - accuracy: 1.0000\n", "Epoch 158/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0674 - accuracy: 1.0000\n", "Epoch 159/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0652 - accuracy: 1.0000\n", "Epoch 160/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0632 - accuracy: 1.0000\n", "Epoch 161/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0612 - accuracy: 1.0000\n", "Epoch 162/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0592 - accuracy: 1.0000\n", "Epoch 163/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0574 - accuracy: 1.0000\n", "Epoch 164/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0556 - accuracy: 1.0000\n", "Epoch 165/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0538 - accuracy: 1.0000\n", "Epoch 166/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0521 - accuracy: 1.0000\n", "Epoch 167/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0504 - accuracy: 1.0000\n", "Epoch 168/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0488 - accuracy: 1.0000\n", "Epoch 169/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0473 - accuracy: 1.0000\n", "Epoch 170/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0458 - accuracy: 1.0000\n", "Epoch 171/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0444 - accuracy: 1.0000\n", "Epoch 172/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0430 - accuracy: 1.0000\n", "Epoch 173/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0416 - accuracy: 1.0000\n", "Epoch 174/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0403 - accuracy: 1.0000\n", "Epoch 175/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0390 - accuracy: 1.0000\n", "Epoch 176/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0378 - accuracy: 1.0000\n", "Epoch 177/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0366 - accuracy: 1.0000\n", "Epoch 178/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0355 - accuracy: 1.0000\n", "Epoch 179/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0344 - accuracy: 1.0000\n", "Epoch 180/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0333 - accuracy: 1.0000\n", "Epoch 181/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0323 - accuracy: 1.0000\n", "Epoch 182/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0313 - accuracy: 1.0000\n", "Epoch 183/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0303 - accuracy: 1.0000\n", "Epoch 184/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0294 - accuracy: 1.0000\n", "Epoch 185/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0284 - accuracy: 1.0000\n", "Epoch 186/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0276 - accuracy: 1.0000\n", "Epoch 187/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0267 - accuracy: 1.0000\n", "Epoch 188/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0259 - accuracy: 1.0000\n", "Epoch 189/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0251 - accuracy: 1.0000\n", "Epoch 190/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0243 - accuracy: 1.0000\n", "Epoch 191/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0236 - accuracy: 1.0000\n", "Epoch 192/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0229 - accuracy: 1.0000\n", "Epoch 193/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0222 - accuracy: 1.0000\n", "Epoch 194/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0215 - accuracy: 1.0000\n", "Epoch 195/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0208 - accuracy: 1.0000\n", "Epoch 196/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0202 - accuracy: 1.0000\n", "Epoch 197/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0196 - accuracy: 1.0000\n", "Epoch 198/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0190 - accuracy: 1.0000\n", "Epoch 199/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0184 - accuracy: 1.0000\n", "Epoch 200/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0178 - accuracy: 1.0000\n", "Epoch 201/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0173 - accuracy: 1.0000\n", "Epoch 202/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0168 - accuracy: 1.0000\n", "Epoch 203/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0163 - accuracy: 1.0000\n", "Epoch 204/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0158 - accuracy: 1.0000\n", "Epoch 205/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0153 - accuracy: 1.0000\n", "Epoch 206/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0149 - accuracy: 1.0000\n", "Epoch 207/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0144 - accuracy: 1.0000\n", "Epoch 208/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0140 - accuracy: 1.0000\n", "Epoch 209/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0136 - accuracy: 1.0000\n", "Epoch 210/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0132 - accuracy: 1.0000\n", "Epoch 211/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0128 - accuracy: 1.0000\n", "Epoch 212/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0124 - accuracy: 1.0000\n", "Epoch 213/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0120 - accuracy: 1.0000\n", "Epoch 214/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0117 - accuracy: 1.0000\n", "Epoch 215/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0113 - accuracy: 1.0000\n", "Epoch 216/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0110 - accuracy: 1.0000\n", "Epoch 217/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0107 - accuracy: 1.0000\n", "Epoch 218/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0104 - accuracy: 1.0000\n", "Epoch 219/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0101 - accuracy: 1.0000\n", "Epoch 220/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0098 - accuracy: 1.0000\n", "Epoch 221/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0095 - accuracy: 1.0000\n", "Epoch 222/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0092 - accuracy: 1.0000\n", "Epoch 223/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0089 - accuracy: 1.0000\n", "Epoch 224/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0087 - accuracy: 1.0000\n", "Epoch 225/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0084 - accuracy: 1.0000\n", "Epoch 226/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0082 - accuracy: 1.0000\n", "Epoch 227/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0079 - accuracy: 1.0000\n", "Epoch 228/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0077 - accuracy: 1.0000\n", "Epoch 229/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0075 - accuracy: 1.0000\n", "Epoch 230/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0073 - accuracy: 1.0000\n", "Epoch 231/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0071 - accuracy: 1.0000\n", "Epoch 232/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0069 - accuracy: 1.0000\n", "Epoch 233/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0067 - accuracy: 1.0000\n", "Epoch 234/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0065 - accuracy: 1.0000\n", "Epoch 235/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0063 - accuracy: 1.0000\n", "Epoch 236/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0061 - accuracy: 1.0000\n", "Epoch 237/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0059 - accuracy: 1.0000\n", "Epoch 238/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0058 - accuracy: 1.0000\n", "Epoch 239/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0056 - accuracy: 1.0000\n", "Epoch 240/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0055 - accuracy: 1.0000\n", "Epoch 241/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0053 - accuracy: 1.0000\n", "Epoch 242/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0051 - accuracy: 1.0000\n", "Epoch 243/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0050 - accuracy: 1.0000\n", "Epoch 244/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0049 - accuracy: 1.0000\n", "Epoch 245/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0047 - accuracy: 1.0000\n", "Epoch 246/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0046 - accuracy: 1.0000\n", "Epoch 247/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0045 - accuracy: 1.0000\n", "Epoch 248/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0043 - accuracy: 1.0000\n", "Epoch 249/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0042 - accuracy: 1.0000\n", "Epoch 250/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0041 - accuracy: 1.0000\n", "Epoch 251/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0040 - accuracy: 1.0000\n", "Epoch 252/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0039 - accuracy: 1.0000\n", "Epoch 253/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0038 - accuracy: 1.0000\n", "Epoch 254/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0037 - accuracy: 1.0000\n", "Epoch 255/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0036 - accuracy: 1.0000\n", "Epoch 256/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0035 - accuracy: 1.0000\n", "Epoch 257/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0034 - accuracy: 1.0000\n", "Epoch 258/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0033 - accuracy: 1.0000\n", "Epoch 259/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0032 - accuracy: 1.0000\n", "Epoch 260/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0031 - accuracy: 1.0000\n", "Epoch 261/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0030 - accuracy: 1.0000\n", "Epoch 262/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0029 - accuracy: 1.0000\n", "Epoch 263/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0028 - accuracy: 1.0000\n", "Epoch 264/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0028 - accuracy: 1.0000\n", "Epoch 265/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0027 - accuracy: 1.0000\n", "Epoch 266/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0026 - accuracy: 1.0000\n", "Epoch 267/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0025 - accuracy: 1.0000\n", "Epoch 268/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0025 - accuracy: 1.0000\n", "Epoch 269/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0024 - accuracy: 1.0000\n", "Epoch 270/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0023 - accuracy: 1.0000\n", "Epoch 271/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0023 - accuracy: 1.0000\n", "Epoch 272/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0022 - accuracy: 1.0000\n", "Epoch 273/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0022 - accuracy: 1.0000\n", "Epoch 274/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0021 - accuracy: 1.0000\n", "Epoch 275/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0020 - accuracy: 1.0000\n", "Epoch 276/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0020 - accuracy: 1.0000\n", "Epoch 277/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0019 - accuracy: 1.0000\n", "Epoch 278/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0019 - accuracy: 1.0000\n", "Epoch 279/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0018 - accuracy: 1.0000\n", "Epoch 280/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0018 - accuracy: 1.0000\n", "Epoch 281/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0017 - accuracy: 1.0000\n", "Epoch 282/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0017 - accuracy: 1.0000\n", "Epoch 283/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0016 - accuracy: 1.0000\n", "Epoch 284/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0016 - accuracy: 1.0000\n", "Epoch 285/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 286/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 287/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 288/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0014 - accuracy: 1.0000\n", "Epoch 289/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0014 - accuracy: 1.0000\n", "Epoch 290/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 291/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 292/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 293/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 294/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 295/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 296/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 297/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 298/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 299/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 300/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0010 - accuracy: 1.0000\n", "Epoch 301/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 0.0010 - accuracy: 1.0000\n", "Epoch 302/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 9.7451e-04 - accuracy: 1.0000\n", "Epoch 303/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 9.4899e-04 - accuracy: 1.0000\n", "Epoch 304/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.2388e-04 - accuracy: 1.0000\n", "Epoch 305/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 8.9982e-04 - accuracy: 1.0000\n", "Epoch 306/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 8.7595e-04 - accuracy: 1.0000\n", "Epoch 307/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 8.5303e-04 - accuracy: 1.0000\n", "Epoch 308/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.3085e-04 - accuracy: 1.0000\n", "Epoch 309/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 8.0925e-04 - accuracy: 1.0000\n", "Epoch 310/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.8835e-04 - accuracy: 1.0000\n", "Epoch 311/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.6778e-04 - accuracy: 1.0000\n", "Epoch 312/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.4866e-04 - accuracy: 1.0000\n", "Epoch 313/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 7.2878e-04 - accuracy: 1.0000\n", "Epoch 314/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 7.1012e-04 - accuracy: 1.0000\n", "Epoch 315/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 6.9186e-04 - accuracy: 1.0000\n", "Epoch 316/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 6.7412e-04 - accuracy: 1.0000\n", "Epoch 317/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.5694e-04 - accuracy: 1.0000\n", "Epoch 318/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 6.4035e-04 - accuracy: 1.0000\n", "Epoch 319/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.2377e-04 - accuracy: 1.0000\n", "Epoch 320/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 6.0793e-04 - accuracy: 1.0000\n", "Epoch 321/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 5.9257e-04 - accuracy: 1.0000\n", "Epoch 322/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.7734e-04 - accuracy: 1.0000\n", "Epoch 323/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.6279e-04 - accuracy: 1.0000\n", "Epoch 324/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.4858e-04 - accuracy: 1.0000\n", "Epoch 325/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 5.3491e-04 - accuracy: 1.0000\n", "Epoch 326/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.2128e-04 - accuracy: 1.0000\n", "Epoch 327/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 5.0820e-04 - accuracy: 1.0000\n", "Epoch 328/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.9532e-04 - accuracy: 1.0000\n", "Epoch 329/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.8287e-04 - accuracy: 1.0000\n", "Epoch 330/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 4.7073e-04 - accuracy: 1.0000\n", "Epoch 331/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.5893e-04 - accuracy: 1.0000\n", "Epoch 332/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.4753e-04 - accuracy: 1.0000\n", "Epoch 333/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.3628e-04 - accuracy: 1.0000\n", "Epoch 334/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.2535e-04 - accuracy: 1.0000\n", "Epoch 335/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.1477e-04 - accuracy: 1.0000\n", "Epoch 336/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.0446e-04 - accuracy: 1.0000\n", "Epoch 337/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.9450e-04 - accuracy: 1.0000\n", "Epoch 338/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.8459e-04 - accuracy: 1.0000\n", "Epoch 339/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.7501e-04 - accuracy: 1.0000\n", "Epoch 340/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.6576e-04 - accuracy: 1.0000\n", "Epoch 341/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.5670e-04 - accuracy: 1.0000\n", "Epoch 342/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.4787e-04 - accuracy: 1.0000\n", "Epoch 343/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.3924e-04 - accuracy: 1.0000\n", "Epoch 344/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.3094e-04 - accuracy: 1.0000\n", "Epoch 345/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.2266e-04 - accuracy: 1.0000\n", "Epoch 346/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.1476e-04 - accuracy: 1.0000\n", "Epoch 347/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.0707e-04 - accuracy: 1.0000\n", "Epoch 348/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.9939e-04 - accuracy: 1.0000\n", "Epoch 349/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.9208e-04 - accuracy: 1.0000\n", "Epoch 350/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 2.8490e-04 - accuracy: 1.0000\n", "Epoch 351/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.7788e-04 - accuracy: 1.0000\n", "Epoch 352/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.7110e-04 - accuracy: 1.0000\n", "Epoch 353/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6449e-04 - accuracy: 1.0000\n", "Epoch 354/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.5795e-04 - accuracy: 1.0000\n", "Epoch 355/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.5161e-04 - accuracy: 1.0000\n", "Epoch 356/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4552e-04 - accuracy: 1.0000\n", "Epoch 357/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3951e-04 - accuracy: 1.0000\n", "Epoch 358/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3362e-04 - accuracy: 1.0000\n", "Epoch 359/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 2.2796e-04 - accuracy: 1.0000\n", "Epoch 360/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2233e-04 - accuracy: 1.0000\n", "Epoch 361/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1692e-04 - accuracy: 1.0000\n", "Epoch 362/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1166e-04 - accuracy: 1.0000\n", "Epoch 363/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0647e-04 - accuracy: 1.0000\n", "Epoch 364/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0146e-04 - accuracy: 1.0000\n", "Epoch 365/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.9656e-04 - accuracy: 1.0000\n", "Epoch 366/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 1.9178e-04 - accuracy: 1.0000\n", "Epoch 367/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8712e-04 - accuracy: 1.0000\n", "Epoch 368/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8255e-04 - accuracy: 1.0000\n", "Epoch 369/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7815e-04 - accuracy: 1.0000\n", "Epoch 370/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 1.7379e-04 - accuracy: 1.0000\n", "Epoch 371/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6957e-04 - accuracy: 1.0000\n", "Epoch 372/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6544e-04 - accuracy: 1.0000\n", "Epoch 373/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6146e-04 - accuracy: 1.0000\n", "Epoch 374/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5754e-04 - accuracy: 1.0000\n", "Epoch 375/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5374e-04 - accuracy: 1.0000\n", "Epoch 376/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5005e-04 - accuracy: 1.0000\n", "Epoch 377/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4640e-04 - accuracy: 1.0000\n", "Epoch 378/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 1.4285e-04 - accuracy: 1.0000\n", "Epoch 379/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 1.3939e-04 - accuracy: 1.0000\n", "Epoch 380/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3603e-04 - accuracy: 1.0000\n", "Epoch 381/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3272e-04 - accuracy: 1.0000\n", "Epoch 382/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2952e-04 - accuracy: 1.0000\n", "Epoch 383/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2640e-04 - accuracy: 1.0000\n", "Epoch 384/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2335e-04 - accuracy: 1.0000\n", "Epoch 385/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2037e-04 - accuracy: 1.0000\n", "Epoch 386/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1749e-04 - accuracy: 1.0000\n", "Epoch 387/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 1.1464e-04 - accuracy: 1.0000\n", "Epoch 388/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1186e-04 - accuracy: 1.0000\n", "Epoch 389/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0919e-04 - accuracy: 1.0000\n", "Epoch 390/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0655e-04 - accuracy: 1.0000\n", "Epoch 391/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0399e-04 - accuracy: 1.0000\n", "Epoch 392/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0147e-04 - accuracy: 1.0000\n", "Epoch 393/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.9093e-05 - accuracy: 1.0000\n", "Epoch 394/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.6663e-05 - accuracy: 1.0000\n", "Epoch 395/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 9.4349e-05 - accuracy: 1.0000\n", "Epoch 396/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.2073e-05 - accuracy: 1.0000\n", "Epoch 397/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.9831e-05 - accuracy: 1.0000\n", "Epoch 398/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.7691e-05 - accuracy: 1.0000\n", "Epoch 399/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.5586e-05 - accuracy: 1.0000\n", "Epoch 400/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.3538e-05 - accuracy: 1.0000\n", "Epoch 401/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.1516e-05 - accuracy: 1.0000\n", "Epoch 402/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.9560e-05 - accuracy: 1.0000\n", "Epoch 403/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.7666e-05 - accuracy: 1.0000\n", "Epoch 404/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.5813e-05 - accuracy: 1.0000\n", "Epoch 405/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.3993e-05 - accuracy: 1.0000\n", "Epoch 406/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.2205e-05 - accuracy: 1.0000\n", "Epoch 407/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.0474e-05 - accuracy: 1.0000\n", "Epoch 408/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.8802e-05 - accuracy: 1.0000\n", "Epoch 409/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.7129e-05 - accuracy: 1.0000\n", "Epoch 410/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.5531e-05 - accuracy: 1.0000\n", "Epoch 411/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.3952e-05 - accuracy: 1.0000\n", "Epoch 412/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.2420e-05 - accuracy: 1.0000\n", "Epoch 413/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.0930e-05 - accuracy: 1.0000\n", "Epoch 414/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.9486e-05 - accuracy: 1.0000\n", "Epoch 415/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.8042e-05 - accuracy: 1.0000\n", "Epoch 416/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.6670e-05 - accuracy: 1.0000\n", "Epoch 417/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.5319e-05 - accuracy: 1.0000\n", "Epoch 418/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.3988e-05 - accuracy: 1.0000\n", "Epoch 419/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.2688e-05 - accuracy: 1.0000\n", "Epoch 420/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.1434e-05 - accuracy: 1.0000\n", "Epoch 421/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.0196e-05 - accuracy: 1.0000\n", "Epoch 422/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.9015e-05 - accuracy: 1.0000\n", "Epoch 423/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 4.7830e-05 - accuracy: 1.0000\n", "Epoch 424/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.6688e-05 - accuracy: 1.0000\n", "Epoch 425/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.5578e-05 - accuracy: 1.0000\n", "Epoch 426/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.4482e-05 - accuracy: 1.0000\n", "Epoch 427/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.3424e-05 - accuracy: 1.0000\n", "Epoch 428/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.2395e-05 - accuracy: 1.0000\n", "Epoch 429/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 4.1378e-05 - accuracy: 1.0000\n", "Epoch 430/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.0391e-05 - accuracy: 1.0000\n", "Epoch 431/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.9436e-05 - accuracy: 1.0000\n", "Epoch 432/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.8486e-05 - accuracy: 1.0000\n", "Epoch 433/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.7566e-05 - accuracy: 1.0000\n", "Epoch 434/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.6669e-05 - accuracy: 1.0000\n", "Epoch 435/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.5789e-05 - accuracy: 1.0000\n", "Epoch 436/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.4939e-05 - accuracy: 1.0000\n", "Epoch 437/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.4108e-05 - accuracy: 1.0000\n", "Epoch 438/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.3292e-05 - accuracy: 1.0000\n", "Epoch 439/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.2503e-05 - accuracy: 1.0000\n", "Epoch 440/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.1730e-05 - accuracy: 1.0000\n", "Epoch 441/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.0984e-05 - accuracy: 1.0000\n", "Epoch 442/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.0255e-05 - accuracy: 1.0000\n", "Epoch 443/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.9511e-05 - accuracy: 1.0000\n", "Epoch 444/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.8808e-05 - accuracy: 1.0000\n", "Epoch 445/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.8130e-05 - accuracy: 1.0000\n", "Epoch 446/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.7451e-05 - accuracy: 1.0000\n", "Epoch 447/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6798e-05 - accuracy: 1.0000\n", "Epoch 448/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6161e-05 - accuracy: 1.0000\n", "Epoch 449/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.5537e-05 - accuracy: 1.0000\n", "Epoch 450/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4933e-05 - accuracy: 1.0000\n", "Epoch 451/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4343e-05 - accuracy: 1.0000\n", "Epoch 452/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3760e-05 - accuracy: 1.0000\n", "Epoch 453/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3195e-05 - accuracy: 1.0000\n", "Epoch 454/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2644e-05 - accuracy: 1.0000\n", "Epoch 455/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2101e-05 - accuracy: 1.0000\n", "Epoch 456/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1579e-05 - accuracy: 1.0000\n", "Epoch 457/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1064e-05 - accuracy: 1.0000\n", "Epoch 458/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0569e-05 - accuracy: 1.0000\n", "Epoch 459/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0084e-05 - accuracy: 1.0000\n", "Epoch 460/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.9598e-05 - accuracy: 1.0000\n", "Epoch 461/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.9131e-05 - accuracy: 1.0000\n", "Epoch 462/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8680e-05 - accuracy: 1.0000\n", "Epoch 463/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8234e-05 - accuracy: 1.0000\n", "Epoch 464/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7801e-05 - accuracy: 1.0000\n", "Epoch 465/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7378e-05 - accuracy: 1.0000\n", "Epoch 466/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6967e-05 - accuracy: 1.0000\n", "Epoch 467/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6572e-05 - accuracy: 1.0000\n", "Epoch 468/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6168e-05 - accuracy: 1.0000\n", "Epoch 469/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5789e-05 - accuracy: 1.0000\n", "Epoch 470/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5413e-05 - accuracy: 1.0000\n", "Epoch 471/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5046e-05 - accuracy: 1.0000\n", "Epoch 472/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4687e-05 - accuracy: 1.0000\n", "Epoch 473/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4342e-05 - accuracy: 1.0000\n", "Epoch 474/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4000e-05 - accuracy: 1.0000\n", "Epoch 475/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3669e-05 - accuracy: 1.0000\n", "Epoch 476/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3350e-05 - accuracy: 1.0000\n", "Epoch 477/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3031e-05 - accuracy: 1.0000\n", "Epoch 478/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2719e-05 - accuracy: 1.0000\n", "Epoch 479/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2419e-05 - accuracy: 1.0000\n", "Epoch 480/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2122e-05 - accuracy: 1.0000\n", "Epoch 481/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1836e-05 - accuracy: 1.0000\n", "Epoch 482/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1555e-05 - accuracy: 1.0000\n", "Epoch 483/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1281e-05 - accuracy: 1.0000\n", "Epoch 484/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1015e-05 - accuracy: 1.0000\n", "Epoch 485/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0754e-05 - accuracy: 1.0000\n", "Epoch 486/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0500e-05 - accuracy: 1.0000\n", "Epoch 487/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0250e-05 - accuracy: 1.0000\n", "Epoch 488/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0010e-05 - accuracy: 1.0000\n", "Epoch 489/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.7697e-06 - accuracy: 1.0000\n", "Epoch 490/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.5404e-06 - accuracy: 1.0000\n", "Epoch 491/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.3130e-06 - accuracy: 1.0000\n", "Epoch 492/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.0941e-06 - accuracy: 1.0000\n", "Epoch 493/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.8781e-06 - accuracy: 1.0000\n", "Epoch 494/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.6682e-06 - accuracy: 1.0000\n", "Epoch 495/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.4668e-06 - accuracy: 1.0000\n", "Epoch 496/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.2635e-06 - accuracy: 1.0000\n", "Epoch 497/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.0672e-06 - accuracy: 1.0000\n", "Epoch 498/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.8772e-06 - accuracy: 1.0000\n", "Epoch 499/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.6919e-06 - accuracy: 1.0000\n", "Epoch 500/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.5115e-06 - accuracy: 1.0000\n", "Epoch 501/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.3323e-06 - accuracy: 1.0000\n", "Epoch 502/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 7.1581e-06 - accuracy: 1.0000\n", "Epoch 503/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.9895e-06 - accuracy: 1.0000\n", "Epoch 504/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.8234e-06 - accuracy: 1.0000\n", "Epoch 505/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.6634e-06 - accuracy: 1.0000\n", "Epoch 506/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.5060e-06 - accuracy: 1.0000\n", "Epoch 507/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.3534e-06 - accuracy: 1.0000\n", "Epoch 508/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.2020e-06 - accuracy: 1.0000\n", "Epoch 509/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.0562e-06 - accuracy: 1.0000\n", "Epoch 510/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.9133e-06 - accuracy: 1.0000\n", "Epoch 511/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.7738e-06 - accuracy: 1.0000\n", "Epoch 512/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.6365e-06 - accuracy: 1.0000\n", "Epoch 513/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.5039e-06 - accuracy: 1.0000\n", "Epoch 514/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.3738e-06 - accuracy: 1.0000\n", "Epoch 515/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 5.2474e-06 - accuracy: 1.0000\n", "Epoch 516/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.1238e-06 - accuracy: 1.0000\n", "Epoch 517/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.0031e-06 - accuracy: 1.0000\n", "Epoch 518/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.8845e-06 - accuracy: 1.0000\n", "Epoch 519/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.7694e-06 - accuracy: 1.0000\n", "Epoch 520/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.6569e-06 - accuracy: 1.0000\n", "Epoch 521/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.5502e-06 - accuracy: 1.0000\n", "Epoch 522/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.4410e-06 - accuracy: 1.0000\n", "Epoch 523/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.3362e-06 - accuracy: 1.0000\n", "Epoch 524/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.2340e-06 - accuracy: 1.0000\n", "Epoch 525/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.1347e-06 - accuracy: 1.0000\n", "Epoch 526/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.0360e-06 - accuracy: 1.0000\n", "Epoch 527/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.9414e-06 - accuracy: 1.0000\n", "Epoch 528/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.8489e-06 - accuracy: 1.0000\n", "Epoch 529/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.7582e-06 - accuracy: 1.0000\n", "Epoch 530/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.6696e-06 - accuracy: 1.0000\n", "Epoch 531/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.5834e-06 - accuracy: 1.0000\n", "Epoch 532/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.4992e-06 - accuracy: 1.0000\n", "Epoch 533/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.4169e-06 - accuracy: 1.0000\n", "Epoch 534/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.3365e-06 - accuracy: 1.0000\n", "Epoch 535/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.2582e-06 - accuracy: 1.0000\n", "Epoch 536/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.1813e-06 - accuracy: 1.0000\n", "Epoch 537/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.1065e-06 - accuracy: 1.0000\n", "Epoch 538/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.0344e-06 - accuracy: 1.0000\n", "Epoch 539/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.9623e-06 - accuracy: 1.0000\n", "Epoch 540/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.8927e-06 - accuracy: 1.0000\n", "Epoch 541/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.8249e-06 - accuracy: 1.0000\n", "Epoch 542/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.7587e-06 - accuracy: 1.0000\n", "Epoch 543/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6939e-06 - accuracy: 1.0000\n", "Epoch 544/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6307e-06 - accuracy: 1.0000\n", "Epoch 545/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.5687e-06 - accuracy: 1.0000\n", "Epoch 546/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.5085e-06 - accuracy: 1.0000\n", "Epoch 547/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4499e-06 - accuracy: 1.0000\n", "Epoch 548/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3920e-06 - accuracy: 1.0000\n", "Epoch 549/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3359e-06 - accuracy: 1.0000\n", "Epoch 550/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2813e-06 - accuracy: 1.0000\n", "Epoch 551/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2275e-06 - accuracy: 1.0000\n", "Epoch 552/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1757e-06 - accuracy: 1.0000\n", "Epoch 553/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1248e-06 - accuracy: 1.0000\n", "Epoch 554/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0750e-06 - accuracy: 1.0000\n", "Epoch 555/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0263e-06 - accuracy: 1.0000\n", "Epoch 556/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.9790e-06 - accuracy: 1.0000\n", "Epoch 557/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.9328e-06 - accuracy: 1.0000\n", "Epoch 558/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8870e-06 - accuracy: 1.0000\n", "Epoch 559/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8430e-06 - accuracy: 1.0000\n", "Epoch 560/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7999e-06 - accuracy: 1.0000\n", "Epoch 561/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7577e-06 - accuracy: 1.0000\n", "Epoch 562/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7167e-06 - accuracy: 1.0000\n", "Epoch 563/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6764e-06 - accuracy: 1.0000\n", "Epoch 564/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6373e-06 - accuracy: 1.0000\n", "Epoch 565/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5996e-06 - accuracy: 1.0000\n", "Epoch 566/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5617e-06 - accuracy: 1.0000\n", "Epoch 567/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5252e-06 - accuracy: 1.0000\n", "Epoch 568/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4897e-06 - accuracy: 1.0000\n", "Epoch 569/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4549e-06 - accuracy: 1.0000\n", "Epoch 570/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4207e-06 - accuracy: 1.0000\n", "Epoch 571/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3879e-06 - accuracy: 1.0000\n", "Epoch 572/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.3555e-06 - accuracy: 1.0000\n", "Epoch 573/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3239e-06 - accuracy: 1.0000\n", "Epoch 574/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2929e-06 - accuracy: 1.0000\n", "Epoch 575/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2627e-06 - accuracy: 1.0000\n", "Epoch 576/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2333e-06 - accuracy: 1.0000\n", "Epoch 577/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2046e-06 - accuracy: 1.0000\n", "Epoch 578/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1763e-06 - accuracy: 1.0000\n", "Epoch 579/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1490e-06 - accuracy: 1.0000\n", "Epoch 580/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1223e-06 - accuracy: 1.0000\n", "Epoch 581/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0961e-06 - accuracy: 1.0000\n", "Epoch 582/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0706e-06 - accuracy: 1.0000\n", "Epoch 583/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0457e-06 - accuracy: 1.0000\n", "Epoch 584/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0213e-06 - accuracy: 1.0000\n", "Epoch 585/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.9761e-07 - accuracy: 1.0000\n", "Epoch 586/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.7450e-07 - accuracy: 1.0000\n", "Epoch 587/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.5201e-07 - accuracy: 1.0000\n", "Epoch 588/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 9.2977e-07 - accuracy: 1.0000\n", "Epoch 589/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.0821e-07 - accuracy: 1.0000\n", "Epoch 590/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.8717e-07 - accuracy: 1.0000\n", "Epoch 591/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.6668e-07 - accuracy: 1.0000\n", "Epoch 592/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.4663e-07 - accuracy: 1.0000\n", "Epoch 593/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.2701e-07 - accuracy: 1.0000\n", "Epoch 594/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.0776e-07 - accuracy: 1.0000\n", "Epoch 595/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.8886e-07 - accuracy: 1.0000\n", "Epoch 596/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.7086e-07 - accuracy: 1.0000\n", "Epoch 597/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.5287e-07 - accuracy: 1.0000\n", "Epoch 598/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.3548e-07 - accuracy: 1.0000\n", "Epoch 599/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.1853e-07 - accuracy: 1.0000\n", "Epoch 600/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.0173e-07 - accuracy: 1.0000\n", "Epoch 601/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.8561e-07 - accuracy: 1.0000\n", "Epoch 602/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.6966e-07 - accuracy: 1.0000\n", "Epoch 603/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.5419e-07 - accuracy: 1.0000\n", "Epoch 604/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.3920e-07 - accuracy: 1.0000\n", "Epoch 605/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 6.2445e-07 - accuracy: 1.0000\n", "Epoch 606/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.1009e-07 - accuracy: 1.0000\n", "Epoch 607/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.9585e-07 - accuracy: 1.0000\n", "Epoch 608/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.8204e-07 - accuracy: 1.0000\n", "Epoch 609/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.6888e-07 - accuracy: 1.0000\n", "Epoch 610/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.5566e-07 - accuracy: 1.0000\n", "Epoch 611/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.4293e-07 - accuracy: 1.0000\n", "Epoch 612/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.3047e-07 - accuracy: 1.0000\n", "Epoch 613/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 5.1826e-07 - accuracy: 1.0000\n", "Epoch 614/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.0618e-07 - accuracy: 1.0000\n", "Epoch 615/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.9472e-07 - accuracy: 1.0000\n", "Epoch 616/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.8332e-07 - accuracy: 1.0000\n", "Epoch 617/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.7217e-07 - accuracy: 1.0000\n", "Epoch 618/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.6135e-07 - accuracy: 1.0000\n", "Epoch 619/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.5088e-07 - accuracy: 1.0000\n", "Epoch 620/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.4045e-07 - accuracy: 1.0000\n", "Epoch 621/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.3052e-07 - accuracy: 1.0000\n", "Epoch 622/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.2059e-07 - accuracy: 1.0000\n", "Epoch 623/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.1108e-07 - accuracy: 1.0000\n", "Epoch 624/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.0164e-07 - accuracy: 1.0000\n", "Epoch 625/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.9242e-07 - accuracy: 1.0000\n", "Epoch 626/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.8350e-07 - accuracy: 1.0000\n", "Epoch 627/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.7470e-07 - accuracy: 1.0000\n", "Epoch 628/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.6619e-07 - accuracy: 1.0000\n", "Epoch 629/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.5785e-07 - accuracy: 1.0000\n", "Epoch 630/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.4982e-07 - accuracy: 1.0000\n", "Epoch 631/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 3.4197e-07 - accuracy: 1.0000\n", "Epoch 632/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.3393e-07 - accuracy: 1.0000\n", "Epoch 633/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 3.2641e-07 - accuracy: 1.0000\n", "Epoch 634/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.1896e-07 - accuracy: 1.0000\n", "Epoch 635/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.1190e-07 - accuracy: 1.0000\n", "Epoch 636/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.0491e-07 - accuracy: 1.0000\n", "Epoch 637/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.9789e-07 - accuracy: 1.0000\n", "Epoch 638/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.9104e-07 - accuracy: 1.0000\n", "Epoch 639/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.8440e-07 - accuracy: 1.0000\n", "Epoch 640/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.7818e-07 - accuracy: 1.0000\n", "Epoch 641/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.7212e-07 - accuracy: 1.0000\n", "Epoch 642/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6616e-07 - accuracy: 1.0000\n", "Epoch 643/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6005e-07 - accuracy: 1.0000\n", "Epoch 644/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.5411e-07 - accuracy: 1.0000\n", "Epoch 645/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4825e-07 - accuracy: 1.0000\n", "Epoch 646/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4251e-07 - accuracy: 1.0000\n", "Epoch 647/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3731e-07 - accuracy: 1.0000\n", "Epoch 648/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3219e-07 - accuracy: 1.0000\n", "Epoch 649/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2711e-07 - accuracy: 1.0000\n", "Epoch 650/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2207e-07 - accuracy: 1.0000\n", "Epoch 651/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1706e-07 - accuracy: 1.0000\n", "Epoch 652/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1214e-07 - accuracy: 1.0000\n", "Epoch 653/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0714e-07 - accuracy: 1.0000\n", "Epoch 654/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 2.0248e-07 - accuracy: 1.0000\n", "Epoch 655/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.9797e-07 - accuracy: 1.0000\n", "Epoch 656/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.9373e-07 - accuracy: 1.0000\n", "Epoch 657/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8962e-07 - accuracy: 1.0000\n", "Epoch 658/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8553e-07 - accuracy: 1.0000\n", "Epoch 659/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8151e-07 - accuracy: 1.0000\n", "Epoch 660/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7747e-07 - accuracy: 1.0000\n", "Epoch 661/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7342e-07 - accuracy: 1.0000\n", "Epoch 662/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6947e-07 - accuracy: 1.0000\n", "Epoch 663/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6571e-07 - accuracy: 1.0000\n", "Epoch 664/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6208e-07 - accuracy: 1.0000\n", "Epoch 665/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5848e-07 - accuracy: 1.0000\n", "Epoch 666/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5492e-07 - accuracy: 1.0000\n", "Epoch 667/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5159e-07 - accuracy: 1.0000\n", "Epoch 668/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4843e-07 - accuracy: 1.0000\n", "Epoch 669/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4531e-07 - accuracy: 1.0000\n", "Epoch 670/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.4220e-07 - accuracy: 1.0000\n", "Epoch 671/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.3913e-07 - accuracy: 1.0000\n", "Epoch 672/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3605e-07 - accuracy: 1.0000\n", "Epoch 673/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3300e-07 - accuracy: 1.0000\n", "Epoch 674/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.3007e-07 - accuracy: 1.0000\n", "Epoch 675/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2737e-07 - accuracy: 1.0000\n", "Epoch 676/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2466e-07 - accuracy: 1.0000\n", "Epoch 677/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.2199e-07 - accuracy: 1.0000\n", "Epoch 678/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1933e-07 - accuracy: 1.0000\n", "Epoch 679/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1669e-07 - accuracy: 1.0000\n", "Epoch 680/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.1407e-07 - accuracy: 1.0000\n", "Epoch 681/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.1153e-07 - accuracy: 1.0000\n", "Epoch 682/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0917e-07 - accuracy: 1.0000\n", "Epoch 683/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0698e-07 - accuracy: 1.0000\n", "Epoch 684/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0485e-07 - accuracy: 1.0000\n", "Epoch 685/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0268e-07 - accuracy: 1.0000\n", "Epoch 686/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.0052e-07 - accuracy: 1.0000\n", "Epoch 687/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.8383e-08 - accuracy: 1.0000\n", "Epoch 688/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.6280e-08 - accuracy: 1.0000\n", "Epoch 689/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.4238e-08 - accuracy: 1.0000\n", "Epoch 690/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 9.2419e-08 - accuracy: 1.0000\n", "Epoch 691/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 9.0649e-08 - accuracy: 1.0000\n", "Epoch 692/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.8895e-08 - accuracy: 1.0000\n", "Epoch 693/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.7250e-08 - accuracy: 1.0000\n", "Epoch 694/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.5452e-08 - accuracy: 1.0000\n", "Epoch 695/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.3718e-08 - accuracy: 1.0000\n", "Epoch 696/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.2012e-08 - accuracy: 1.0000\n", "Epoch 697/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 8.0289e-08 - accuracy: 1.0000\n", "Epoch 698/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.8592e-08 - accuracy: 1.0000\n", "Epoch 699/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.6918e-08 - accuracy: 1.0000\n", "Epoch 700/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.5234e-08 - accuracy: 1.0000\n", "Epoch 701/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.3578e-08 - accuracy: 1.0000\n", "Epoch 702/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 7.1978e-08 - accuracy: 1.0000\n", "Epoch 703/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 7.0348e-08 - accuracy: 1.0000\n", "Epoch 704/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.8742e-08 - accuracy: 1.0000\n", "Epoch 705/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 6.7208e-08 - accuracy: 1.0000\n", "Epoch 706/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.5890e-08 - accuracy: 1.0000\n", "Epoch 707/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.4615e-08 - accuracy: 1.0000\n", "Epoch 708/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.3382e-08 - accuracy: 1.0000\n", "Epoch 709/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.2191e-08 - accuracy: 1.0000\n", "Epoch 710/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 6.0980e-08 - accuracy: 1.0000\n", "Epoch 711/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.9797e-08 - accuracy: 1.0000\n", "Epoch 712/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.8617e-08 - accuracy: 1.0000\n", "Epoch 713/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 5.7447e-08 - accuracy: 1.0000\n", "Epoch 714/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.6394e-08 - accuracy: 1.0000\n", "Epoch 715/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.5556e-08 - accuracy: 1.0000\n", "Epoch 716/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.4710e-08 - accuracy: 1.0000\n", "Epoch 717/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.3885e-08 - accuracy: 1.0000\n", "Epoch 718/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.3120e-08 - accuracy: 1.0000\n", "Epoch 719/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 5.2270e-08 - accuracy: 1.0000\n", "Epoch 720/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 5.1429e-08 - accuracy: 1.0000\n", "Epoch 721/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 5.0606e-08 - accuracy: 1.0000\n", "Epoch 722/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.9737e-08 - accuracy: 1.0000\n", "Epoch 723/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.8910e-08 - accuracy: 1.0000\n", "Epoch 724/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.8049e-08 - accuracy: 1.0000\n", "Epoch 725/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.7179e-08 - accuracy: 1.0000\n", "Epoch 726/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.6332e-08 - accuracy: 1.0000\n", "Epoch 727/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 4.5497e-08 - accuracy: 1.0000\n", "Epoch 728/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.4652e-08 - accuracy: 1.0000\n", "Epoch 729/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.3808e-08 - accuracy: 1.0000\n", "Epoch 730/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.2975e-08 - accuracy: 1.0000\n", "Epoch 731/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.2149e-08 - accuracy: 1.0000\n", "Epoch 732/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.1331e-08 - accuracy: 1.0000\n", "Epoch 733/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 4.0522e-08 - accuracy: 1.0000\n", "Epoch 734/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.9727e-08 - accuracy: 1.0000\n", "Epoch 735/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.8933e-08 - accuracy: 1.0000\n", "Epoch 736/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.8151e-08 - accuracy: 1.0000\n", "Epoch 737/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.7386e-08 - accuracy: 1.0000\n", "Epoch 738/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.6621e-08 - accuracy: 1.0000\n", "Epoch 739/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.5870e-08 - accuracy: 1.0000\n", "Epoch 740/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.5134e-08 - accuracy: 1.0000\n", "Epoch 741/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 3.4419e-08 - accuracy: 1.0000\n", "Epoch 742/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.3704e-08 - accuracy: 1.0000\n", "Epoch 743/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.2990e-08 - accuracy: 1.0000\n", "Epoch 744/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.2316e-08 - accuracy: 1.0000\n", "Epoch 745/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.1620e-08 - accuracy: 1.0000\n", "Epoch 746/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.0944e-08 - accuracy: 1.0000\n", "Epoch 747/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 3.0308e-08 - accuracy: 1.0000\n", "Epoch 748/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.9667e-08 - accuracy: 1.0000\n", "Epoch 749/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 2.9021e-08 - accuracy: 1.0000\n", "Epoch 750/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.8387e-08 - accuracy: 1.0000\n", "Epoch 751/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 2.7766e-08 - accuracy: 1.0000\n", "Epoch 752/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.7194e-08 - accuracy: 1.0000\n", "Epoch 753/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6610e-08 - accuracy: 1.0000\n", "Epoch 754/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.6017e-08 - accuracy: 1.0000\n", "Epoch 755/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 2.5450e-08 - accuracy: 1.0000\n", "Epoch 756/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4851e-08 - accuracy: 1.0000\n", "Epoch 757/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.4260e-08 - accuracy: 1.0000\n", "Epoch 758/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.3715e-08 - accuracy: 1.0000\n", "Epoch 759/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 2.3230e-08 - accuracy: 1.0000\n", "Epoch 760/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.2782e-08 - accuracy: 1.0000\n", "Epoch 761/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 2.2354e-08 - accuracy: 1.0000\n", "Epoch 762/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1963e-08 - accuracy: 1.0000\n", "Epoch 763/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1576e-08 - accuracy: 1.0000\n", "Epoch 764/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.1217e-08 - accuracy: 1.0000\n", "Epoch 765/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 2.0862e-08 - accuracy: 1.0000\n", "Epoch 766/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0546e-08 - accuracy: 1.0000\n", "Epoch 767/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 2.0211e-08 - accuracy: 1.0000\n", "Epoch 768/1000\n", "47/47 [==============================] - 0s 4ms/step - loss: 1.9922e-08 - accuracy: 1.0000\n", "Epoch 769/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.9656e-08 - accuracy: 1.0000\n", "Epoch 770/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.9415e-08 - accuracy: 1.0000\n", "Epoch 771/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.9198e-08 - accuracy: 1.0000\n", "Epoch 772/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8998e-08 - accuracy: 1.0000\n", "Epoch 773/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8817e-08 - accuracy: 1.0000\n", "Epoch 774/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8649e-08 - accuracy: 1.0000\n", "Epoch 775/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.8495e-08 - accuracy: 1.0000\n", "Epoch 776/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8350e-08 - accuracy: 1.0000\n", "Epoch 777/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8217e-08 - accuracy: 1.0000\n", "Epoch 778/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.8093e-08 - accuracy: 1.0000\n", "Epoch 779/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7977e-08 - accuracy: 1.0000\n", "Epoch 780/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7872e-08 - accuracy: 1.0000\n", "Epoch 781/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7769e-08 - accuracy: 1.0000\n", "Epoch 782/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7672e-08 - accuracy: 1.0000\n", "Epoch 783/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7582e-08 - accuracy: 1.0000\n", "Epoch 784/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7499e-08 - accuracy: 1.0000\n", "Epoch 785/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7417e-08 - accuracy: 1.0000\n", "Epoch 786/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7342e-08 - accuracy: 1.0000\n", "Epoch 787/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7268e-08 - accuracy: 1.0000\n", "Epoch 788/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7202e-08 - accuracy: 1.0000\n", "Epoch 789/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7135e-08 - accuracy: 1.0000\n", "Epoch 790/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7074e-08 - accuracy: 1.0000\n", "Epoch 791/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.7015e-08 - accuracy: 1.0000\n", "Epoch 792/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6960e-08 - accuracy: 1.0000\n", "Epoch 793/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6904e-08 - accuracy: 1.0000\n", "Epoch 794/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6856e-08 - accuracy: 1.0000\n", "Epoch 795/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6800e-08 - accuracy: 1.0000\n", "Epoch 796/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6753e-08 - accuracy: 1.0000\n", "Epoch 797/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6714e-08 - accuracy: 1.0000\n", "Epoch 798/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6672e-08 - accuracy: 1.0000\n", "Epoch 799/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6623e-08 - accuracy: 1.0000\n", "Epoch 800/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6586e-08 - accuracy: 1.0000\n", "Epoch 801/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6549e-08 - accuracy: 1.0000\n", "Epoch 802/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6510e-08 - accuracy: 1.0000\n", "Epoch 803/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6478e-08 - accuracy: 1.0000\n", "Epoch 804/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6442e-08 - accuracy: 1.0000\n", "Epoch 805/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6404e-08 - accuracy: 1.0000\n", "Epoch 806/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6372e-08 - accuracy: 1.0000\n", "Epoch 807/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6340e-08 - accuracy: 1.0000\n", "Epoch 808/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6310e-08 - accuracy: 1.0000\n", "Epoch 809/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6284e-08 - accuracy: 1.0000\n", "Epoch 810/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6260e-08 - accuracy: 1.0000\n", "Epoch 811/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6230e-08 - accuracy: 1.0000\n", "Epoch 812/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6200e-08 - accuracy: 1.0000\n", "Epoch 813/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6179e-08 - accuracy: 1.0000\n", "Epoch 814/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6153e-08 - accuracy: 1.0000\n", "Epoch 815/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6130e-08 - accuracy: 1.0000\n", "Epoch 816/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6110e-08 - accuracy: 1.0000\n", "Epoch 817/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6081e-08 - accuracy: 1.0000\n", "Epoch 818/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6064e-08 - accuracy: 1.0000\n", "Epoch 819/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6043e-08 - accuracy: 1.0000\n", "Epoch 820/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.6024e-08 - accuracy: 1.0000\n", "Epoch 821/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.6002e-08 - accuracy: 1.0000\n", "Epoch 822/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5978e-08 - accuracy: 1.0000\n", "Epoch 823/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5966e-08 - accuracy: 1.0000\n", "Epoch 824/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5943e-08 - accuracy: 1.0000\n", "Epoch 825/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5925e-08 - accuracy: 1.0000\n", "Epoch 826/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5900e-08 - accuracy: 1.0000\n", "Epoch 827/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5890e-08 - accuracy: 1.0000\n", "Epoch 828/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5873e-08 - accuracy: 1.0000\n", "Epoch 829/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5855e-08 - accuracy: 1.0000\n", "Epoch 830/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5841e-08 - accuracy: 1.0000\n", "Epoch 831/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5821e-08 - accuracy: 1.0000\n", "Epoch 832/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5813e-08 - accuracy: 1.0000\n", "Epoch 833/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5799e-08 - accuracy: 1.0000\n", "Epoch 834/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5789e-08 - accuracy: 1.0000\n", "Epoch 835/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5776e-08 - accuracy: 1.0000\n", "Epoch 836/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5749e-08 - accuracy: 1.0000\n", "Epoch 837/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5745e-08 - accuracy: 1.0000\n", "Epoch 838/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5730e-08 - accuracy: 1.0000\n", "Epoch 839/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5720e-08 - accuracy: 1.0000\n", "Epoch 840/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5690e-08 - accuracy: 1.0000\n", "Epoch 841/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5688e-08 - accuracy: 1.0000\n", "Epoch 842/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5678e-08 - accuracy: 1.0000\n", "Epoch 843/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5677e-08 - accuracy: 1.0000\n", "Epoch 844/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5664e-08 - accuracy: 1.0000\n", "Epoch 845/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5650e-08 - accuracy: 1.0000\n", "Epoch 846/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5639e-08 - accuracy: 1.0000\n", "Epoch 847/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5627e-08 - accuracy: 1.0000\n", "Epoch 848/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5618e-08 - accuracy: 1.0000\n", "Epoch 849/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5609e-08 - accuracy: 1.0000\n", "Epoch 850/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5600e-08 - accuracy: 1.0000\n", "Epoch 851/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5584e-08 - accuracy: 1.0000\n", "Epoch 852/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5570e-08 - accuracy: 1.0000\n", "Epoch 853/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5572e-08 - accuracy: 1.0000\n", "Epoch 854/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5558e-08 - accuracy: 1.0000\n", "Epoch 855/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5548e-08 - accuracy: 1.0000\n", "Epoch 856/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5540e-08 - accuracy: 1.0000\n", "Epoch 857/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5534e-08 - accuracy: 1.0000\n", "Epoch 858/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5514e-08 - accuracy: 1.0000\n", "Epoch 859/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5515e-08 - accuracy: 1.0000\n", "Epoch 860/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5512e-08 - accuracy: 1.0000\n", "Epoch 861/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5496e-08 - accuracy: 1.0000\n", "Epoch 862/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5499e-08 - accuracy: 1.0000\n", "Epoch 863/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5486e-08 - accuracy: 1.0000\n", "Epoch 864/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5487e-08 - accuracy: 1.0000\n", "Epoch 865/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5468e-08 - accuracy: 1.0000\n", "Epoch 866/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5468e-08 - accuracy: 1.0000\n", "Epoch 867/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5452e-08 - accuracy: 1.0000\n", "Epoch 868/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5454e-08 - accuracy: 1.0000\n", "Epoch 869/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5444e-08 - accuracy: 1.0000\n", "Epoch 870/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5430e-08 - accuracy: 1.0000\n", "Epoch 871/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5427e-08 - accuracy: 1.0000\n", "Epoch 872/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5430e-08 - accuracy: 1.0000\n", "Epoch 873/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5422e-08 - accuracy: 1.0000\n", "Epoch 874/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5409e-08 - accuracy: 1.0000\n", "Epoch 875/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5410e-08 - accuracy: 1.0000\n", "Epoch 876/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5393e-08 - accuracy: 1.0000\n", "Epoch 877/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5392e-08 - accuracy: 1.0000\n", "Epoch 878/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5372e-08 - accuracy: 1.0000\n", "Epoch 879/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5379e-08 - accuracy: 1.0000\n", "Epoch 880/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5375e-08 - accuracy: 1.0000\n", "Epoch 881/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5370e-08 - accuracy: 1.0000\n", "Epoch 882/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5361e-08 - accuracy: 1.0000\n", "Epoch 883/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5356e-08 - accuracy: 1.0000\n", "Epoch 884/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5362e-08 - accuracy: 1.0000\n", "Epoch 885/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5350e-08 - accuracy: 1.0000\n", "Epoch 886/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5344e-08 - accuracy: 1.0000\n", "Epoch 887/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5329e-08 - accuracy: 1.0000\n", "Epoch 888/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5324e-08 - accuracy: 1.0000\n", "Epoch 889/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5336e-08 - accuracy: 1.0000\n", "Epoch 890/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5291e-08 - accuracy: 1.0000\n", "Epoch 891/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5309e-08 - accuracy: 1.0000\n", "Epoch 892/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5316e-08 - accuracy: 1.0000\n", "Epoch 893/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5304e-08 - accuracy: 1.0000\n", "Epoch 894/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5291e-08 - accuracy: 1.0000\n", "Epoch 895/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5305e-08 - accuracy: 1.0000\n", "Epoch 896/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5289e-08 - accuracy: 1.0000\n", "Epoch 897/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5302e-08 - accuracy: 1.0000\n", "Epoch 898/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5294e-08 - accuracy: 1.0000\n", "Epoch 899/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5287e-08 - accuracy: 1.0000\n", "Epoch 900/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5278e-08 - accuracy: 1.0000\n", "Epoch 901/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5269e-08 - accuracy: 1.0000\n", "Epoch 902/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5274e-08 - accuracy: 1.0000\n", "Epoch 903/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5272e-08 - accuracy: 1.0000\n", "Epoch 904/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5264e-08 - accuracy: 1.0000\n", "Epoch 905/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5264e-08 - accuracy: 1.0000\n", "Epoch 906/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5258e-08 - accuracy: 1.0000\n", "Epoch 907/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5249e-08 - accuracy: 1.0000\n", "Epoch 908/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5253e-08 - accuracy: 1.0000\n", "Epoch 909/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5255e-08 - accuracy: 1.0000\n", "Epoch 910/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5254e-08 - accuracy: 1.0000\n", "Epoch 911/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5232e-08 - accuracy: 1.0000\n", "Epoch 912/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5243e-08 - accuracy: 1.0000\n", "Epoch 913/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5238e-08 - accuracy: 1.0000\n", "Epoch 914/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5235e-08 - accuracy: 1.0000\n", "Epoch 915/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5221e-08 - accuracy: 1.0000\n", "Epoch 916/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5177e-08 - accuracy: 1.0000\n", "Epoch 917/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5215e-08 - accuracy: 1.0000\n", "Epoch 918/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5221e-08 - accuracy: 1.0000\n", "Epoch 919/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5219e-08 - accuracy: 1.0000\n", "Epoch 920/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5210e-08 - accuracy: 1.0000\n", "Epoch 921/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5205e-08 - accuracy: 1.0000\n", "Epoch 922/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5204e-08 - accuracy: 1.0000\n", "Epoch 923/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5191e-08 - accuracy: 1.0000\n", "Epoch 924/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5195e-08 - accuracy: 1.0000\n", "Epoch 925/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5205e-08 - accuracy: 1.0000\n", "Epoch 926/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5195e-08 - accuracy: 1.0000\n", "Epoch 927/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5189e-08 - accuracy: 1.0000\n", "Epoch 928/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5175e-08 - accuracy: 1.0000\n", "Epoch 929/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5178e-08 - accuracy: 1.0000\n", "Epoch 930/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5180e-08 - accuracy: 1.0000\n", "Epoch 931/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5178e-08 - accuracy: 1.0000\n", "Epoch 932/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5179e-08 - accuracy: 1.0000\n", "Epoch 933/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5178e-08 - accuracy: 1.0000\n", "Epoch 934/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5143e-08 - accuracy: 1.0000\n", "Epoch 935/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5164e-08 - accuracy: 1.0000\n", "Epoch 936/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5167e-08 - accuracy: 1.0000\n", "Epoch 937/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5160e-08 - accuracy: 1.0000\n", "Epoch 938/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5159e-08 - accuracy: 1.0000\n", "Epoch 939/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5145e-08 - accuracy: 1.0000\n", "Epoch 940/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5153e-08 - accuracy: 1.0000\n", "Epoch 941/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5147e-08 - accuracy: 1.0000\n", "Epoch 942/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5157e-08 - accuracy: 1.0000\n", "Epoch 943/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5156e-08 - accuracy: 1.0000\n", "Epoch 944/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5157e-08 - accuracy: 1.0000\n", "Epoch 945/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5149e-08 - accuracy: 1.0000\n", "Epoch 946/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5114e-08 - accuracy: 1.0000\n", "Epoch 947/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5137e-08 - accuracy: 1.0000\n", "Epoch 948/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5125e-08 - accuracy: 1.0000\n", "Epoch 949/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5125e-08 - accuracy: 1.0000\n", "Epoch 950/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5126e-08 - accuracy: 1.0000\n", "Epoch 951/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5128e-08 - accuracy: 1.0000\n", "Epoch 952/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5132e-08 - accuracy: 1.0000\n", "Epoch 953/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5120e-08 - accuracy: 1.0000\n", "Epoch 954/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5127e-08 - accuracy: 1.0000\n", "Epoch 955/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5126e-08 - accuracy: 1.0000\n", "Epoch 956/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5109e-08 - accuracy: 1.0000\n", "Epoch 957/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5112e-08 - accuracy: 1.0000\n", "Epoch 958/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5099e-08 - accuracy: 1.0000\n", "Epoch 959/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5108e-08 - accuracy: 1.0000\n", "Epoch 960/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5129e-08 - accuracy: 1.0000\n", "Epoch 961/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5106e-08 - accuracy: 1.0000\n", "Epoch 962/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5112e-08 - accuracy: 1.0000\n", "Epoch 963/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5111e-08 - accuracy: 1.0000\n", "Epoch 964/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5108e-08 - accuracy: 1.0000\n", "Epoch 965/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5108e-08 - accuracy: 1.0000\n", "Epoch 966/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5097e-08 - accuracy: 1.0000\n", "Epoch 967/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5100e-08 - accuracy: 1.0000\n", "Epoch 968/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5106e-08 - accuracy: 1.0000\n", "Epoch 969/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5094e-08 - accuracy: 1.0000\n", "Epoch 970/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5104e-08 - accuracy: 1.0000\n", "Epoch 971/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5094e-08 - accuracy: 1.0000\n", "Epoch 972/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5086e-08 - accuracy: 1.0000\n", "Epoch 973/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5089e-08 - accuracy: 1.0000\n", "Epoch 974/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5077e-08 - accuracy: 1.0000\n", "Epoch 975/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5073e-08 - accuracy: 1.0000\n", "Epoch 976/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5080e-08 - accuracy: 1.0000\n", "Epoch 977/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5077e-08 - accuracy: 1.0000\n", "Epoch 978/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5076e-08 - accuracy: 1.0000\n", "Epoch 979/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5084e-08 - accuracy: 1.0000\n", "Epoch 980/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5093e-08 - accuracy: 1.0000\n", "Epoch 981/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5079e-08 - accuracy: 1.0000\n", "Epoch 982/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5091e-08 - accuracy: 1.0000\n", "Epoch 983/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5070e-08 - accuracy: 1.0000\n", "Epoch 984/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5085e-08 - accuracy: 1.0000\n", "Epoch 985/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5068e-08 - accuracy: 1.0000\n", "Epoch 986/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5040e-08 - accuracy: 1.0000\n", "Epoch 987/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5060e-08 - accuracy: 1.0000\n", "Epoch 988/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5072e-08 - accuracy: 1.0000\n", "Epoch 989/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5070e-08 - accuracy: 1.0000\n", "Epoch 990/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5064e-08 - accuracy: 1.0000\n", "Epoch 991/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5060e-08 - accuracy: 1.0000\n", "Epoch 992/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5055e-08 - accuracy: 1.0000\n", "Epoch 993/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5046e-08 - accuracy: 1.0000\n", "Epoch 994/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5066e-08 - accuracy: 1.0000\n", "Epoch 995/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5066e-08 - accuracy: 1.0000\n", "Epoch 996/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5056e-08 - accuracy: 1.0000\n", "Epoch 997/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5052e-08 - accuracy: 1.0000\n", "Epoch 998/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5039e-08 - accuracy: 1.0000\n", "Epoch 999/1000\n", "47/47 [==============================] - 0s 6ms/step - loss: 1.5059e-08 - accuracy: 1.0000\n", "Epoch 1000/1000\n", "47/47 [==============================] - 0s 5ms/step - loss: 1.5033e-08 - accuracy: 1.0000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " FutureWarning\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": {} } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ff25KMbQNOjC", "outputId": "7e733b04-5729-4134-ecb9-4f2bdc24869a" }, "source": [ "# Did we learn anything?\n", "predictions = np.round(model.predict(test_data))\n", "score = accuracy_score(predictions, test_target)\n", "print(f\"Neural network performed with the accuracy of {score}\")\n", "\n", "predictions_random = np.random.randint(0,2, len(predictions))\n", "score_random = accuracy_score(predictions_random, test_target)\n", "print(f\"Random baseline performed with the accuracy of {score_random}\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Neural network performed with the accuracy of 1.0\n", "Random baseline performed with the accuracy of 0.484\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "P2iKgcOdOeqy" }, "source": [ "Mini tasks to better understand the learning part:\n", "\n", "1. Try to change the activation and the number of hidden neurons.\n", "\n", "2. Try to omit the activation alltogether, what do you observe? (HINT: Think in terms of linear separability)" ] }, { "cell_type": "markdown", "metadata": { "id": "iR6x0i4bNHJQ" }, "source": [ "\n", "\n", "The next example will consider the well known _iris_ data set. Here, we are doing the _multiclass_ classification!\n", "\n", "![image.png](data:image/png;base64,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)" ] }, { "cell_type": "code", "metadata": { "id": "qyfEe8VNLWTg", "colab": { "base_uri": "https://localhost:8080/", "height": 371 }, "outputId": "f6885884-5ecd-4af2-f9a7-5161e8720f3f" }, "source": [ "\n", "# We will first of all load the Iris data set\n", "data = load_iris()\n", "X = data['data']\n", "Y = data['target']\n", "names = data['feature_names']\n", "tnames = data['target_names']\n", "\n", "# Let's just encode the target via real names for the visualization purposes\n", "target_vec_names = []\n", "for el in Y:\n", " if el == 0:\n", " target_vec_names.append(tnames[0])\n", " elif el == 1:\n", " target_vec_names.append(tnames[1])\n", " else:\n", " target_vec_names.append(tnames[2])\n", "\n", "# Plot a scatterplot (Seaborn library)\n", "sns.scatterplot(X[:,0],X[:,1], hue = target_vec_names, palette = \"Set2\")\n", "plt.xlabel(names[0])\n", "plt.ylabel(names[1])\n", "plt.show()\n", "\n", "# As this is a multiclass problem, let's perform a one-hot encoding (Discussion time: what is this?)\n", "unique_classes = len(np.unique(Y))\n", "Y = Y.reshape(-1,1)\n", "encoder = OneHotEncoder().fit(Y)\n", "Y = encoder.transform(Y).todense() # -> dense matrices are natively supported in Keras.\n", "\n", "# Let's split the data in stratified manner next\n", "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, stratify=Y, random_state=42)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " FutureWarning\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qxwtVmzCLsV4", "outputId": "285dca10-473d-4e6e-cdba-cd635e250a5c" }, "source": [ "\n", "\n", "def construct_simple_network(n_hidden = 1, dropout = 0.5):\n", " \"\"\"\n", " A supporting method useful for exploring various hyperparameter settings. The idea is that it constructs and compiles the model,\n", " which can be subsequently trained in an e.g., loop-like structure.\n", " Args:\n", " n_hidden (int): The number of hidden layers\n", " dropout (float): The dropout level after each ELU activated hidden layer\n", " \"\"\"\n", " model = keras.Sequential()\n", " model.add(keras.Input(shape=(X_train.shape[1],)))\n", " for j in range(n_hidden):\n", " model.add(layers.Dense(8, activation=\"elu\"))\n", " model.add(layers.Dropout(dropout))\n", " model.add(layers.Dense(unique_classes, activation=\"softmax\"))\n", " model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", " return model\n", "\n", "# Construct a network here\n", "model = construct_simple_network()\n", "\n", "# Model summary gives us insight w.r.t. the model properties\n", "model.summary()\n", "\n", "# Model fitting via standard Sklearn-like API call\n", "history = model.fit(X_train, Y_train, epochs=200, batch_size=4, verbose=0)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_2 (Dense) (None, 8) 40 \n", " \n", " dropout (Dropout) (None, 8) 0 \n", " \n", " dense_3 (Dense) (None, 3) 27 \n", " \n", "=================================================================\n", "Total params: 67\n", "Trainable params: 67\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AnIImif1MVOY", "outputId": "c0470fd3-83d7-4b0f-9303-bc3427bcf4eb" }, "source": [ "# Did the model learn anything?\n", "from sklearn.metrics import accuracy_score\n", "\n", "# We collect max column indices (class predictions)\n", "predictions = np.argmax(model.predict(X_test),axis = 1)\n", "\n", "# Decode that to integers (from boolean)\n", "Y_test = encoder.inverse_transform(Y_test)\n", "\n", "# Compute the performance.\n", "report_accuracy = accuracy_score(predictions, Y_test)\n", "print(f\"The performance accuracy on the test set is {report_accuracy}\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The performance accuracy on the test set is 0.9736842105263158\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "G65NL-GG_RdO", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "16b58239-b67b-49ce-fd26-630d801060bd" }, "source": [ "import pandas as pd\n", "import tqdm\n", "\n", "# Parameter space to be explored\n", "num_hidden = [1 ,2 ,3, 4]\n", "n_epochs = [16, 64, 128]\n", "dropout_levels = [0.1, 0.5, 0.9]\n", "dropout_results = []\n", "\n", "for hidden_layers in tqdm.tqdm(num_hidden):\n", " for n_epo in n_epochs:\n", " for dropout_level in dropout_levels:\n", " # Let's split the data in stratified manner next\n", " X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, stratify=Y, random_state=42)\n", "\n", " # A call to the network constructor method\n", " neural_net = construct_simple_network(n_hidden = hidden_layers, dropout = dropout_level)\n", "\n", " # A call to the fit method with specified hyperparameters\n", " neural_net.fit(X_train, Y_train, epochs=n_epo, batch_size=32, verbose=0)\n", "\n", " # Get discrete predictions\n", " predictions = np.argmax(neural_net.predict(X_test),axis = 1)\n", "\n", " # Decode them to a single vector\n", " Y_test = encoder.inverse_transform(Y_test)\n", "\n", " # Compute accuracy\n", " report_accuracy = accuracy_score(predictions, Y_test)\n", "\n", " # Store the results to a table for further analysis\n", " dropout_results.append([hidden_layers, n_epo, dropout_level, report_accuracy])" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\r 0%| | 0/4 [00:00.predict_function at 0x7fbf7dfb1f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "WARNING:tensorflow:6 out of the last 17 calls to .predict_function at 0x7fbf7de40c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", " 25%|██▌ | 1/4 [00:16<00:49, 16.37s/it]/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", " 50%|█████ | 2/4 [00:35<00:35, 17.72s/it]/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", " 75%|███████▌ | 3/4 [00:54<00:18, 18.55s/it]/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:590: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n", " FutureWarning,\n", "100%|██████████| 4/4 [01:15<00:00, 18.86s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "7lUTfbGREnqo", "outputId": "7d6d93d5-bd2e-4114-8773-f87c40a8eb39" }, "source": [ "final_dataframe = pd.DataFrame(dropout_results)\n", "final_dataframe.columns = ['layers', 'epochs', 'dropout','accuracy']\n", "final_dataframe = final_dataframe.sort_values(by = [\"accuracy\"], ascending = False)\n", "\n", "# Can we overfit?\n", "sns.barplot(final_dataframe.layers, final_dataframe.accuracy, capsize = 0.1, palette = \"coolwarm\")\n", "plt.xlabel(\"Num. hidden layers\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.title(\"Impact of the number of hidden layers\")\n", "plt.show()\n", "\n", "# How does dropout impact the performance\n", "sns.barplot(final_dataframe.dropout, final_dataframe.accuracy, capsize = 0.1, palette = \"coolwarm\")\n", "plt.xlabel(\"Dropout\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.title(\"Impact of the number of dropout\")\n", "plt.show()\n", "\n", "# Does training time impact the performance?\n", "sns.barplot(final_dataframe.epochs, final_dataframe.accuracy, capsize = 0.1, palette = \"coolwarm\")\n", "plt.xlabel(\"Num epochs.\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.title(\"Impact of the training length\")\n", "plt.show()\n", "\n", "# What's the best hyperparameter setting? (first row)\n", "print(final_dataframe.head())" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " FutureWarning\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " FutureWarning\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " FutureWarning\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " layers epochs dropout accuracy\n", "15 2 128 0.1 0.973684\n", "24 3 128 0.1 0.973684\n", "8 1 128 0.9 0.947368\n", "33 4 128 0.1 0.947368\n", "30 4 64 0.1 0.921053\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "2sYO-TDWORdh" }, "source": [ "**Problemsets:**\n", "\n", "\n", "1. Try to improve the model's performance by varying the number of hyperparameters\n", "2. What happens if you increase the dropout? Can you interpret these results?\n", "3. Can you force it to overfit? How did you achieve this result? What is overfitting?\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "GCzBHbrmTgIv" }, "source": [ "#Image classification\n", "The next example addresses the image classification problem (MNIST). Examples of this data set can be seen below.\n", "\n", "![image.png](data:image/png;base64,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)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "I4SaV8YNPa7k", "outputId": "169c5f94-ffd7-4ede-b24d-9578dedb6ab7" }, "source": [ "import numpy as np\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "# Adapted from the official Docs: https://keras.io/examples/vision/mnist_convnet/\n", "\n", "# Model / data parameters\n", "num_classes = 10\n", "input_shape = (28, 28, 1)\n", "\n", "# the data, split between train and test sets\n", "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n", "\n", "# Scale images to the [0, 1] range\n", "x_train = x_train.astype(\"float32\") / 255\n", "x_test = x_test.astype(\"float32\") / 255\n", "\n", "# Make sure images have shape (28, 28, 1)\n", "x_train = np.expand_dims(x_train, -1)\n", "x_test = np.expand_dims(x_test, -1)\n", "print(\"x_train shape:\", x_train.shape)\n", "print(x_train.shape[0], \"train samples\")\n", "print(x_test.shape[0], \"test samples\")\n", "\n", "\n", "# convert class vectors to binary class matrices (one hot encoding)\n", "y_train = keras.utils.to_categorical(y_train, num_classes)\n", "y_test = keras.utils.to_categorical(y_test, num_classes)\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", "11493376/11490434 [==============================] - 0s 0us/step\n", "11501568/11490434 [==============================] - 0s 0us/step\n", "x_train shape: (60000, 28, 28, 1)\n", "60000 train samples\n", "10000 test samples\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 268 }, "id": "ZetDPavk55G_", "outputId": "50e5dfee-628c-43fb-de8d-7494093d3c72" }, "source": [ "# Let's plot some instances next.\n", "from matplotlib import pyplot\n", "\n", "for i in range(0, 9):\n", " location = 330 + 1 + i\n", " pyplot.subplot(location)\n", " pyplot.imshow(x_train[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))\n", "\n", "pyplot.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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gAB0dHTz77LP09/fz0UcfMT09zbvvvsv3vve9TanAUiqVGI1GDAYDL774Il/4whdED4LTp0/zv/7X/xJ9WNcSu1j3ueoul4tIJILH4yESieB0OtfyNrfN8iYO0tflclkEf2q1mkiYT6VSaLValErlNbsPmavUU1eFQoHBYBABvq6uLpqbm4VmpVJJBO8ymQyLi4sEg0EikYjQe60pJ1KFmdvtpqOjA7vdXveAx3qzntpqNBpcLhdOp1NU7AGbXuuvUCjQ6XTCeBqNRrRarViDFM/YiN3vmgznww8/zCuvvMILL7zAK6+8wqFDh9Z7XWsin89z8uRJEWF3Op2oVCo6OjoolUoi/SQcDq97XlcjUC9dpaPf3r17Rb7m7t27MZlMwNWI8Jtvvonf72dhYYGlpSWWlpaYnZ0lnU6vS3mfQqHAbDZjt9tFo5BGYj211Wq1OJ1OEbADRFAumUxu2jFdr9dz33330dnZyeDgICaTiUqlwtLSEuVymVwuJ04q681NDecf/MEf8MEHHxCLxThw4ABf+cpXeOGFF/j93/99fvCDH9DS0sK3vvWtdV/YWigUCly4cAGdTsfg4CCDg4N4vV76+/spFou0t7eTz+dJp9N3vOHcKrpK7hapCkXqSrVjxw6Rh5lKpXj//fc5e/Ysfr+fcDgsGlSXSqV18V8pFAr0ev01zUK2IxutrdQ82mazodFoRJ+HxcVF0un0pu069Xo9o6Oj7Nq1i97eXkwmE6lUSmRMSPnaddlxfvOb37zu97/3ve+t+2JuF+noLnUy+uUvf8nOnTvp6OhAp9MxMjKCy+XCbrcTCoXI5/PkcjkymQzz8/N3VKOOraKrSqXCZrOJEkipTZlCoSAWi/HRRx8xPz/PxMQEwWCQRCJBoVAQx3e5j+a11EPbbDbL4uIiqVRq3Q2nSqVCqVRiMplEbwK3243L5WJwcJCOjg4sFgtwNdvi/Pnz2Gw2UqmUaCu43mybyqFbRdqBHD9+nMnJSX7lV36Fffv2YTKZePzxx8nn81y6dIlwOEwkEiEYDDI7O0s8Hr9jkt+3ElqtlubmZpqamujv72d4eFj4zPx+P9///vcJBAKcO3eOpaWl6/rRZH91fanVasRiMWZmZohGo+tuqKTG5FIhhM/n47777sPpdLJnzx5cLpdIQwoGg/z0pz/lscceIxqNbliQquEMJ1wVMpfLkUgkWFhYYHZ2FovFgslkwmw209TUJMZlSM7ty5cvYzAYhM/s400+ZDYGjUYjuh1JR79isUgulyOZTBKNRllYWBBVY+tJo1UMbXWkNDH4t9+9Xq8XDcWXNxWXkuzVarXoMN/a2ipa2JnNZgwGg+jPKulYLBaJx+NUKpUNPUE2pOGEq8ntuVyOkydP8r//9/+mvb2dz372s3i9XjEytlwuUywWmZycRKfTEQ6HOXXqFNFolEwmIze13QQcDgePP/44fX19dHZ2AlePW36/n4sXL3Lx4kXR1Wo9WW40G7HscrORRpncaL4XXG2m0tTUtGLkTW9vLzabDbPZLMbWSCNvRkdHsVqtov2j9Fg+n2dpaUlMsCyXy8JXnkqlmJmZoVQqbWj3soY1nJVKhUqlQjweZ2pqCoBkMilEknaaSqWSfD4v2u1PTU0Jp7JUqiUfBdcfaVeh1+vFjlOKZkud9xOJhLgBbgbyKePW+Li7REoLkjrBWyyWazIdpDxdyXDC1VZwra2tOBwOrFYrJpNJGEC73U5fX59ojgz/ll4k9a2Q3HLL++yWy2XS6fSGa9mwhlMiFotx9uxZAoEAhUIBj8fD/v376enpEU0+PB4Phw8fJh6PY7PZCAQCnD59mkuXLlEsFu+Y+vXNRCqP7e/vp6uri9bWVoxGI7VajYWFBc6ePStGlWwEyy9+Kai4uLgo/N0yN6ZarVIqlURwTqFQsHfvXpxOJ0tLS/j9/uu6VZqamsTgRMlASn1Spa8lF1u1WuX8+fNCl3Q6TTQaJRwOizJph8PBl770JWw2m2jskc1mWVhY2PCpDA1vOLPZLNlsVhT72+12UaGi1WrxeDyYzWZ27txJOp1maWkJu90uOsFL7yGzvhiNRlpaWsQ4FLvdLnxcqVSKubk5FhYWNiQHT+LjxjOTyRCPx8XgNpkbI53opD6obW1teDweMeXyers9t9uNz+db4RaR3kPKt5SaladSKa5cuUIqlWJ2dpZoNEogEBAnwmQyic/n4zd/8zdXlNSWSiVSqZS841wvpEbGhUKB9957D7/fT3d3NwMDAzQ1NTEyMoJGo6Gnpwen00m5XMblcjExMcG7775b7+U3HJK/SpoJtTy9KBwOc/nyZcLh8LrtOCW3TGdnJz6fjx07dohditRV6eTJk5w+fZrJyUnZcH4CS0tLvP3223g8HhQKBe3t7aKvQLFYvOFGQ2pGLk2KlXKqpTE0yWSSXC4nuphJfValSZXxeJxkMonBYKC/v5+2tjbRm6JSqYix3Zuh3R1lOKPRKEqlklgshk6no7e3l6GhIYaHh+nt7cXhcDA0NES1WsXlcjE0NMTRo0f54IMP6r38hmP5xErJcBYKBfL5PH6/n7Nnz5LJZNbNcEpGemhoiH379jEyMiIMp9/vJxKJ8Itf/IK33357RQctmWuJRCK8/vrrWK1WKpUKXV1ddHR04PP5PvF10gTMhYUFTp06RSqVIhgMkk6nmZubE7Xxy3/3y08E0v9ms5mRkRHa29vFSGip03s2m5UN50Yg+WdqtZrwx3g8HnEklHwtZrNZJNk6HA6RLrGRR8c7leVdj6ThalJXm/V4bynlyWKx0NPTQ29vLy6XSyRH+/1+0aNzIzrpNCLSRIZAICB2e7faii8ejzMzM0M2myUajZLP58lkMrf8e9doNKLkU6rykko+E4mEbDg3imKxSLFYZGZmhmAwiFarJZfLUa1WheO6qakJh8OB3+9nx44dIudT9nduHFJqiRRVv91yOYVCgVqtxmKxcP/999PV1cWnP/1p7rvvPjGcr1gscuzYMWZmZpidnaVQKMi7zVskl8vx/vvvi1PDrTYzloyudKOUouG3islkor+/X7gIarUac3NznD59mqmpqU3Z3NwxhlNKf5FGiC4XW8ofW47kE5Nz+zYHKUCwvDHDWo2m5D/VarWicYfP56O1tVVEYNPpNMFgkHK5TCQSYXFxUQ4KrRKp0GSzkdKfpM5ZgAgAb1at/B1jODUaDR6PB6PRSG9vL16vF4fDIVqY2Wy2FXfMRCIhjhSXL1+mUCg0XHPbrUSlUmF6elqMObmdP36r1YrL5aK5uZn77rsPj8fDgw8+iM/nI5lMcurUKU6dOsUrr7zCl7/8ZY4dOyaOizLbD8nd8uGHHzI7O7spO86bzhza7kg7TWliosPhoLOzk/7+fnbt2sXevXvp6+tb0RFH2v1kMhkxKU9Kv5DZGJZ32Fnrzk/yTxsMBtE0ZOfOnezcuZOenh7a2tqo1WqEQiHGx8f55S9/STabJRQKibEqMtsTaeyzvOO8TaRaVo/HQ19fHzabjZ07d2Kz2US9q9FoxGKxYDAYRA2t1E1eyhWTfJ8y68/yfD6VSkVraysajYbz58+v2kWiUqno7+/H6/UyNDTE6OgoTqeTgYEBNBoN8/PzzM7O8rOf/Yxf/vKXzM3NbVhyvUzjc1PDGQqF+MM//EMWFxdRKBT8xm/8Bs8999yWGyX7cQwGA06nk97eXg4ePEhTUxP33HMPdrsdq9WKXq+/7uukpGipYiifzzek36veui43jNKpoLm5GavVitPpXLXhVKvVoo/n/fffz6c//WkxtyiXy/Hee+8RCAR4++23+clPfrLeP86Wod663inc1HCqVCr+6I/+iOHhYdLpNE8//TQPPPAA//RP/8T+/ft54YUXOHLkCEeOHKnLRERABHw0Gg1utxuj0SiOZh0dHezYsQOLxSIM5sejf6VSSRjJubk5UqkUFy9eZGZmhvPnzzdkespW0lW6MUljDzo6OrjnnntIpVJEIpEVNckmk0mM2HC73Wg0GtFcYu/evbS1tdHc3CwauCwtLZFKpRgfHxeTThuZraTrZiCdWqRa+c1qDXlTw+nxeMR0PLPZTE9PD+FweMuMkgVEyonZbObee+/F5/Oxb98+du/ejcViwePxiEjr9brg5PN5AoEAi4uL/PjHP2Z6epoLFy4wOTkpLsBGo966Sjv75bt5g8GATqdjz549/Pqv/zqBQIBf/OIXYs5QuVwWhrGrq4u7774bq9VKW1sbRqMRl8uF2Wwmm82SyWRIJBJMT0+zuLjIO++8w9zc3DVjcxuNeutaDxQKhbjO0+n0NRkyG8GqfJx+v5+xsTH27NmzruNGV8vH24EplUq0Wq1ILdJoNKIMK5lMXhMtlS5YqZ61UqmIruIHDhxg//79FAqFFQazkccD10PX5eN/4/H4ivxYnU7Hpz71KUqlEo888siKWmRpiqWksXRDLBaLzM/Pr2g1Vi6XsVgsGI1G/uN//I+iMcXyE4Ss641Z73He64UUu5B819FolM7OTj7/+c+LoG5XV9eGrvuWDWcmk+HFF1/k61//OmazecVjtztudDUoFApMJhM6nU407Ojr6+M//If/gMvlEm30pcl30oUlUa1WCQQCxONxzp49y4cffkgkEuGjjz4im82STCZF6tFyw9mo44HrpWtHRwd33XUXfX19fPnLX6a9vV08JtUzZ7NZwuGwaGxcqVTweDyiYsRgMIjZQ7Vajfn5eRKJBIFAgOnpaaanp/nxj38s2tMVi8VrDKes641Z73He68WuXbv48pe/TFtbG729vTidTv7v//2/vP7660xMTPD+++/z3e9+d0N1vSXDWSqVePHFF3nyySc5fPgwsLmjZJc3PlWr1VitVgwGAy6XC6/XS2trqyij83q9K6YULq9OkMrplpaWRLeVK1euEIlEmJycvOMSoOupq6SLtDusVCqi4MBgMGAwGETGg1RNVKlUcLlcIqghnRyk90gkEkQiEcLhMPPz8wSDQeGzvpOo9/W6mUjXqzTi+Varl26XmxrOWq3Gn/zJn9DT08Pzzz8vvr9Zo2SlERcul4uDBw/idrtF9NXhcIgLqaurS5RFLiedThOJRMTgr6WlJS5duiQusHA4LKpV7iSjWW9d0+k0U1NTqFQqZmZmUCgUuN3uFbsjlUqFxWKhUqlgNpup1WpC33K5LOasT0xMsLS0xNGjR7l48SLZbJZ0Oi3Sye4k6q1rPVAoFLhcLrq7u0Vbuo3mpobz5MmT/PCHP2RgYIDPfOYzwNXxo5s1SlZKXnc4HNx99910dHTQ3t6Ow+HAZrPd9M6Zz+eJRqOEQiE++OADQqEQ58+fF1MtGzFifivUW9dCoSB2/tFoVHQP/7jhXL6DWH5jk/zSqVSKqakp5ufn+cUvfsGHH364IevdLtRb13phMpnEjXczyqRvajjvueceLl26dN3H1nvcqNQyX6/X097ejtfrxWKx4Ha7cbvdIqnZbrdjMBium4spBRICgQCRSITp6WlOnjzJ4uIiFy5cIJFIkEwmRXrLncpm6no9SqWSqBc/evQo586dY2RkhJaWFlpbW2lvb7/mAqjVakLXhYUFJicnicfjXLhwgXg8Tjgc3vB1b3XqretmU69ZUVuqckitVuPxeLDb7Rw4cIC7774bl8tFZ2cner1e5O0B1/1lLZ+rPj4+zunTpzl37hz/+q//KgIOUnRWpr5IEe5cLsePfvQjTCYTDz30EH19fezbt4+2trZrXlOr1ZienubUqVNcvnyZd999l2QyKYa5ybreuWy28dxShlOlUmG32/F6vTQ3N+Pz+bBarZjNZpFupFAoKBQKlMtlcrkcmUxGXDClUolwOEw6nebMmTNcvnyZQCAgejvKRnPrIZW3wtX0mVqthlqtplwur+hOJaWPnT9/nomJCfx+P4lEglwud9vt52S2F1KA12Qy1S3HeksZTo1Gw8DAAH19fdx1113s3btXRMqkfM1qtUo8HieVSjE9Pc3ExIQwiKlUip///Of4/X6SyaSYkd6oZZONgNQEN51O895776FWq/mXf/mXa4J8EtKkQyk4JJ0yZO4c0uk0Y2NjpNNp7rvvvmsKKTaDLWU4AXF8SyaTRKPRa7bglUqFhRwd8KAAACAASURBVIUFMdBrbm5uheH0+/1iomUjVvw0ItJuUW4SLXMrSDOKDAYDfr8frVZLOBxmaWlpxQl0I9lShjObzfLWW29x/PhxfvrTn2KxWK77PKnRbSaTWZF7WS6XxYAneRciI9OYxONxjh8/jk6n48yZMxiNRnHCTCaTm9L1aksZznK5TDAYrPcyZGRktjDFYpFIJAIgRnhvNg3fyFhGRkZmvZENp4yMjMwqkQ2njIyMzCqRDaeMjIzMKpENp4yMjMwqUdQ2MXP09OnTN0xsvhMoFAqMjo7WexnrjqyrrGsj8km6bqrhlJGRkWkE5KO6jIyMzCqRDaeMjIzMKpENp4yMjMwqkQ2njIyMzCqRDaeMjIzMKpENp4yMjMwq2TTDeezYMR577DEeffRRjhw5slkfu2pCoRDPPPMMjz/+OE888YSY0xKPx3n++ec5fPgwzz//PIlEos4r3TpsB21lXVePrOsnUNsEyuVy7dChQ7XZ2dlaoVCoPfnkk7Xx8fHN+OhVEw6Ha+fOnavVarVaKpWqHT58uDY+Pl77i7/4i9pLL71Uq9VqtZdeeqn2l3/5l/Vc5pZhu2gr67o6ZF0/mU3ZcZ45c4bOzk7a29vRarU88cQTHD16dDM+etV4PB6Gh4cBMJvN9PT0EA6HOXr0KE899RQATz31FG+88UY9l7ll2C7ayrquDlnXT+a2DOetbuXD4TDNzc3ia6/Xuy1Gufr9fsbGxtizZw+Li4t4PB4AmpqaWFxcrPPqNo7VHNG2o7Z3qq7Q2NfsZuq6ZsNZqVT48z//c77zne/w2muv8eqrrzIxMbGea6srmUyGF198ka9//euYzeYVj9VrlvNmIOvamLpCY2u72bquuVb91KlTfPvb3+a73/0uAC+99BIAv/d7v3fD537+858Hrjp0txs+n++21z0yMrLlm0GsRlfp+dLdXNZ1a7Paa1bW9ca6rnnHuZqt/MjICNPT0wDo9fq1fmRdWY91b4dOM6s9oo2MjACyrtuB1V6zIOt6IzZlWJtareYb3/gGs7OzADz77LOb8bHryssvv3zb675w4cI6rWbroFarxZ1d1rVxkHX9ZF3XvOP0er3Mz8+Lr8PhMF6v94bPP3jw4Fo/SmYTWa2uMtsHWdv1Y82GUzp+z83NUSwWee2113j44YfXc20ydUDWtXGRtV0/1nxUl47fX/ziF6lUKjz99NP09/ev59pk6oCsa+Mia7t+3JaP8+DBg/IRvAGRdW1cZG3XB7nJh4yMjMwqkQ2njIyMzCqRDaeMjIzMKtmUPM7thFKpFP8vL9NSKBQolUpqtRprLLaSkZFZB65XQrnZ16VsOJeh1+sZHh7G5XIxODhIZ2enEKm5uZl77rmHSCRCNBolnU7Xe7kyMncEarUalUol/pnNZlpaWtBoNOj1ehQKBVNTU/j9fqrVKtVqdePXtOGfsI3QarUMDQ3R29vLI488wv333y8M5+nTpxkeHkaj0ZDNZmXDKSOzSajVajQaDRqNBq1Wi8vlYseOHZhMJqxWKyqVikKhICqdZMO5SRgMBpqamnC73QwNDdHX14fb7QauHgGWHwvko7qMzMahVqvR6XTo9Xra2towGAw4HA6MRiMmkwmTyYTNZqOjo0M8T6FQkE6nqVarRKNRpqamNn6dG/4J2wCbzcZdd91FW1sbhw4dYmBgAJ1OJwxktVqVDaaMzCag0+lwOp14vV4ee+wxvF4vXV1duFwu3G43Ho8HpVK5IhZRrVZxu910dnby4YcfMjc3t+HrlA0noFKpxN1Mr9ej1+tRqVTX7DZl6s/yI5vRaEStVqPValGr1cIXJlEoFIjH45TLZSqVCrVaDa1Wi0ajoVgsks1mRRBQvinWB41GI64/g8GA3W6npaWFpqYmOjo6hLF0OBxYrVbR9aharaJQKFCrr5owu91Oc3MzDocDg8EgXGwbpatsOLkqnsPhwOl0YjAYUKvVssHcgigUCtxuNy6Xi/b2dnbt2oXVaqWrqwuz2Yzb7cZisYjnz8zM8JOf/IR4PE46naZUKtHa2orH4yEQCHDmzBk0Gg06nY58Pl/Hn+zORKVS4XK5MJlM7N27l6GhIdrb2xkdHcVgMOB0OtFqteh0OuHHjMVilMtlisUiKpUKt9uNwWCgs7MTm81GOp3m2LFj4kZaKpU2ZO13tOFUKBSoVCp0Oh1ms1nsYKQ7Va1Wo1KpUCwWqVarlEolsXOR2VzUajVKpRKr1UpTUxPNzc10d3djt9vp7+/HbDbj8XiE4VQoFOj1esbGxrBarcTjcYrFIh0dHbS0tAAQCAREpFZm81CpVOLUYLfbsdlstLa20t3dTVdXFwMDA+JkIG1garUa6XRaGM5CoYBGo8FutwOIXabFYsFgMKw4zm8Ed7ThdLlceL1ehoeHeeihh/B6vTgcDgBSqRTpdJoLFy7wxhtv8PDDD3Py5EkSiQSZTKbOK7+z0Gq19PX14XA4eOihh7jrrrtwOBy0tLSg0+lEZDUejxMMBtHpdOh0OtRqNYcPH15x85NukB0dHVgsFuGikTXdeKTouNfrZf/+/TidTnbt2oXb7aa5uRmPx4PZbEav169woSSTSfL5PO+88w5vvPEG5XIZuHr9PvfccwwNDYldqcViwWw2y4ZzIzGZTDQ3N9Pa2kp/fz9utxuj0QhAPp8nmUwyPj7Oa6+9xj333IPf76dUKm3Y9l/m+qjVarxeLy0tLYyOjnLgwAH0ej0mk0n4ssrlMsFgkEgkgsFgwGQyYTab2blzp/CDLXe/aLVa0um0iMzKbDxKpRKtVovb7WZ0dJSWlhbuu+8+vF4vOp0OjUZzzWtqtZq4Fi9fvszRo0epVquo1WpaWlr4tV/7NQBxcpBumhs9P+qOM5wKhQK73Y7RaGR0dJQHH3yQjo4OrFarOBpUq1VmZ2cZGxvj0qVLxONxqtWq2LXIR/WNR6FQoNPpsNlsOBwOHnjgAfr6+ujr68NgMFAsFgkGg6TTaSYmJkgmk0xMTBCJRMTF09bWxoEDB7DZbLhcLnFTBMjlcoTDYSwWi3wj3CQcDgdtbW0MDg4yMjKCx+PBbrej1Wqv2R1WKhWy2Sy5XI5jx44xPj7OiRMnSCQSIgm+ntzUcP7xH/8xb775Ji6Xi1dffRWAeDzOV7/6VQKBAK2trXzrW9/CZrNt+GLXA6VSSVNTE01NTXzqU5/i6aefxmg0Yrfbha+rUqkwOTnJsWPHmJycZGlpSRz3GoWtrKtU3mo0GmltbaW1tZVHHnmE0dFRtFotWq2WTCbD7OwsgUCAH/7wh8zPzzMxMcHCwoKIro+MjOB0OmltbcVkMq0wnNlsllAoRGdnZ8MZzq2qrZQnvWvXLu6++26cTucNd4WVSoVEIkEsFuP111/n5z//OclkklgshsFgWKFlPbipE+Czn/0s3/nOd1Z878iRI+zfv5/XX3+d/fv333T29lZCqVTicDhobW0VuxDJp7LcAR0OhwkEAsJoNhpbWVez2YzP56Orq4u7776b0dFRnE4nGo2GQqHA0tISc3NznD59mvPnzxMKhVhYWCCbzVIqlUSWhMvlwul0YrPZUKvV1Go1EVhIJpMEg0HK5bLwmTUKW1Vbk8mEz+fD5XKtCPwAlMtlcrkcqVSKSCSC3+/nzJkznDp1ilAoRCaToVAoUKvVxE3VaDRuqB/zk7jpjvPee+/F7/ev+N7Ro0f5/ve/D8BTTz3FM888w9e+9rWNWeE6o1KpGBwc5O6772ZwcBCHwyEcycViEb/fz+LiIqdOneLdd98VOYCNxlbWtaWlhZGREYaGhvit3/otnE4nFosFtVpNIBAgEAjw3nvv8Xd/93diF1IqlSiXy1SrVZqamhgeHmb37t3s2rULl8sl/JzZbJZsNsvU1BTvvfcejzzySMMFhraqts3Nzdx9992iznw5mUyGVCpFLBbD7/cTDAZ55ZVXCAaD+P1+4S6Dq0nyHo8Hj8dTtwmja/JxLi4u4vF4AGhqahLzl2+Gz+dDr9fz8ssvr+Vj1wUpd0yK3k1MTIjHqtUquVwOjUbD5z73OR599FHxWHd3d13XvRnUW1cpkioFd/R6PaVSiWg0SiQSEYECtVrNXXfdRVdX13WruqTyPIPBQCwWI5lMisckA9vT08M3vvEN2tra+Ju/+ZuG91uvRdv1vl5tNhtOpxO1Ws3k5OSKBHVpg1Iul7FarRgMBn7nd35HpAAurz9Xq9WYTCY0Gg3VapVLly6Jx+x2O1/60pdwu90cOXJkw+rWbzs4tJroVT3HjUrbe7vdzp/+6Z/y+OOPYzabsVgsVCoV8vk8S0tLvPrqq1y8eJETJ05w/vx58fo7bYzsZuuq1WrZv38/PT093HvvvTz44INYLBZ8Ph+lUolTp04RDoc5duwYJ0+eJBqNMj09LXaZy/n1X/91nnvuOYxGI729vWJXUqlU+MlPfsI777zD2NgYP//5z/nrv/5rnnnmmTWvG7aXrnDr2q739drR0UFvby8tLS088MADqNVqpqamSCaTLC0tEY/HyWQyLC0tkc/niUQiFAoFSqXSCo19Ph8PPPAA7e3t/PZv/zaDg4PisZ/+9Kf8z//5P/nd3/1dXnjhBbLZ7JrX+0m6rslwulwuIpEIHo+HSCSC0+lc8+I2C5VKhV6vx2g04na7RRL08iT3bDZLIBBgenqaRCJR5xVvPvXSValUotFoaGlpYWBggMHBQYaGhkQdcjabJRwOMzMzw+XLlzl79iylUumaYJ1kEGw2G11dXcK3CVdPE5VKhXA4zMWLF/H7/cJndiewFa7ZxcVFisUiqVQKj8eDWq3m3LlzxGIxotEoiUSCfD5POp3+RF2kv5frReM3izUZzocffphXXnmFF154gVdeeYVDhw6t97rWDamm2e1288gjj9Da2kpvb++K5yQSCU6fPk0oFGJsbIwrV67ckYazHrpaLBaGh4fxeDx8+tOfZvfu3Xi9XqrVKul0mvn5eRYWFvjZz37G+Pg4k5OTFAqFFX5nKYfP6/WKzjl2ux2TyYRSqaRQKHD+/HnC4TDHjx/nwoULpFKphvRd34itcM1KRnNubo63334bgGg0SqFQIJfLCV1vdjOTOie1trbWLQf3pobzD/7gD/jggw+IxWIcOHCAr3zlK7zwwgv8/u//Pj/4wQ9oaWnhW9/61masdU1oNBqsVittbW0cPnyYnp4e2tvbVzwnlUpx/vx55ubmuHLlyjWO9UZkq+hqNBrZs2cPnZ2d7Nu3j127dondYTqdZmZmhkAgwPHjxzl//rzwUUpIZbNarVYkyft8Pmw2m9iRFItFcUM8e/YsV65c2fCfq55sFW0/jlQ8kslkCIfDa34fnU6H1+vF4/Gg1WrXcYW3zk0N5ze/+c3rfv973/veui9mIzAYDDQ3N4s0CLvdLiJ66XSaZDLJ3Nwcly5dIhQK3ZZPZDtRb12VSqVIZO7u7qanp0fUmUspYbOzs7zzzjvMz88TjUZX+DPVajV6vR6LxcKOHTuw2+0MDAzg8Xjo6+tDrVZTKBSIRCIsLi5y8eJFxsfHWVpa2pSfr57UW9vbwWq1imO8lFf98X64vb29uFwubDabuJYzmQy5XI6FhQUWFhaoVCobeqJo+Mohu93Ojh07xE7T5/MJQWKxGFeuXOHMmTMcO3aMhYUFUqlUnVd8Z6BSqTAYDLhcLu69914GBgaw2+3UajWi0Sjj4+OcPn2av/3bvyUajYqqLQm9Xi96MD7//PN0dXXR0dEhUo80Gg2JRIJz584RCAR46623GBsbu2NujNsVj8fDQw89hF6vF6WTy/2Y1WqV5uZmOjs7sdvtom/u4uIi8/PzTE5OMjk5SbFY3NCClYY1nNIv3u12097eTnNzs0h0l3wpkUiEqakpAoEA6XSafD6/KW33Zf6tOkjaOUqdqQCSySQzMzPMz88LTaTdhV6vR6vVip1JW1sbPp8Pt9st0lgkisUii4uLYkZULpdruGT37YoUyJP6cUpdjXp7e+np6UGv16PRaFYMTZR8nw6HA5vNJqqHpJS1K1euEA6HKZVKG954vCENp0KhoK2tjfb2dvbt28fnPvc5bDYbNpuNarXK/Pw8sViMN954g1deeYV4PC4ifrLh3Bwk36RGoxENOaSdxYULF/j7v/974vG4KJG966678Hg84uRgs9nw+XwYDAZaWlrEhbaceDzOiRMnmJ2dZWFh4Y6Kom9lpKi4RqMRHZH27dvH7t27aW9vZ8+ePdcc1Ze3elSr1eIGKfXofOutt3j11VcJh8MUCoUN/xkaznBKF6TNZhO+TZ/Ph9FoFAmz2WyWVCrFwsICc3NzZLNZ2WjWAemCkCq3pJ1FsVgknU6LZGiTyURrays+n4/e3l66u7uxWq14vV7RiHj5cU66wAqFAouLiywtLclGcwsgaa3RaLBYLOJEaLVa6ejooK+vT1yvKpVKXI9S1sTHd5HVapV8Pk+pVCKRSBAOh0kkEvKwttWiVCrFlv/Tn/40Tz75pOgKLjXCrVarZDIZotGoqCqRjebmI+XPSlHWTCaDXq9HrVbz4IMP4na7RcWIWq2mqakJo9GI1WoVnXGKxSKlUolCoYBCoRA3R+l7CwsLXLhwQbhiZOqDdFO0Wq04HA46Ozt5/PHHsdvtooO72+3G4XCIdn/FYpH5+XnK5TJtbW04HI5rmk5Lo21UKhWtra2Mjo4yOztLLBbb8AkODWU4pXI9s9nM4OAgDz744HWfl8/nSaVSZLNZ8vn8HZXPt1Wo1Woi7ahYLJLP59FoNKjVatE+bvlzP042myWRSIiLR+r1qNFoqFQqFAoF0uk0oVDotlJfZG6f5d2unE4nfX19PProoyvayklpZtLfQjqdJhAIUCqVsNls4mb58YR36b0dDgcdHR1kMpkV/VflmUO3gE6nY8eOHfh8Ppqbm687bK1UKjExMSF8X/LxrT5Uq1WxK3zjjTcYHx9n37599PT0iHkx5XKZTCZDsVgkEomI/L9oNEoulyOdTmM2m0VHeIPBgMFgYHFxkenpaWZnZxuuZdx2QnLBSH7MvXv3sm/fPtra2mhqakKtVnPx4kVSqZQouUylUiIpPplMiuwLo9GI2Wxe4cdWKBRotVrRuEfylQcCAbRaLQaDgUKhsCE9dBvOcO7evZuhoSFaW1uv+8sqlUqcP3+eN998k6WlJfmIXiekPLv5+Xn+3//7fzQ1NeF0OvH5fGLsRbFYFBfTqVOniEQifPjhh1y4cIFCoUA2mxVVQj09PaKMcGFhgbNnzzI5OSkbzjqx/BQgFSU88sgj/NZv/ZYI/CQSCc6cOcPk5KQoPAmHw2Iuuslkwmq1ivp2yRW3/DOkINGuXbsYHh7GZDIxMTGBTqfDaDRSLpc3JMLeEIZTqkO3Wq24XC6amprEL3R595VUKkU8HieZTJJOpzcl+ibzyZTLZeLxOACnT5+mWq2KyYZSxDSXyzExMUEsFiMYDIpou5SeZLPZsFqtoopE8pvm83n5RFEHpO79zc3NWK1WhoeH6ejooK2tTbhSpMYec3NzTE9PEwwGiUajosWf0WhkYGBADOYzm81otVrRISsajYq/FSkzQ8oL3rVrl5jwEI/HiUQipFIpqtXqio2SFFxai6uuIQynXq8XUdcdO3YwPDyM2+1e8ZxcLsfY2BjhcJjJyUnm5+fl3eYWQOqBGgqFmJ2dXTEvRvKDLp8wKpXtuVwuurq6aGlpob+/n56eHlQqFQqFQlSQLO/hKLM5SMEap9PJI488QltbGw8//DCDg4Ni+mQikeDixYsEg0GOHTvGhQsXyGQyZLNZ0TKuo6OD3/7t36a7u5vBwUG8Xq8IKIbDYX72s59RKBTw+XyYzWb6+/vp7Oykv7+flpYW/H4/X/nKV1hcXOTYsWNcvnxZ+E+lm2mpVCIQCKypH2tDGE7p7ubz+URzB8kXUq1Wha8sHA4TDAZJpVJyIvQWQerKLnVmX83r1Gq1GKUh7UZqtZpoJpHL5WTDuclotVrMZjNOp5OWlhZaW1uFGyafzxOPx4lGowSDQUKhkMhska5Hg8FAU1OTSEtqbm4Wp0epQ3woFBLdrSqVimgPKRVRSD0KvF6vaAiSz+cpFArCcEppict3uauhIQxnV1cX/+W//BdaW1vp6+sTg9cA0WFnenqaf/iHf2BmZobp6en6LljmtpH8Z1IEVTKatVqNYDDIBx98IGapy2w80imhs7OT+++/n/b2dp544gk8Hg9Wq5V8Ps/Zs2c5ceIEoVCI48ePE4/HmZycJJfLiT4SQ0NDHDp0CK/Xy8jICDabjXw+Tzgc5sSJE7z99ttEIhFOnTpFsVgUDcm7u7tFSfXg4CAulwuXy4XP56OlpUUEiaShi6lUivn5ef7qr/5qTf0LGsJwSq3J2traVpTuASLAsLCwwPj4OFNTU7JvswGQsiU+Xo4HiNNFNpuVd5ybhBRBl4I57e3tdHZ24nK5yOfzFItFFhYWGBsbY35+nrGxMdGYQwryOBwO2tvb2b17N06nU+R1JpNJkskks7OznD59msXFRSYmJiiVShgMBjQaDel0msXFReLxOCaTSZToWq1WmpqaVkTjc7kc0WgUs9m85qFvDWE4lUqlSEVZniALVyOsH3zwATMzM2IHIudtbn+kccB6vV6+EdYZhUJBe3s7ra2t3H333Rw4cEA04Mhmsxw/fpzp6WnOnj3LmTNnKJVK4jgulc8ub8LT1dVFqVTiww8/JJVKMTY2RigU4tKlS1y5coVcLif6dhaLRcrlMvPz8ytOly0tLbz//vs4nU66u7tXNG6en5/nvffeIxQKiS73q+WmhjMUCvGHf/iHLC4uolAo+I3f+A2ee+65LTFuVEKKqkudUpbvPmKxGGfOnCEUCpFKpeT0lP+f7aDrJyFVGzmdTvk4vox66KpQKGhpaWH37t2Mjo5y1113iayIbDbLmTNn+OCDD5ienmZ8fByLxUJvby8Oh4OHHnqI1tZWBgcH6erqEp2twuEwFy5cYG5ujvfee4/JyUmRFbMcyTcajUaJRqMAnDlzhv/8n/8zP/3pT/F4PGQyGTo6OsRrxsfH+dGPfiRmWa2FmxpOlUrFH/3RHzE8PEw6nebpp5/mgQce4J/+6Z/Yv38/L7zwAkeOHOHIkSObPjWvpaWFjo4Odu7cKaKxH0eqV47FYvJOcxlbWddbwWAwYLPZrjmG3enUQ1eFQoHP52NkZIT29nbUavWK7ldtbW2k02m8Xi/d3d1YLBba29uxWq1i0qw05iSRSDA/P08oFOLs2bMEg0HC4bAohLhVqtUqgUBAtImcnJwUj0mpT+l0es024aaGUxrDCVfnXff09BAOh7fEuNGdO3fymc98hq6uLoxG43UNZzqdZnp6WnQ/krnKVtb1VrDZbAwODtLT01O3EbFbkXroqlQqGRoa4vHHH18RY5BybSWDKtWaG41G0aBFpVIJ/6hSqWR+fp7XX3+d2dlZfvzjHxOJREQq2mpycqvVKhcuXEChUHDixIkVpZpSWtvtJMavysfp9/sZGxtjz549dR03unwol+RAnpmZQalUil+EFEFzu9389//+38X40bX+ohp5PPBW0XU1SCV1Op2OVCrFxYsXxWO7d+/mm9/8pvgb+CTNZV1vzK3qKu04peKShYUFYGXxiXRzUygUFAoFMZ5meTaElJAuVf/df//9a75mu7u7+Zu/+ZtVv+5WuWXDmclkePHFF/n6178uCu4lNnPcqJSfZbPZxJ3TYrHQ3NyMSqUSs5nPnDnDxYsXOX78OP/4j/9IJpMRKQlroVHHA28VXVdLX18f999/P729vTz//PO0trYCVy/Eo0eP8j/+x/8Q7QM/SXNZ1xtzq7qq1Wq++tWv8uyzz4p2jss3MVKertQlKZFIMDk5KWZKJRIJrly5wszMDLFYDL/fL3I+15pvvdG63pLhLJVKvPjiizz55JMcPnwYqN+4UamcS6/XYzKZROKr9IcgddyRyrmksQu3s9tsVLaSrmtheR/P5Z1wNrql2FanHrrmcjkRuLFareIIvlwXaVOTTqeJRCIixSgWi4lUQWmuunQdb1VuajhrtRp/8id/Qk9PD88//7z4fj3HjWq1WtEJRapRVigUYqSslALxz//8z8Tj8RXpCzJX2Yq6roZ0Os3k5CQajYZisXjdfM47kXroWqlUePPNN/H7/aLsWRrJLQ3NK5VKzM3NMTk5KfJsC4UC8XicfD5PMpkUFX3b4Vq9qeE8efIkP/zhDxkYGOAzn/kMcHX8aD3HjUpOZ2nnKUXxpFZlmUyG6elpTp8+vWlr2m5sRV1Xg9TowePxrNiZ3Om7zXroWqvVGB8fZ3p6mra2NmKxGFarla6uLuGDzufznDt3jhMnTlAsFslkMqIceqsbyetxU8N5zz33cOnSpes+th3Gjcpcn+2uaz6fF4PYpF6OBoMBtVqNTqfDZrOhVCrFBXqnUC9dJV/k0tISFy9eRKfTMTc3t2LHGQqFyGQyoqXgRvTJ3CwaonJI5s4jn8+Tz+dxOBwsLCzQ1NQkjoZ6vR6XywUgIrwyG4tkDCORCAsLC9fd+W/05MnNZFsaTmmYl1RJYDAYMJlM9V6WTB0oFosEAgGMRqMIGDocDvr6+jCZTESjUTEi5U7aedaTRjKQN2LbGc5qtUo8HqdUKokSLpfLRU9PzzXzSGQan2QyyVtvvcXk5KSYfDkwMMCv/dqvcenSJZaWlohGo4TDYXK5XL2XK9MgbDvDCVfTLaQ8r0AgIBqgKpVKlpaWSKfTa+qxJ7P9KJfLLC0todPpSCaT5HI5tFotHo+HWCyG2+2mWq0Si8XkjvAy68a2M5y1Wo1cLkehUOCtt95ibGwMrVYr2kNJ0/JmZ2frvFKZzSCbzYoRwKOjo1gsFux2O/39/ZhMJtLpNMFgkH/+538mn8+LdBcZmdth2xlO+LcIXjAYJBgM1nk1MvVE2nGWSiXCHGUVfQAAG2tJREFU4TDhcBiDwYDVaqVYLNLT0yO6kqvV6i2fWC2zPdiWhlNGRmL5qIy3336b6elpRkdHWVxcRK/X09XVhcViwefzEQqFSCQScmtBmdtGNpwy25parSYGuJ08eZIzZ86QTCbR6/V0dnYyNDQkpp9aLBY5QCSzLsiGU6ZhkFw4gUCA48ePMzU1RSQSEeOFk8mk3C1eZl2QDadMwyDtPM+dO8fFixdRKBSo1WpqtZqYiChH1WXWA9lwyjQcUhszGZmNQlHbxFvw6dOn7+hu3YVCgdHR0XovY92RdZV1bUQ+SddNNZwyMjIyjYBcoygjIyOzSmTDKSMjI7NKZMMpIyMjs0pkwykjIyOzSmTDKSMjI7NKZMMpIyMjs0o2zXAeO3aMxx57jEcffZQjR45s1seumlAoxDPPPMPjjz/OE088Iea0xONxnn/+eQ4fPszzzz9PIpGo80q3DttBW1nX1SPr+gnUNoFyuVw7dOhQbXZ2tlYoFGpPPvlkbXx8fDM+etWEw+HauXPnarVarZZKpWqHDx+ujY+P1/7iL/6i9tJLL9VqtVrtpZdeqv3lX/5lPZe5Zdgu2sq6rg5Z109mU3acZ86cobOzk/b2drRaLU888QRHjx7djI9eNR6Ph+HhYQDMZjM9PT2Ew2GOHj3KU089BcBTTz3FG2+8Uc9lbhm2i7ayrqtD1vWTuS3Deatb+XA4THNzs/ja6/USDodv56M3Bb/fz9jYGHv27GFxcRGPxwNAU1MTi4uLdV7dxrGaI9p21PZO1RUa+5rdTF3XbDgrlQp//ud/zne+8x1ee+01Xn31VSYmJtZzbXUlk8nw4osv8vWvfx2z2bziMYVCcd3xp42ArGtj6gqNre1m67rmWvVTp07x7W9/m+9+97sAvPTSSwD83u/93g2f+/nPfx646tDdbkgdxG+HkZGRLd8MYjW6Ss+X7uayrlub1V6zsq431nXNO87VbOVHRkaYnp4GQK/Xr/Uj68p6rHs7dJpZ7RFtZGQEkHXdDqz2mgVZ1xuxKf041Wo13/jGN8TkyWeffXYzPnZdefnll2973RcuXFin1Wwd1Gq1uLPLujYOsq6frOuad5xer5f5+XnxdTgcxuv13vD5Bw8eXOtHyWwiq9VVZvsga7t+rNlwSsfvubk5isUir732Gg8//PB6rk2mDsi6Ni6ytuvHmo/q0vH7i1/8IpVKhaeffpr+/v71XJtMHZB1bVxkbdeP2/JxHjx4UD6CNyCyro2LrO36IDf5kJGRkVklDTvl0mAwoNfrUavVIjWhXC5TrVZJp9Pk83mq1ao8LlZGRmbVNKThVCqV3HXXXYyOjtLR0cHo6CjVapWFhQUSiQQ/+tGP+Oijj8hms6TT6XovV0ZGZpvRsIbT4/GwY8cOBgcHOXjwINVqlUAgwOLiIidOnODy5cuUSqV6L1VmDUjlc1IpnVK5No9TrVZr6BLL7crHda1UKlvuZNhQhlOpVGIymTAYDPT19TE6OorH40GpVKJUKnE6nahUKpqammhqaqJSqRCPx7ecKDIrUSqVWCwWtFqtuKgsFgtutxuHw8F9992HzWajVqvdspaJRIJgMIjNZsPtdhOLxWTXTR2RdDWbzTidTux2O6Ojo2i1Wt588/9r7+xj2zjrOP49n1/uzueX+D2xnaRp2qZJN9aljBXYgHYNjKkiZYh/NkD5ZxJIVGNCCDYJCQlNYn+gSvy1iX9ACAmEIEijMNTCYJumvXSgrtRts6Rp4tixHb+/nu/s44/qbnHTJnab2Gfn+UhRVTvJPc7Xz9f3PM/v5XVcu3at00NsoKeMk6IocBwHi8WCYDCI8fFxGI1G9ZPLZrOBpmk4HA44HA7kcrkOj5jQDIpxchwHnU4Hmqbh8XgwOjqK4eFhfPOb30QgEGjJOJeXl3Hx4kVYLBbYbDZks9mWfp6wvVAUBZqmYTab4ff7EQwGcfLkSfA8j4WFBWKcO4ksyxBFEZVKBZlMBvF4HFarFQzDbFjOkSWadjGZTGAYBizLwuPxgOM47N27F1artWH1EAgE4Ha7YTabW76GMkEFQcDk5CTcbjcWFxeRTCZRq9VQr9d34JUR7gRN0zAYDAgGgzh27Bjcbre6WuQ4DgzDQJIkSJLU6aEC6EHjLJfLqNfriEQimJubQyAQgMvluut9MEL7MZvNcLvdGBgYwMMPPwyXy4XJyUl4PB51ScdxHPr6+kDTNIxGY8t3i3a7HRaLBVevXsWpU6cQiUTwpz/9CcViEdVqFYIg7OArJNyKwWAAy7K4//778Z3vfAccx6FUKiGbzcJut8NqtWrqMLenjBO4uZEsSRIEQUC5XEa1Wu30kAgtYrFYEAgEMDAwgGAwCIfDAafTCbvdrq4UGIZpWEkoptmseVIUBb1eD4qi4PF4IMsy7HY7WJaFLMvEONuM8oGo1+vBcRw4jkOtVoPRaFS/tKRJTxmnslSXZRn5fB5ra2twOBxk36rLOHDgAE6dOgWfz4fDhw+D4ziwLKsaHXBz33M7tltomsaDDz6IbDaLCxcuIBqNYnV1FcVikbxvOghN07BYLOoHms1mgyiKmmmm11PGCUBdsin7VGSvqvtgWRZOpxMulwtOpxMsyzY8L8sy6vW6egeiGOitRqeEtKz/93ZYLBZQFAWz2QyGYWAwGHbgVRFaQbn7NBgM6r80TXd6WCo9ZZwURcFoNMJkMsHlcsHv98PhcJCDoC4jmUziypUrEEURhw4danhOEAQIgoBYLIa5uTlIknRH4+Q4Dl6vFyzLYmBgABzHte01EHqbnjJO4GYFGKPRCJ7n0dfXB7PZTIyzyygUCohGo+jr60OtVlMfXx81kUgkEAqF1D1snU63YXVht9tRr9fVmM9mjJMsz7WFVuduTxmncnuvbDBbrVawLNvwx9fpdLDb7fD5fMjn84jFYhBFEeVymUwajZBIJPDhhx9ibW0NgiA0GF6lUkG1WkUsFsO1a9cajBVoND6O43D58mU4nU6YzWYYDAYYjcaGpbgsy1haWkI6ncby8jJisRjy+Tx5L2gEWZZB0zT0er263aIFbbY0zh/96Ed4/fXX4XQ68eqrrwIAMpkMvve972FlZQV+vx9nzpyBzWbb8cFuhWKcRqMRZrMZNpsNHMdt+NRSYgCV7JFSqQRBEDZMwl5Gy7pGo1HE43EwDIN33nmnIZRM2bculUrI5XKb7mHr9XqwLAu/34/Dhw/D7XbDarU2GGe9XsfCwgKi0SgWFxexsrKimVjBu0XL2jbL+nTY9capFbYcyVe/+lX88pe/bHjslVdewdGjR/H3v/8dR48e3bL3drtQlnKCICCdTiMajaqpdAp6vR5erxd79+7F4OAgPB6PGli9m9CyrsrhXrVaRbFYvO2XIAhqtas7fel0OlitVlitVhiNxjtOvnK5jEqlAlEUeyL4XcvatoJinizLqoksWmFLt/jkJz+54ZPp/PnzmJ6eBgBMT0/j3LlzOzO6FlFKxqVSKYRCIbz55psblnMGgwFHjhzB9PQ0vvCFL+CBBx7A6OjorjtJ1bKuyql5tVpFOp1GKpVSv9LpNLLZLEql0pa/h+d5jI2NYWxsDC6XS12u33qtfD6PdDqtmme3G6eWtW0VnU4Hl8uF4eFh9PX1aWbP8672OJPJJDweDwDA7Xar/Ze3or+/HwzD4Ne//vXdXLZplNASjuNgNpsxPz9/2z94f38/nnzySQiCgKeffnrTCbNnz54dH3en0bquzaIEUxsMBtUsDQYDlpaWNrwPJEmC3++H2+3Gnj17UC6XOzTqneVutG2nrkoqbV9fH5aWltT9zHq9jrGxMQwODuJLX/pS0/G1Oz1f7/lwSHmTNkM72406HA5YrVZMT0/jJz/5CXieB9B4ePCPf/wDZ8+exeLiIs6dO4disXjH37fb2shqVddmCAaD2Lt3L/x+Px555BG43W6Mj4/D6/VueF0XL17EL37xC1y5cgXhcBiZTKbl63WTrkDz2rZTV6Xw+PT0NF588UV1aV6tVvHaa6/h8uXLeOONN/Daa681tSLQZHtgp9OJeDwOAIjH43A4HHc3sh0klUphcXERiUSi4Q/diiHsNrpB19uhBLgrhSJsNhv6+/vh9/vh9/vR398PlmU3BMErWwLhcBg3btzQTB70TqB1bRUtZFlGtVqFJEnqHqfRaFQzx7TCXY3k2LFjmJ2dxTPPPIPZ2VkcP358u8e1bShGeWsYAzHQjXSTrgosy2JoaAhWqxVjY2Pw+Xzw+/0YHByE1WrF4OCgWmlpPeVyGZlMBpIkoVKpaLJY7naidW2VVOlUKoUbN26gVCphaGhIs4e2Wxrnc889h3fffRfpdBqPPvoovvvd7+KZZ57Bs88+iz/84Q8YGBjAmTNn2jHWu2a9Sfby5GiFXtAVuFnsY8+ePfD5fHj88cdx8OBB2O12OJ1O0DR9xzS9SqWCtbU1SJLUEwdC6+lGbWu1Gmq1GnK5HCKRCGRZxsDAAEwmU6eHdlu2NM6f//znt338V7/61bYPZqdQ8teJaX6MlnVVAtVZloXL5QLDMLDZbDAajRu+12azYXJyEk6nE4ODg7Db7eA4DjRNN6wolIkZiUQQj8eRSCRw/fp1HDx4ELlcrqfuOLWsba+gnU2DHeJOpkmMVLswDAO73Y7+/n489NBDcDgcGBsbg91u3/C9PM/jwIED4HkeNE2r+5i37mUqNTbff/99vP3221hZWcHly5fx4x//GPF4nPSfIrREzxvnrRCz1C5KBRyPx4PBwUH4fD4MDw/DZrPB5/PBarVu+BmO48DzvBocfTt96/U6yuUySqUSEokEwuEwYrEYMpkM6vX6rsoY60aUgz8t7XfuOuMkaBOdTgen0wme53HixAlMT0+rp+NGoxEMw9x2v1I5dd0MSZKwvLyMRCKB9957D//85z/VYiFK4WuCNqEoCgzDgOd5mEwmzRzoEuMkaAaDwQCGYeB2uzEyMqJ2PGwmDEWJmrjdxKrX6xBFUU3hVHLcSVfL7mCzD85OQYyToGmaucNYv4S7nRGaTCaMjIzA6/Xi0KFDmJ+fRyqVQjgc3taxErYfmqYRDAZhsVhw4cIFcsdJIGwXd7rTVKBpGk6nEzabTQ2Il2UZkUikjaMktILyAah0NFXaOGuFXWGc6wPg108wq9WKQCCAQqGgqayE3Ygsy8jlchBFERcuXIDJZILD4cDo6Chomsba2prawfR2d5XrExxcLhd8Ph9sNhuGhoZ2XQGXbqZQKKgrAS1HOvS8W6zPHAIal3I2mw3Dw8PIZDKa2j/ZjciyjGw2i2w2i7feeguhUAj9/f04evQoKIrCpUuXkE6n1dJvm3Ho0CFMTk5iaGgIPp9PNU4SgqZ9CoUCFhcXQVGUpjvU9rxxbobSk8blcsFisaBSqaBSqfRUFkk3Uq1WUSgUkEwmMT8/D5qmEY/Hkc1mm2rAF41G8dFHH0Gv16s5z4TuQJIktT6q8gG5vtoVy7IQBKHjptrzxrm+eMCtE8jr9cLhcKBarSIYDIKiKMRisZ4tLdYtlMtlCIKATCbTsGxTDHMrI0yn07h69SpSqZQa1kToDkRRRCqVgtPpVPVWSs5xHAen04lCoYB0Ot3R+NueN05JktQ9TJPJ1HACq/QnUtps5PN5pFIpYpwdRqkAr1SBbxUl9IjEZ3YfSkLC+hRYpfqVTqeDXq/XxLZazxvn6uoq3njjDfh8Phw+fBgWi2XD9/T19eHhhx/GysoK0uk0crlcB0ZK2C6UONDh4WFyMNRlKB+atx4CUhSl9h4ixtkGisUiIpEIdDrdHU/pTCYT+vv7IUnSllkohO1nuypXKXthZrMZXq8XdrtdE5OM0DzK1ppyx7n+PbH+jrPT8ZxbGmc0GsUPfvADJJNJUBSFr3/96/jWt77VNV3zlPJhHMeRpds6Oq2rYnIMw6gn3/F4HIVC4a4aptE0jeHhYTidTjz00EN49NFH4fP5NNXgqx10Wtd7JZvNIhQKQZZlRKNRtQWOyWTC4OAgPvOZz2BpaQmpVKqj83nLrHmapvHDH/4QZ8+exe9+9zv89re/xUcffdQ1XfNEUVSX36SYw8doQVedTgeGYRAIBNRiHndbzIGmaQQCAUxMTOBTn/oUTpw4gcnJyV1nnFrQ9V5QwpGU7g2ZTAa1Wg16vR79/f04fPiwJporbvkO9Xg8mJiYAHCzhNfIyAhisVjXdM3L5XK4du0a5ufnkU6nUSwWIUlSQ3yn2WzG6OgoRkdH4XK5wPN8x4XZaTqlK8/z8Hg82L9/Px555BF87nOfw+c//3l89rOfhd/vB8MwTSUj6HQ6GI1GmM1mBAIBjIyM4L777sORI0cwODi4YUm3/tBh/VKw1+j2+aogiiISiQRWV1dRLBYhiiIikQg++OADzM3NdVc4UjgcRigUwic+8Ym77ojYbmKxGNbW1pBMJnHq1CnwPA+Xy9VgjA6HA5/+9KcRiURw9uxZrK6uIpVKaTpzYTtpl64URalZPRMTE3jsscfgdDpx6NAhyLKMcDiMxcVFyLKMSqWy6e/S6/XgOA5WqxUTExPwer14/PHHMTk5CZZlG1plyLKsVnpXTtx3Q6xuN85XhUqlgoWFBciyDLvdDp7ncfnyZfzxj39EuVzueORL08ZZLBZx+vRpPP/882rHSIVm+/d0so2sUkU8nU6jUCg0HBooYxdFEU8//TSmp6chSVLD0r5X2wO3U1eKotRuhgzDwGKxgKZpdRJ/7Wtfw9TUlHpnuBnKQYFOpwPLsjAYDLBarUgkEg3jVk5nlTvN8fFxBAIBSJKEarWK4eFhousd6OR8NRqNcDgcMJlMEEURS0tLePDBB3HmzJmmMsA00R5YFEWcPn0aJ0+exNTUFICPu+Z5PJ6mu+Z1so3syMgIvv/972Pfvn0YGxvDwMBAw/MURWFlZQUvvfQS3n77bSSTSWSzWfX5XmwP3G5d9Xo9ZmZmcOrUKdhsNoyMjKil5ERRRCgUwqVLlyAIwpZ3FG63G6Ojo+B5HkNDQ2osLsuyqjHkcjlcv34dmUwG77zzDqLRKN577z18+OGHkCQJgiAQXTehk/PV7/fjqaeewvDwMI4ePYpgMIjZ2Vn89Kc/bapH1E7ruqVxyrKMF154ASMjI5iZmVEf13rXvFup1WooFovI5/Mb0vBu7YC5G+iUrlarFQMDA7Db7bBYLOpBEEVRcDgcCAQCEARB7Xp4J3w+n2qcSidL5U5E2cMsFotYW1vD2toabty4gZWVFcTjcRSLxW19TVqiV+brrayP42wm7Xan2dI4L1y4gD//+c/Yv38/vvKVrwC42UVP613zbqVYLOKDDz5APB5HIBBAMBjs9JA6itZ01ev1OHjwIAKBQFPtLFiWhdVqhcFgUPery+UyRFFELBZDJBLB9evX8de//hWpVAqRSATFYhGZTKYdL6djaE3Xe0Wp8M8wDAYGBtT92oWFhY6eQWxpnEeOHMHVq1dv+1w3dc0TBAHLy8sQRRGFQqHTw+k4ndT1dneSOp0OXq8XXq+3pd+1PnheaYextraG+fl5hEIhvPnmm8hkMj3XAvhO9Mp8BT7WlqZpGAwGtZ6qTqfD0tKSto2zV6jVashkMjAYDEgmk0ilUmAYBhzHdXpouwZZlnHlyhX87W9/g9/vx8TEBHieRyAQaLp/tlIARDnwUYpCVCoVzM/Pq83Yrl+/jng8jlKpRCokdSEmkwnDw8PYt28fzGYzarUaSqUS1tbWNBGTvWuMU5lgsiwjHo8jFovB5XIR42wj9XodFy9eRCwWw4EDB1Aul+Hz+eB0Ops2zlKphGw2q4YXFQoFXLt2Del0Gm+99ZZqnqurq+p+J6H7YBgG+/btw/j4OMxms1qsJ5FIqOUFO8muMU4lNjCfzyMUCqnhK0qvboqikEwmsbq6inK5TNIzdwBZllEqlZBOpxGJRBAKhZBMJuFwOOB0OuF2u9UDI6W2QC6XUydNtVpFNBpFIpFQl+XlchnhcBj5fB7RaBSZTAalUqnjdySEe6NeryOfzyObzUKWZTAMA0EQIElSx00T2EXGKUkSMpkMcrkcfvOb36gl5tan99XrdWSzWQiCoAlxehEl/XV1dRX/+9//4HA4sLi4iP7+fjz22GMYHx+H0WgEy7LI5XK4dOkSstksrl69inQ6jStXrmBhYQGVSkXNa1cmk5YmFuHeqFQquHHjhrr3bbFY1L1qLWy97BrjBKCGMfT6yaqWUTRYX2szEolAFEWEw2FYLBaYTCaYTCakUiksLy8jm80iHA6rp+OxWEw1zk5PIMLOoERHKAHwPM833TqlHewq4yRoByWjJ5PJ4N1334XRaMR//vMf8DyvFvqoVqtq3K2Sr1wsFlEul3s215xwk2Qyid///vdgWRZGoxF6vR7xeBzxeLypzLKdhhgnoWPIsoxqtYp4PA4ApM85QUWJktAqrdfvIhAIhF0OMU4CgUBoEWKcBAKB0CLEOAkEAqFFiHESCARCi1ByG2M6/vvf/zadWteLCIKABx54oNPD2HaIrkTXXmQzXdtqnAQCgdALkKU6gUAgtAgxTgKBQGgRYpwEAoHQIsQ4CQQCoUWIcRIIBEKLtM04//3vf+OLX/wiTpw4gVdeeaVdl22ZaDSKb3zjG/jyl7+MJ554Qu3TkslkMDMzg6mpKczMzDS0Dt7tdIO2RNfWIbpugtwGJEmSjx8/Li8tLcmCIMgnT56U5+bm2nHplonFYvKlS5dkWZblfD4vT01NyXNzc/LPfvYz+eWXX5ZlWZZffvll+aWXXurkMDVDt2hLdG0NouvmtOWO8+LFixgaGkIwGITRaMQTTzyB8+fPt+PSLePxeDAxMQEA4HkeIyMjiMViOH/+PKanpwEA09PTOHfuXCeHqRm6RVuia2sQXTenLcYZi8Xg8/nU/3u9XsRisXZc+p4Ih8MIhUJqL2ePxwMAcLvdSCaTHR6dNuhGbYmuW0N03RxyOHQHisUiTp8+jeeffx48zzc8R1GU2vOZ0F0QXXuTduvaFuP0er1YXV1V/x+LxeD1ettx6btCFEWcPn0aJ0+exNTUFADA6XSqlcrj8TgcDkcnh6gZuklbomvzEF03py3Ged9992FxcRHLy8uoVqv4y1/+gmPHjrXj0i0jyzJeeOEFjIyMYGZmRn382LFjmJ2dBQDMzs7i+PHjnRqipugWbYmurUF03Zy2Ffn417/+hRdffBG1Wg1PPvkkvv3tb7fjsi3z/vvv46mnnsL+/fvV1sHPPfcc7r//fjz77LOIRqMYGBjAmTNn1J7su51u0Jbo2jpE1ztDqiMRCARCi5DDIQKBQGgRYpwEAoHQIsQ4CQQCoUWIcRIIBEKLEOMkEAiEFiHGSSAQCC1CjJNAIBBahBgngUAgtMj/AaKMpR1fI68cAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "3sQg9MrX7Fxi" }, "source": [ "##**Model building**\n", "\n", "The next code snipped shows the basic structure for constructing a _deep_ neural network. It consists of one main API call - the Sequential class. \n", "The main difference to the XOR network discussed above is the use of _convolutions and _pooling_. These layers enable local aggregation of the pixel space, and when layered, offer the capability to learn potentially modular structure (lines - shapes - object etc.)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 528 }, "id": "KsvTEDhsHnRi", "outputId": "ca265185-e2d6-44fc-b1be-30e654b43cc6" }, "source": [ "from IPython.display import Image\n", "Image(url='https://miro.medium.com/max/700/1*Fw-ehcNBR9byHtho-Rxbtw.gif')" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "markdown", "metadata": { "id": "SO_62KFcIHcl" }, "source": [ "*Convolutions* are commonly coupled with a pooling operation. Let's inspect a two most common ones next to gain some intuition." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "lulkikZTIASh", "outputId": "f843ddbd-201a-45ed-b3e3-80127e204c02" }, "source": [ "Image(url='https://www.researchgate.net/publication/333593451/figure/fig2/AS:765890261966848@1559613876098/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max.png')" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "markdown", "metadata": { "id": "k0EGLGtqHy9S" }, "source": [ "There are always two main steps to creating a Keras model: specification and compilation. Let's consider the folling specification:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pZMBpOPKP0k4", "outputId": "45877722-2a2e-4711-eec1-cb170b3ba782" }, "source": [ "model = keras.Sequential(\n", " [\n", " keras.Input(shape=input_shape),\n", " layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"), # Convolutions\n", " layers.MaxPooling2D(pool_size=(2, 2)), # Pooling\n", " layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n", " layers.MaxPooling2D(pool_size=(2, 2)),\n", " layers.Flatten(),\n", " layers.Dropout(0.5),\n", " layers.Dense(num_classes, activation=\"softmax\"),\n", " ]\n", ")\n", "\n", "model.summary()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential_38\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " conv2d (Conv2D) (None, 26, 26, 32) 320 \n", " \n", " max_pooling2d (MaxPooling2D (None, 13, 13, 32) 0 \n", " ) \n", " \n", " conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n", " \n", " max_pooling2d_1 (MaxPooling (None, 5, 5, 64) 0 \n", " 2D) \n", " \n", " flatten (Flatten) (None, 1600) 0 \n", " \n", " dropout_91 (Dropout) (None, 1600) 0 \n", " \n", " dense_130 (Dense) (None, 10) 16010 \n", " \n", "=================================================================\n", "Total params: 34,826\n", "Trainable params: 34,826\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 865 }, "id": "6rMdmhaU7oxq", "outputId": "17120b75-26e5-40e7-9805-877f75aa5305" }, "source": [ "# Let's inspect the scheme of the model next:\n", "\n", "keras.utils.plot_model(model, show_shapes=True)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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evDlr1iwkx5Q5O3fuXLt2bU5OjpOTk5KaDQAYWMNwlJ10qp2r5unTp42NjcXFxREREQcOHLh8+bKpqSlCKCsrKzw8/JNPPqmvr+dyuX/5y19IkjQyMlJSM3pSnyl88PsW+E3S9957z9HR8bvvvtu0aRNBEFlZWQEBAThhWv+nzKEsXLiQ+s/B8EYQhKqbAAbb/PnzVd0EBYy4gCSTUueqodPpJiYmXl5ednZ2jo6O8fHxycnJCCE9Pb39+/dTm7148eLYsWPdstyrllJPy5kzZ3bv3l1WViYQCCSDIkEQq1atCgsLu3Tp0vvvv/+vf/2LGgpPTZmzZcsWansul9uHvYeEhPD5/P4dgbrDieNCQ0NV3RAwqPB1H0IgIP0fgzZXDY/H09DQ6G3GlJs3byKEpk+fruxmyEkZp+XKlSu//PJLaGjokydPfH19582b9913340aNSolJeXrr7+mNlu6dOmmTZsOHjxoZWXF4XDwMBAkMWVOSEhIP1vC5/MXLFjQz0rUXHZ2NkJo2B8m6AZf9yFkpDxDkpOS5qqpq6tbtGiRZAme1Ku3GVPS09Pt7Ow8PT37v+sBoYzT8ssvv+DEz6WlpSKRaM2aNfb29kwms1u3koGBwcKFC/Py8vbs2fPZZ59R5X2bMgcAoM4gIA3YXDVSdsFisS5cuHD58mXcJVVcXPzpp5+yWCwqXf8777zz+PHjzs7OR48ebdiw4eLFixkZGfi5jqoo77SIRKJXr15RMxHgKd0uXrzY3t5eXl5OjS+nrF69+vXr1/n5+ZIvO0uZMgcAMFSpeJSfIuQZwpiSkoJfkdHR0ZkzZ05qairOaTh69OiKiooDBw5wOByEkI2Nzf3790mSDAoKotPpFhYWmpqaHA5n7ty5FRUVVG11dXXTp09nMpl2dnbr1q376quvEEI8Hg8PgL59+7aNjY22tva777778uVL6Q2bM2eOnZ2drq4ug8FwcHAICAgoLS2l1n7wwQf6+vqampoGBgbe3t54+PJAnZOioqJx48bht53Mzc23b98+aKflm2++cXBw6O27l5ubiysMDw83NDTU19f39/fHL5A5ODhQo8xJknzrrbciIiK6Hdfr16/Dw8Otra01NTVNTEz8/PzKysoSEhLwG11WVlbyTJqAYNg3GL6G3HUnSLXM/PZG/v7+aKB7RVetWpWdnV1XVzeAdQ4mZZwTpH6nxdvbe9++fXZ2dgNeM0EQx48fH/YPV5T0PQFqbshdd+iyG0pz1QwmlZ8Wqrvvzp07+G5Mte0BACgbBKQB8McffxC9G/Apv0aI8PDw8vLy+/fvL1u2LDY2VtXNGRFWrVpFfW+7zRVy8eLFiIgIyflElixZIrmBl5cXm83W0NAYN27c7du3B7fh/yMmJsbZ2ZnD4TAYDB6P9/XXX3ebV/Do0aM47YiNjc2yZcvkz0CvvJqxrq6upKSkblmJT58+nZCQIPlfw7y8POoCGRsbK7SLoUHVfYYKGPD+0IiICDxwwNbWNjs7ewBrHjTK6CNWk9OyefNmGo1mZWVF5QpSBgTPkCTg2dYLCgru3bvX3t5OlUdFRc2ePVsgEOBFBwcH/NZ2fn6+5McLCgp8fHwGtuUK8fT0TE1NraurEwgEx48fp9PpM2fOpNZmZWUhhBISEvDL6fb29q6uriKRSLU1kyR5//79qVOnIoQmTpzYbVVycrKnpyeV8aurq6uqqurKlSuzZs0allOYj+iANAzAOeknZQek1tZWPp+v8qrkD0gWFhbdCnfs2OHo6NjW1kaVODg4HDlyhEajWVhYNDY2UuUqD0je3t6dnZ3UIn40SI2OmT59+qhRo7q6uvAiHj5z7do11dZcUlIyb968zMxMV1fXngGJJMng4GA+n98tvH355ZfDMiBBlx0ASjSAk3GoZF6PBw8eREZGbtu2TXLyFISQu7t7SEjIs2fPNmzYMMhNkiI/Px9nlsJwp1ZraytefPr0KZfLpV50w28BdntpYfBrnjhxYk5OTmBgIIPBeOMG0dHRJSUlOKXLsAcBCQAZSJJMTEzEqWYNDAzmzp1LJc1TaDKOgZ3X49y5cxwOZ/v27Uo99r1795IkOWfOnJ6r4uLiHB0dDx48ePHixTd+Vsp5kz4BCuplbhFFPXv2TFtbmxoOY29vLxnR8WMee3t7taq5JwMDA09Pz+TkZHLojIjuO9XeoClkyN1+DgI4J/2E5Oiyi4qK0tLSOnz4cGNj4507dyZNmmRsbEy9eabQZBwDOK9Hfn4+m82OiYmR5zD73GVnb2/v7OzcbTMHB4fKykqSJH/66ScajWZra9vc3Ez26LKTft6kT4DS29wi8mtpaWGz2cHBwVRJYWEhnU7fu3evQCD47bffxo4d++GHHypUp1Jr/stf/vLGLjuSJCMiIhBCxcXFVAl02QEwErW1tSUmJs6bN2/x4sV6enouLi779++vra09cOBA3yocqHk9vL29BQJBZGRk35ohj5aWlsrKSimvNvP5/NDQ0EePHm3cuLHbKjnP2xsnQJE5t4g84uPjuVxuXFwcVeLp6RkeHh4cHMzhcMaPHy8UCg8ePKhQncquuTd4xs7S0tIBrFM9QUACQJqysrLm5ubJkydTJW5ublpaWj1THPXBYM7r0QfV1dUkSeKkHr2Ji4sbM2ZMamrqtWvXJMsVPW+SE6D0f26R3NzcEydOnD9/ns1mU4WbN28+cODApUuXmpubHz586O7uzufz8Xye6lCzFPgSvHr1aqAqVFsQkACQprGxESGkq6srWaivry8UCgekfqXO69FP7e3tCKHeHrZjTCbz0KFDBEEsX768ra2NKu/PeaPmFqHeuXn8+DE1gkCmrKysnTt3FhYW2traUoUvXrxISEj4/PPP33vvPRaLZWdnl56e/vz5c9wvqvKapcPZsPDlGN4gIAEgjb6+PkKo28/oQE3GMWjTnfQN/h2UmbODz+eHhYWVl5dLvr/cn/NGzS0i+XTh+vXr8rQ5JSUlMzPz8uXL3aYTw/n1JQs5HI6hoWFvU8AMZs0ydXR0oD8vx/AG8yEBIM348eN1dXVv3bpFldy4caOjo+Ptt9/Gi/2ZjENJ050MFFNTU4IgmpqaZG4ZGxubn59fXFyMc7cjOc6bFH2bW4QkyY0bNzY0NOTl5Wlqdv9lw4FQMh+8UCisr6/vbQqYwalZTvgSmJmZDVSFagvukACQhslkrl+/Pjc3NzMzUyAQlJaWrl69msvlBgUF4Q0UnYxjoOb1KCgoUPawbx0dHXt7+6qqKplb4o47yTd1ZJ436bX1NrdIQECAmZnZG1MT3b17d9euXenp6XQ6XTJ31549exBCdnZ206dPT09Pv3LlSltb29OnT3FLVqxYgT+ukprlhC+Bi4tLn2sYKiAgASDD1q1b4+PjY2JijI2NPT09bW1tqcmcEEJr1qyZPn36xx9/PGbMmNjYWNyvQj3TXr16tampqbOz86xZs+rr6xFC7e3tLi4u2traHh4ejo6OP/zwA/WQRtGqBoG3t3dZWRn1cOj777/n8XgVFRVubm7r1q2T3HLKlCnU/F6YlPOWlpaGZ9eeMGHCw4cP09PT169fjxCaOXNmeXk5Qig5OTk0NDQhIcHIyIjL5YaEhDQ0NCCEOjo6qqurT5061bOppNTXdAiCyM7ODggIWLFihYGBgbOz85MnT3Jycjw8PPAGKqkZIVRUVPTuu++OGjXqxo0bv/76K5fLnTp16pUrVyS3uXnzpoWFxYQJE6Q0Y5gY/JHmfTbkxtQPAjgn/YQGN5cdThY3aLuj9Pk9pPLyck1NTXlmlhocYrHYw8MjIyNj5NRcW1vLZDL37NkjWQjvIQEABoDK5/WQrq2t7fz58+Xl5fhBOo/Hi4mJiYmJ6ZbcWiXEYnFeXp5QKBzwDPrqXHN0dLSrq2twcDBCiCTJ58+fX7t27cGDBwPaTHUBAQkA8L/q6+tnzpzp6Oi4fPlyXBIREeHv7x8QECDP6AalKiwszMnJKSgokP5q1HCqOTExsaSk5OzZs3Q6HSF06tQpCwsLDw+PM2fODGw71QQEJAAGyaZNmw4dOtTU1GRnZ3fy5ElVN+cN9u/fT3WeZGZmUuXbt28PDg7esWOHCtuGEJoxY8aRI0eodH/DvuZTp069fv26sLDQwMAAl8ydO5e6QDjJ4TADw74BGCTx8fHx8fGqbkUfeXl5eXl5qboVI4uPj4+Pj4+qWzGo4A4JAACAWoCABAAAQC1AQAIAAKAWICABAABQC0NsUENVVdWJEydU3Qo1gnOKwDnpDzmzdg5p8D0ZmaqqqtQ2de+bqeBl3L6aP3++qs8WAAAMJUMrUwNBjoR52gFQpgULFiC4/wCg3+AZEgAAALUAAQkAAIBagIAEAABALUBAAgAAoBYgIAEAAFALEJAAAACoBQhIAAAA1AIEJAAAAGoBAhIAAAC1AAEJAACAWoCABAAAQC1AQAIAAKAWICABAABQCxCQAAAAqAUISAAAANQCBCQAAABqAQISAAAAtQABCQAAgFqAgAQAAEAtQEACAACgFiAgAQAAUAsQkAAAAKgFCEgAAADUAgQkAAAAagECEgAAALUAAQkAAIBagIAEAABALUBAAgAAoBYgIAEAAFALEJAAAACoBQhIAAAA1AIEJAAAAGoBAhIAAAC1QJAkqeo2ADDEHDlyJCMjo6urCy9WVlYihOzs7PAijUZbsWJFYGCgytoHwNAEAQkAhd25c2fixIlSNvj1118nTJgwaO0BYHiAgARAXzg5Od27d++Nq3g8Xnl5+SC3B4BhAJ4hAdAXS5YsodPpPcvpdPqyZcsGvz0ADANwhwRAXzx8+JDH473xz6e8vJzH4w1+kwAY6uAOCYC+sLe3nzRpEkEQkoUEQUyePBmiEQB9AwEJgD765JNPNDQ0JEs0NDQ++eQTVbUHgKEOuuwA6KPq6moul0sN/kYI0Wi058+fm5mZqbBVAAxdcIcEQB+Zmpp6enpSN0kaGhrTpk2DaARAn0FAAqDvlixZItnHsGTJEhU2BoChDrrsAOg7gUBgYmLS0dGBEKLT6dXV1fr6+qpuFABDFdwhAdB3HA5n5syZmpqampqas2bNgmgEQH9AQAKgXxYvXiwWi8ViMSSvA6CfoMsOgH5pb283NjYmSbK2tlZbW1vVzQFgKCOHqePHj6v61AIAwMA7fvy4qn9flUVT1edWuUZOWLp+/XpycvLIOV7pFi5cGBISwufzB2d3JSUlBEFIz/+tDElJSQih0NDQQd4vUKGFCxequglKNMwD0oIFC1TdhMGTnJw8oo5XioULF/L5/EE7G/PmzUMIaWoO9l9TdnY2GmFfcgABCQAgzeCHIgCGJRhlBwAAQC1AQAIAAKAWICABAABQCxCQAAAAqAUISAD8j7Nnz+rp6f373/9WdUMGycWLFyMiInJycuzt7QmCIAiiW3JYLy8vNputoaExbty427dvq6SRMTExzs7OHA6HwWDweLyvv/66ublZcoOjR4+6ubmx2WwbG5tly5a9fPlS5TVjXV1dSUlJ7u7ukoWnT59OSEgQi8UKVTWCqPpFKGXBb+SouhWDZ6Qdr3SoTy8P5ufnczic06dPK6NJyjB//vz58+f37bNRUVGzZ88WCAR40cHBwcjICCGUn58vuVlBQYGPj09/G9oPnp6eqampdXV1AoHg+PHjdDp95syZ1NqsrCyEUEJCQmNjY3Fxsb29vaurq0gkUm3NJEnev39/6tSpCKGJEyd2W5WcnOzp6dnQ0CBnVd307bs9VAzbn7CR9gM90o5XOjX/o21tbeXz+f2vp88BaceOHY6Ojm1tbVSJg4PDkSNHaDSahYVFY2MjVa7ygOTt7d3Z2Ukt4peunjx5ghenT58+atSorq4uvLhv3z6E0LVr11Rbc0lJybx58zIzM11dXXsGJJIkg4OD+Xy+/OFNkpp/t/sJuuwAGGwZGRnV1dWq2vuDBw8iIyO3bdvGZDIly93d3UNCQp49e7ZhwwZVta2n/Px8yXnijY2NEUKtrZU2X9QAACAASURBVK148enTp1wulyAIvGhlZYUQevz4sWprnjhxYk5OTmBgIIPBeOMG0dHRJSUlycnJ8tQ2okBAAgAhhK5du2ZtbU0QBP6/cFpaGovF0tHROXXq1EcffcThcCwtLY8dO4Y33rt3L5PJNDU1XbVqFZfLZTKZ7u7uN27cwGuDg4O1tLTMzc3x4hdffMFisQiCqK2tRQiFhISsX7++oqKCIAgej4cQOnfuHIfD2b59++Ac6d69e0mSnDNnTs9VcXFxjo6OBw8evHjx4hs/S5JkYmLi2LFjGQyGgYHB3Llz//jjD7xK+hlDCInF4qioKGtra21t7QkTJvQtzdWzZ8+0tbXt7Ozwor29vWRox4957O3t1armngwMDDw9PZOTk0nIbd2Niu/QlGakdWGNtOOVDvWpW+Pp06cIoZSUFLy4efNmhNClS5eampqqq6s9PDxYLFZHRwdeGxQUxGKx7t69297eXlZWhp9+Ux0+gYGBZmZmVM27d+9GCNXU1OBFPz8/BwcHam1+fj6bzY6JiVG0wX3rsrO3t3d2du5W6ODgUFlZSZLkTz/9RKPRbG1tm5ubyR5ddlFRUVpaWocPH25sbLxz586kSZOMjY1fvnyJ10o/Yxs2bGAwGCdPnmxoaNi0aRONRrt586ZCLW9paWGz2cHBwVRJYWEhnU7fu3evQCD47bffxo4d++GHHyp6QpRX81/+8pc3dtmRJBkREYEQKi4uVrTOvn23hwq4QwJAGnd3dw6HY2JiEhAQ0NLS8uTJE2qVpqYmvldwdnZOS0sTCoWHDh3qwy68vb0FAkFkZOTAtbpXLS0tlZWVDg4OvW3A5/NDQ0MfPXq0cePGbqva2toSExPnzZu3ePFiPT09FxeX/fv319bWHjhwQHKzN56x9vb2tLQ0X19fPz8/fX39LVu20Ol0RU9XfHw8l8uNi4ujSjw9PcPDw4ODgzkczvjx44VC4cGDBxWqU9k192b06NEIodLS0gGscxiAgASAXLS0tBBCIpHojWsnT56so6ND9V+prerqapIkdXR0pGwTFxc3ZsyY1NTUa9euSZaXlZU1NzdPnjyZKnFzc9PS0qL6KruRPGP37t1rbW0dP348XqWtrW1ubq7Q6crNzT1x4sT58+fZbDZVuHnz5gMHDly6dKm5ufnhw4fu7u58Ph/f6apDzVLgS/Dq1auBqnB4gIAEwMBgMBg1NTWqboUM7e3tCKHeHrZjTCbz0KFDBEEsX768ra2NKm9sbEQI6erqSm6sr68vFApl7relpQUhtGXLFuJPjx8/pkYQyJSVlbVz587CwkJbW1uq8MWLFwkJCZ9//vl7773HYrHs7OzS09OfP3+OO0hVXrN0eC5HfDkABQISAANAJBI1NjZaWlqquiEy4N9BmS9m8vn8sLCw8vLy2NhYqlBfXx8h1C38yHnUJiYmCKGkpCTJBwbXr1+Xp80pKSmZmZmXL18eNWqUZHl5eblYLJYs5HA4hoaGZWVl8lSr1Jpl6ujoQH9eDkCBtPkADIDCwkKSJKdMmYIXNTU1e+vcUy1TU1OCIJqammRuGRsbm5+fX1xcbG1tjUvGjx+vq6t769YtapsbN250dHS8/fbbMmuzsrJiMpklJSUKtZYkyY0bNzY0NOTl5fWc4wMHwhcvXlAlQqGwvr4eD9FWVc1ywpfAzMxsoCocHuAOCYA+6urqamho6OzsvHPnTkhIiLW19dKlS/EqHo9XX1+fl5cnEolqamq6vb9iaGj4/PnzR48eCYVCkUhUUFAwaMO+dXR07O3tq6qqZG6JO+4k39RhMpnr16/Pzc3NzMwUCASlpaWrV6/mcrlBQUHy1LZs2bJjx46lpaUJBAKxWFxVVYV/8QMCAszMzN6Ymuju3bu7du1KT0+n0+mEhD179iCE7Ozspk+fnp6efuXKlba2tqdPn+KWrFixAn9cJTXLCV8CFxeXPtcwPKliaN9gGGnDoEfa8UqHFB8am5KSgt8c0tHRmTNnTmpqKn7sPHr06IqKigMHDnA4HISQjY3N/fv3SZIMCgqi0+kWFhaampocDmfu3LkVFRVUbXV1ddOnT2cymXZ2duvWrfvqq68QQjweD48Lv337to2Njba29rvvvvvy5cuzZ8+y2ey4uDhFD7Nvw76Dg4PpdHpraytezM3NxYPujI2N165d223jr776SnLYd1dX1+7du0ePHk2n0w0MDHx9fe/du4dXyTxjr1+/Dg8Pt7a21tTUNDEx8fPzKysrI0nS19cXIRQVFdWzqb0NQtu9ezfeoLa2NiQkhMfjMRgMXV3dqVOnfv/999THVVIzSZLXr1+fOnUql8vFdZqbm7u7u//3v/+V3Mbb29vCwoLKBCG/Pny3h5Bh+xM20n6gR9rxSjcIf7RBQUGGhoZK3YVMfQtI5eXlmpqahw8fVkaT+kAsFnt4eGRkZIycmmtra5lM5p49e/rw2eEdkKDLDoA+GqI5m3k8XkxMTExMTLfk1iohFovz8vKEQmFAQMDIqTk6OtrV1TU4OHhgGzYMQEAabDKT3lNWrlzJZrMJglD0UbBM9+7dW7du3bhx49hstqampp6enqOjo7e3t5yjnvpDyuFLzoOAaWlpmZqaTps2bffu3Q0NDcpu28gRERHh7+8fEBAgz+gGpSosLMzJySkoKJD+atRwqjkxMbGkpOTs2bN0On1gGzYcqPoWTVnUtgtLetL7bnAqMHnyi8h/vAcPHqTT6X/961/PnTvX0NDQ3t5eUVGRlZXl7u7+7bffKnAkfSLz8B0cHPT09EiSxEMGfvjhh6VLlxIEweVy5c80g5TcrREREYHf+rS1tc3OzlbejqTrz/QTJEmeP38+PDx8ANsDZMrLy4uPj5fMMq4oZX+3VUsdf7IHhNoGJOlJ77sZ8IB0/fp1DQ2N9957r2fq+3PnzlFp3JRH5uFTAUlSdnY2jUYzNTWVnBlBiuH9R0vpZ0ACQ9Hw/m5Dl91gk570vhsq+/1AiYuLE4vFO3bs6PnuxYcffrh27dqB3V1PCh0+Zf78+UuXLq2urt6/f79y2wcAUB0ISOjw4cOTJ09mMpksFsvW1ha/mk72Nc3+2LFjCYKg0Whvv/02/p39+uuv9fT0mEzmP/7xj55775b0niTJ3bt3jxkzhsFg6Onp4eHCA6Wjo+PSpUtGRkbvvPOO9C1VdfhS4Fd8CgoKFDhgAMDQouI7NKWRswsrKSkJIbRjx466urr6+vpvv/02MDCQ7Eea/c7OTltbW2tra8mOqdDQ0G5JU7CeSe83b95MEMTf/va3hoaG1tbW1NRUNHBddvfv30cITZkyRWZtqjp8spcuO5IkBQIBQsjKykpm48nh3q1BgS67EWh4f7dHdEDq6OjQ19efPn06VdLZ2ZmcnNza2qqrqxsQEECV//zzzwghasYa/ItMzQCNw8aDBw/wIg5yJ06cwIstLS3W1tZNTU09G7B582ZHR0eBQIAXW1tbdXR0PvjgA2qDgX2GhJO+vP/++9I3U9XhY70FJJIkCYLQ19eX3nhseP/RUiAgjUDD+7s9onPZ3blzp7Gx8cMPP6RKNDQ0vvzyy1u3bvU5zT5CaOXKldHR0cnJyf7+/gihzMzMuXPn4rfWJeGk9xcuXKCS3j948KC1tXXGjBkDd4j/B87TLPOBTX9mGUD9OHzpWlpaSJLsWU9vBmEIu8rh9DMnTpxQdUMAGBgjOiDhXiCcw1hSf9Ls4w9+/vnnu3fv/vnnn995551vvvnm5MmT3bbJyspKTEwsLCyUzCiMf19wXmRlsLW1ZTKZuONOClUdvnS42U5OTnJun5ycnJycLOfGQ9rChQtV3QQABsaIHtSAfw1ra2u7lfcnzT6G04UlJSVduXLFysqq2wSdvSW9ZzKZCKHXr18reBzyYjAYH374YW1t7Y8//thzbX19/cqVK5HqDl+6c+fOIYQ++ugjObcfxt0aFOiyG4Hk/5MZikZ0QLK1tTU0NLxw4UK38v6k2ccsLS0XLFhw8uTJyMjIkJAQqpwkyfDw8NLS0ry8vG63IHi/NBrtv//9b5+ORi7R0dEMBiMsLExy4jXst99+w2PBVXX4Urx8+TIpKcnS0nL58uXyfwoAMLSM6IDEYDA2bdp05cqV4ODgZ8+edXV1CYXCu3fv9ifNPmX9+vWdnZ0NDQ3vvfceVSg96T1OgXzy5MmMjAyBQHDnzp0DBw4M7CG7uroeOXLkt99+8/DwOHv2bFNTk0gkqqysTE9PX7FiBc5loqrDp5Ak2dzcjBMh19TUHD9+fOrUqRoaGnl5efI/QwIADD2qvf1UHvkzNezbt8/FxYXJZDKZzLfeeis1NZXsX5p9yvTp0w8ePChZIjPpvVAoXLlypZGRka6u7rvvvhsVFYUQsrS0/PXXXwfqeEmSfPLkyYYNG1xcXHR1dTU0NPT19d96660VK1b8+OOPeAOVHP7p06cnTJigo6OjpaVFo9EQQnhY3TvvvBMTE1NXVyfn0ZHDfSQSBbrsRqDh/d0myGHaKXnixImFCxcO16PraaQdr3QEQRw/fhznJRrG8DjG7OxsVTcEDJ7h/d0e0V12AAAA1AcEJAAAAGoBAhIAI8XFixcjIiIk551asmSJ5AZeXl5sNltDQ2PcuHG3b99WSSNlThgmEoni4+N5PJ6Wlpa+vv748eMfPXqk2poTEhKcnJy0tbVZLJaTk1NkZCR+x1Hmfk+fPp2QkDBEZ3pUChU/w1IatZ1+QklG2vFKh4b1g1+KQoMaoqKiZs+eTSVqcnBwMDIyQgjl5+dLblZQUODj4zPADVWEzBmzfH19x4wZU1RUJBKJnj9/PmfOnNLSUtXW7O3tvWfPnurqaqFQeOLECTqdLpkATPp+k5OTPT09Gxoa5NkROdy/28P2J2yk/UCPtOOVTtl/tK2trXw+X+VVyR+QduzY4ejoSKUfJEnSwcHhyJEjNBrNwsJCcpYplQck6TNmHTt2jCCIO3fuqFXNvr6+kucWDzZ5/vy5PPslSTI4OJjP5/ecouyNhndAgi47ABSWkZFRXV2tblX15sGDB5GRkdu2bcOpQCju7u4hISHPnj3bsGGDUhugEOkzZn3zzTeTJk1ycXFRq5pzc3Mlz62FhQVCiOqXkzkHWHR0dElJyQjJdCUdBCQwQpG9z/kUHByspaVlbm6OF7/44gsWi0UQBM4yFRISsn79+oqKCoIgeDze3r17mUymqanpqlWruFwuk8l0d3enEtEqVBVC6Ny5cxwOZ/v27QN4pHv37iVJcs6cOT1XxcXFOTo6Hjx48OLFi4qeJekzYyGExGJxVFSUtbW1trb2hAkT8E28oiRnzOro6CgqKnJ1de1DPYNZc3l5ub6+vo2Njcz9YgYGBp6ensnJySS8tqHaGzTlGWldWCPteKVDcnRrSJ/zKTAw0MzMjNp49+7dCKGamhq86Ofn5+DgQK0NCgpisVh3795tb28vKytzc3Njs9lUn4xCVeXn57PZbGqmD+nk7LKzt7d3dnbuVujg4FBZWUmS5E8//USj0WxtbZubm8keXXZ9nhmLJMkNGzYwGIyTJ082NDRs2rSJRqPdvHlTnuOidJsxq7KyEiHk6uo6bdo0c3NzBoPh5OS0b98+nNRD5TV3dHRUVVWlpKQwGIzDhw/Ls19KREQEkm+iGXm+20MX3CGBkaitrS0xMXHevHmLFy/W09NzcXHZv39/bW1tn3M1aWpq4tsIZ2fntLQ0oVB46NChPtTj7e0tEAgiIyP71oyeWlpaKisru+W3lcTn80NDQx89erRx48Zuq+Q8S+7u7hwOx8TEJCAgoKWl5cmTJwih9vb2tLQ0X19fPz8/fX39LVu20Ol0Rc9JfHw8l8uNi4vDi7gTzMTEZPv27WVlZa9evZo7d+7atWuPHj2qULVKqtnKysrS0jI6OnrXrl29pWDvtl/K6NGjEUK9pTIZOSAggZFI0TmfFDJ58mQdHR2qa0u1qqurSZLE2Z56ExcXN2bMmNTU1GvXrkmW92dmrHv37rW2to4fPx6v0tbWNjc3V+ic4Bmzzp8/T82YxWAwEELjxo1zd3c3NDTU09Pbtm2bnp6eov+NUFLNT58+ra6uPnr06D//+c+33nqr56PBnvul4Av06tUrhQ5k+IGABEaifs75JBODwaipqRmQqvqpvb0d/fmD2xsmk3no0CGCIJYvXy6ZBr4/Z6mlpQUhtGXLFiqF7uPHj2VODknJysrauXNnYWGhra0tVcjlctH/nS9GS0vLxsamoqJCzmqVWjOdTjcxMfHy8srKyiorK4uPj5e5X4q2tjb682KNZBCQwEjU/zmfpBCJRANVVf/hXzqZr17y+fywsLDy8vLY2FiqsD9nCc8zmZSUJPmEQM5pfHubMUtXV3f06NF3796VLOzs7NTT05OnWqXWLInH42loaJSVlcncL6WjowP9ebFGMghIYCSSOeeTpqYmNSm7ogoLC0mSnDJlSv+r6j9TU1OCIJqammRuGRsb6+TkVFxcTJX0Z2YsKysrJpNZUlKiUGtJWTNmLVy4sLi4+OHDh3ixtbX18ePH8ozVVl7NdXV1ixYtkiwpLy8Xi8VWVlby7BfDF8jMzEzm7oY3CEhgJJI55xOPx6uvr8/LyxOJRDU1NY8fP5b8uKGh4fPnzx89eiQUCnGw6erqamho6OzsvHPnTkhIiLW19dKlS/tQVUFBwcAO+9bR0bG3t6+qqpK5Je64k3xjpj8zYzGZzGXLlh07diwtLU0gEIjF4qqqqhcvXiCEAgICzMzM3piaSOaMWWFhYTY2NkuXLn3y5EldXV14eHhbWxs1HEMlNbNYrAsXLly+fFkgEIhEouLi4k8//ZTFYoWFhcmzXwxfoL69BTWcQEACI9TWrVvj4+NjYmKMjY09PT1tbW0LCwtZLBZeu2bNmunTp3/88cdjxoyJjY3FfSl8Pv/p06cIodWrV5uamjo7O8+aNau+vh4h1N7e7uLioq2t7eHh4ejo+MMPP1CPbRStasB5e3uXlZVRD4e+//57Ho9XUVHh5ua2bt06yS2nTJmCf0blOUtpaWlJSUkIoQkTJjx8+DA9PX39+vUIoZkzZ5aXlyOEkpOTQ0NDExISjIyMuFxuSEhIQ0MDQqijo6O6uvrUqVM9m0rKehHHwMDg6tWrlpaWrq6uFhYWP//885kzZ6j3h1RSM5PJnDp16sqVKy0sLNhstr+/v62tbVFRER7QIXO/2M2bNy0sLCZMmCDPxsPZYI8zHywj7b2ckXa80qHBfVcjKCjI0NBw0HZHkfM9pPLyck1Nzd7ejBl8YrHYw8MjIyMDasZqa2uZTOaePXvk2XiQv9uDDO6QABgA6pywmcfjxcTExMTEdEturRJisTgvL08oFAYEBEDNWHR0tKura3BwsDIqH1ogIAEw/EVERPj7+wcEBMgzukGpCgsLc3JyCgoKpL8aNUJqRgglJiaWlJScPXuWTqcPeOVDDgQkAPpl06ZNhw4dampqsrOzO3nypKqb06vt27cHBwfv2LFDtc2YMWPGkSNHqOR+I7zmU6dOvX79urCw0MDAYMArH4o0Vd0AAIa2+Pj4bq9Aqi0vLy8vLy9VtwL8Lx8fHx8fH1W3Qo3AHRIAAAC1AAEJAACAWoCABAAAQC1AQAIAAKAWhvmgBjy5/UiAU4+MnOOVKSkpKTs7W9WtUK6ioiIEFx0MIwQ5TCfNvX79emJioqpbAUYEnJD0rbfeUnVDwIgQFhbG5/NV3QqlGLYBCYBBs2DBAoTQiRMnVN0QAIY2eIYEAABALUBAAgAAoBYgIAEAAFALEJAAAACoBQhIAAAA1AIEJAAAAGoBAhIAAAC1AAEJAACAWoCABAAAQC1AQAIAAKAWICABAABQCxCQAAAAqAUISAAAANQCBCQAAABqAQISAAAAtQABCQAAgFqAgAQAAEAtQEACAACgFiAgAQAAUAsQkAAAAKgFCEgAAADUAgQkAAAAagECEgAAALUAAQkAAIBagIAEAABALUBAAgAAoBYgIAEAAFALEJAAAACoBQhIAAAA1AIEJAAAAGoBAhIAAAC1AAEJAACAWtBUdQMAGHpaW1tfv35NLXZ0dCCEGhoaqBIGg6Gjo6OClgEwlBEkSaq6DQAMMWlpaV988YWUDVJTU9esWTNo7QFgeICABIDCampquFyuWCx+41oNDY0XL16YmJgMcqsAGOrgGRIACjMxMZkxY4aGhkbPVRoaGu+//z5EIwD6AAISAH2xePHiN/YukCS5ePHiwW8PAMMAdNkB0BdCodDExERyaAOmpaVVU1PD4XBU0ioAhjS4QwKgL9hs9uzZs+l0umShpqamj48PRCMA+gYCEgB9FBgY2NnZKVkiFosDAwNV1R4AhjrosgOgjzo6OoyNjYVCIVWiq6tbW1vLYDBU2CoAhi64QwKgj7S0tPz9/bW0tPAinU5fuHAhRCMA+gwCEgB9t2jRIpymASEkEokWLVqk2vYAMKRBlx0AfdfV1WVubl5TU4MQMjY2fvny5RtfTgIAyAPukADoOxqNtmjRIi0tLTqdHhgYCNEIgP6AgARAv3z88ccdHR3QXwdA/8mb7fv69etPnz5ValMAGIpIkjQyMkIIVVZWPnr0SNXNAUDtWFlZ8fl8uTYl5TN//nwltxkAAMAwNH/+fDkDjQLzIc2fPz87O1t5jQYD68SJEwsXLoRBK4Pg7t27CCFnZ+c+1+Dv748QGvZ/X/CdHIHwd1tOMEEfAP3Vn1AEAKDAoAYAAABqAQISAAAAtQABCQAAgFqAgAQAAEAtQEACAACgFiAgATBUnT17Vk9P79///reqG6IsFy9ejIiIyMnJsbe3JwiCIIglS5ZIbuDl5cVmszU0NMaNG3f79m2VNDImJsbZ2ZnD4TAYDB6P9/XXXzc3N0tuIBKJ4uPjeTyelpaWvr7++PHj5XyBWnk1JyQkODk5aWtrs1gsJyenyMhIgUAgz35Pnz6dkJAgFovl2UsfQEACYKga3i/0bN26de/evZs2bfLz83v48KGDg4ORkVFmZuaZM2eobS5cuJCdnT179uyysrJJkyappJ2XL19eu3bto0ePamtr4+Pjk5OTu715s3Dhwn/9619HjhxpbW39/fffHRwcusWVwa/56tWrn3322ZMnT169ehUbG5uQkCCZ+kDKfufMmcNkMmfMmNHY2CjPjhQmf6YG+d+2Berg+PHj8l9foFpq/vfV2trK5/P7X4/838kdO3Y4Ojq2tbVRJQ4ODkeOHKHRaBYWFo2NjVR5QUGBj49P/9vWZ97e3p2dndTiggULEEJPnjzBi8eOHSMI4s6dO2pVs6+vr+S5xfHm+fPn8uyXJMng4GA+ny8SieTZl0LfbbhDAgDIkJGRUV1dPWi7e/DgQWRk5LZt25hMpmS5u7t7SEjIs2fPNmzYMGiNkSk/P18yy7uxsTFCqLW1FS9+8803kyZNcnFxUauac3NzJc+thYUFQoi6u5K+X4RQdHR0SUlJcnJyH3YtHQQkAIaka9euWVtbEwSxb98+hFBaWhqLxdLR0Tl16tRHH33E4XAsLS2PHTuGN967dy+TyTQ1NV21ahWXy2Uyme7u7jdu3MBrg4ODtbS0zM3N8eIXX3zBYrEIgqitrUUIhYSErF+/vqKigiAIHo+HEDp37hyHw9m+fbuSDm3v3r0kSc6ZM6fnqri4OEdHx4MHD168ePGNnyVJMjExcezYsQwGw8DAYO7cuX/88QdeJf0UIYTEYnFUVJS1tbW2tvaECRPw/Zyinj17pq2tbWdnhxDq6OgoKipydXXtQz2DWXN5ebm+vr6NjY3M/WIGBgaenp7JycnkgHcay3knpeZdCqAn6LIbQvr294UT8KekpODFzZs3I4QuXbrU1NRUXV3t4eHBYrE6Ojrw2qCgIBaLdffu3fb29rKyMjc3NzabTfXDBAYGmpmZUTXv3r0bIVRTU4MX/fz8HBwcqLX5+flsNjsmJkbRBsv5nbS3t3d2du5W6ODgUFlZSZLkTz/9RKPRbG1tm5ubyR5ddlFRUVpaWocPH25sbLxz586kSZPwxIl4rfRTtGHDBgaDcfLkyYaGhk2bNtFotJs3byp0gC0tLWw2Ozg4GC9WVlYihFxdXadNm2Zubs5gMJycnPbt29fV1aVQtUqquaOjo6qqKiUlhcFgHD58WJ79UiIiIhBCxcXFMvcCXXYAjFzu7u4cDsfExCQgIKClpeXJkyfUKk1NTXzr4OzsnJaWJhQKDx061IddeHt7CwSCyMjIgWv1/2ppaamsrHRwcOhtAz6fHxoa+ujRo40bN3Zb1dbWlpiYOG/evMWLF+vp6bm4uOzfv7+2tvbAgQOSm73xFLW3t6elpfn6+vr5+enr62/ZsoVOpyt6fuLj47lcblxcHF7EnWAmJibbt28vKyt79erV3Llz165de/ToUYWqVVLNVlZWlpaW0dHRu3btWrhwoTz7pYwePRohVFpaquiBSAcBCYDhSUtLCyEkEoneuHby5Mk6OjpUd5b6qK6uJklSR0dHyjZxcXFjxoxJTU29du2aZHlZWVlzc/PkyZOpEjc3Ny0tLapzshvJU3Tv3r3W1tbx48fjVdra2ubm5gqdn9zc3BMnTpw/f57NZuMSBoOBEBo3bpy7u7uhoaGent62bdv09PS6BUhV1fz06dPq6uqjR4/+85//fOutt3o+Juy5Xwq+QK9evVLoQGSCgATACMVgMGpqalTdiu7a29vRnz+4vWEymYcOHSIIYvny5W1tbVQ5Housq6srubG+vr5QKJS535aWFoTQli1biD89fvxY8km+dFlZWTt37iwsLLS1taUKuVwuQgg/isO0tLRsbGwqKirkrFapNdPpdBMTEy8vr6ysrLKysvj4eJn7pWhra6M/L9YAgoAEwEgkEokaGxstLS1V3ZDu8C+dzFcv+Xx+WFhYeXl5bGwsVaivr48Q6hZ+5DxMExMThFBSUpLkI43r0y9d/gAAIABJREFU16/L0+aUlJTMzMzLly+PGjVKslxXV3f06NF4uixKZ2ennp6ePNUqtWZJPB5PQ0OjrKxM5n4pHR0d6M+LNYAgIAEwEhUWFpIkOWXKFLyoqanZW+feIDM1NSUIoqmpSeaWsbGxTk5OxcXFVMn48eN1dXVv3bpFldy4caOjo+Ptt9+WWZuVlRWTySwpKVGotSRJhoeHl5aW5uXldbszwxYuXFhcXPzw4UO82Nra+vjxY3nGaiuv5rq6ukWLFkmWlJeXi8ViKysrefaL4QtkZmYmc3cKgYAEwEjR1dXV0NDQ2dl5586dkJAQa2vrpUuX4lU8Hq++vj4vL08kEtXU1Dx+/Fjyg4aGhs+fP3/06JFQKBSJRAUFBcob9q2jo2Nvb19VVSVzS9xxJ/nGDJPJXL9+fW5ubmZmpkAgKC0tXb16NZfLDQoKkqe2ZcuWHTt2LC0tTSAQiMXiqqqqFy9eIIQCAgLMzMzemJro7t27u3btSk9Pp9PphIQ9e/bgDcLCwmxsbJYuXfrkyZO6urrw8PC2tjZqOIZKamaxWBcuXLh8+bJAIBCJRMXFxZ9++imLxQoLC5Nnvxi+QH17C0oKCEgADEn79u1zc3NDCIWHh/v4+KSlpSUlJSGEJkyY8PDhw/T09PXr1yOEZs6cWV5ejj/S3t7u4uKira3t4eHh6Oj4ww8/UI9q1qxZM3369I8//njMmDGxsbG4K4bP5+OR5atXrzY1NXV2dp41a1Z9fb2yD83b27usrIx6OPT999/zeLyKigo3N7d169ZJbjllyhT8M0rZunVrfHx8TEyMsbGxp6enra1tYWEhi8VCCMk8RcnJyaGhoQkJCUZGRlwuNyQkpKGhASHU0dFRXV196tSpnk0lZb2IY2BgcPXqVUtLS1dXVwsLi59//vnMmTPU+0MqqZnJZE6dOnXlypUWFhZsNtvf39/W1raoqAgP6JC5X+zmzZsWFhYTJkyQZ2MFyDk8HN5DGnLgPaQhZBD+voKCggwNDZW6C5nk/E6Wl5dramr29mbM4BOLxR4eHhkZGVAzVltby2Qy9+zZI8/G8B4SAOANlJekeWDxeLyYmJiYmBg5U4UqlVgszsvLEwqFAQEBUDMWHR3t6uoaHBw84DVDQJLLnj178LPW/fv345IBzPwvM8k8ZeXKlWw2myAIRR+99kYysX9v7zkmJiYSBEGj0ZycnK5cudL/HREEQafTLSwsAgMDf//99340/3+o6up0OyiCILS0tExNTadNm7Z7927c2wP6JiIiwt/fPyAgQJ7RDUpVWFiYk5NTUFAg/dWoEVIzQigxMbGkpOTs2bN0On3AK4cuO3nhXuZvvvkGL+bn53M4nNOnT/e/Zk9Pz9TU1Lq6OoFAcPz4cTqdPnPmzN42xqm35MnYIX+XHX4r3tzcnMqhQuns7MQZrmbMmCFPVTJ3pKenR5Jkc3Pz6dOnra2tdXV1//jjj/7XrMKrQx0UHjLwww8/LF26lCAILpcrf+IZZf99RURE4JdAbW1ts7Ozlbcj6RTtRj5//nx4eLjy2gMUlZeXFx8fL5kLXCboshsM3t7eTU1Ns2fP7n9Vurq6uH+fzWYvWLDA19f33Llz+GHyoHn77bdfvnyZl5fXrTwnJwdnAh5YLBZr9uzZf//735ubm1NSUga8fpVcHYIg9PX1p02bdujQoRMnTrx69Qo3o/9t6L/4+PjXr1+TJFlZWSk5842a8/Ly2rlzp6pbAf6Xj49PRESE5MjGgQUBSQVIkszOzqaSfMhM9i6JIAhlNGnNmjUIoW+++aZbeWJiIh6JpAzvvPMOQui3335TUv1905+rQ5k/f/7SpUurq6upXkQAgEwDGZCSk5NZLBaNRnv77bfNzMzodDqLxZo0aZKHhwd+6UxfX//rr7+mtr969aqzs7Oenh6TyXRxcTl//jxC6B//+Ieuri5BEAYGBnl5ebdu3bKxsdHQ0Oj2JtcbSc+xj6Smppe5VpJCmf8RQmKxOD4+fsyYMdra2sbGxnZ2dvHx8Xjaq566JXsnSXL37t1jxoxhMBh6enpfffWVzPPQB++9997YsWN/+OGHe/fuUYU//vhja2url5dXt40H6sJ1dnYiiSQxQ/HqSIFf8SkoKJC5JQDgfwxsP+DWrVsRQjdu3GhpaamtrZ05cyZC6MyZMzU1NS0tLXhURklJCd44Ozs7Ojq6vr6+rq5uypQpRkZGuPzu3bs6OjqffvopXoyIiDh48KCc7ZSeY196anrpa7s9pVAo8//27ds1NDROnTrV2tr6yy+/mJmZTZs27Y3t75nsffPmzQRB/O1vf2toaGhtbU1NTUVKeIZUWVn597//HSEUEhJClfv6+h46dAgnYpF8htTnC0c9bsEOHz6MEPrqq6/w4lC8Oj0PiiIQCBBCVlZWb6yqmxHyjBZeRRiBFPpuKyUgCYVCvPjPf/4TIVRaWooXf/75Z4RQVlZWzw/ivH440S9Jkt9++y1CKDMz8+jRo2FhYXI2kiTJoKAgyV+HmzdvIoS2bdtGkmRra6uurm5AQAC1FrcHT+sifS0p308eNSswDhsPHjzAi25ubu+88w5V8+eff06j0XCffjebN292dHQUCAR4sbW1VUdH54MPPqA2UNKghsrKysbGRhaLZWBg0NraSpJkRUWFpaXl69evewYkSQpdOMlBDSdPnjQzMzM1Na2qqiKH5tXpdlA94adKb1zVDQQkMFwp9N3WVN69F/ozuzvumUEI4WGCb0yZhVdR70l8/vnn//nPf1atWvX++++fPHmyzw2QzLEvPTW9oonrpeuW+b+9vV1ywmCxWEyn03s+GMTJ3i9cuEAle3/w4EFra+uMGTP60AZF6enpLVq0KD09PSsra9myZUlJSWvWrNHS0sJZFHuj6IVramoiCEJDQ8Pc3HzWrFlbt27FgyaG4tWRrqWlhSRJDocjZ6uKior8/f3l3HiIwvlmhv1hAklFRUVUykSZVDmo4cyZM9OmTTMxMWEwGJLPlrDt27c3Nzf3nKJDUVSOfemp6fuTuF6mWbNm/fLLL6dOnWpra7t161ZeXt7/+3//r9tP3huTveM/YJyHeBDgoQ379+9vbGzMzs5etWrVGzfrz4XDNxOdnZ1VVVXfffcdNWvyULw60t2/fx8h5OTk1P8WAjBCKPcOSYonT574+vrOmzfvu+++GzVqVEpKiuRPm0gk+vLLL/EQr7i4ONwT2AeSOfalp6bvT+J6maKjo3/55ZelS5c2NzdzudwFCxZ0S0yZkpJy/vz5y5cvd/vNxf9zf/36df/bIA9XV9cpU6YUFRUFBQX5+/sbGBj03EZJF24oXh3pzp07hxD66KOP5Nx+ypQp2dnZ8tc/FJ04cWLhwoXD/jCBJIVuiFUWkEpLS0Ui0Zo1a+zt7VGP0czr1q377LPP5s2b9+zZs9jYWC8vLz6f34e9SObYl56avj+J62UqKyurqKioqanR1Ox+wkmS3LhxY0NDQ15eXs+148ePp9Fo//3vf1evXt3/ZshjzZo1RUVFJ0+epDJydqOkCzcUr44UL1++TEpKsrS0XL58ef9bCMAIobIuO2tra4TQxYsX29vby8vLJR8GpKamWlhYzJs3DyEUHx/v7OwcGBiIxyzJo7cc+9JT0/cncb1Ma9eutba2fmNCIOnJ3k1MTPz8/E6ePJmRkSEQCO7cuaPo5MeKWrBggbGxsa+vL443PSnpwg3Fq0MhSbK5ubmrq4skyZqamuPHj0+dOlVDQyMvL0/+Z0gAgIEcZZecnIxTJ9na2l69enXnzp14+kIzM7MjR45kZWXh2ZwMDAyOHTtGkmR4eLihoaG+vr6/vz9+ZcTBwcHV1ZUgCENDw59++okkydDQUBqNhhDS09O7deuWzHYGBQXhPGmampocDmfu3LkVFRXU2q6urt27d48ePZpOpxsYGPj6+t67d0+etX/7299w41ks1rx581JSUszNzRFCOjo6c+bMSU1NxQc+evToioqKAwcO4J8hGxub+/fvkyR5+fJlIyMj6pzT6fSxY8fm5OSQJFlaWvrG67J79268a6FQuHLlSiMjI11d3XfffTcqKgohZGlp+euvv0o/FfKMaMrNzcV5g4yNjdeuXYsLv/76a3zySZLcsmULPlIajebs7Hz16tW+Xbgff/zR0dERHxqXy/X39+/ZmCF3dU6fPj1hwgQdHR0tLS18sHhY3TvvvBMTE1NXVyf95EuCUXZguFLlsG+VU4cc+z2lpqZKvt/z+vXr0NBQBoOBB1grCfzxy0klV6ebofL31U/wnRyB1GjYt0qoW479ly9fBgcHS+bn1tLSsra2FolEIpFowCelBwqBqwOA+hhKuez++OMPondKmvmj/7S1tel0ekZGxqtXr0Qi0fPnzw8ePBgVFRUQEAAPGFQOro46u3jxYkREhOQ0H0uWLJHcwMvLi81ma2hojBs37o3TdQ+arq6upKQkd3f3buUJCQlOTk7a2tosFsvJySkyMlL+x+HKq1n6Z6VMuXL69OmEhAQl/qdfGbddqqImOfZ7unLlyvvvv8/hcDQ0NPT09Nzd3VNTU0UikVJ3Ct0jclLJ1elmSPx99Z9C38moqKjZs2dTeTEcHBzwo778/HzJzQoKCnx8fAa4oQq6f//+1KlTEUITJ07stsrb23vPnj3V1dVCofDEiRN0Ol0y8Yqqapb+WelTriQnJ3t6ejY0NMi5rxH9DAlQICANIYPw99Xa2srn81VblfzfyR07djg6OlLZnkiSdHBwOHLkCI1Gs7CwaGxspMpVHpBKSkrmzZuXmZnp6uraM2z4+vpKHgV+Kef58+eqrVn6Z729vSVnPMKJhqmMoCRJBgcH8/l8Of/TBvMhAQC6y8jI6H/ekwGv6o0ePHgQGRm5bds2yZROCCF3d/eQkJBnz55t2LBBeXtX1MSJE3NycgIDA6m89ZJyc3MljwInypJzanbl1Sz9szKnXImOji4pKUlOTpZnXwqBgATAkEH2PgdHcHCwlpYWHu+O0P9n787jmrjWxoGfCVnZAihb2QQCIoKiVSuoV720tJUriojg1qJXpW4IKFUWEdnUaoEfFupt5UN71cqmF6yC9VpLWyvaRVCKrQLKJiqLbJIAIczvj3mbN2+AELKQCTzfv5zt5MyZkMc5c+Y8aOfOnVpaWhiGtbS0IISCg4P37t1bXV2NYRiHw5GcqGVURSGErl69qqurKza9hTxSU1NxHPfy8hq8KT4+3t7e/vTp09evXx9tE0mTiCQ6OtrS0pLFYs2YMYO4n1OsyspKPT094YxZJClZ8rGDU67o6+svXrw4JSUFx3EZqzscKe+koMtO7UCXnRqR8u9Lcg6O9evXGxsbC3c+fvw4Qqi5uZlY9PHxsbW1FW6VnKhlVEVdvnxZR0dHOPO6BFJ+J21sbBwdHcVWEnPS4zh+69YtCoUyZcqUV69e4YO67CQ3keREJPv27WMwGHl5eW1tbRERERQKRfok9DiOv/HGG4M71gh9fX0NDQ0nT55kMBhnzpyRvkyllizNsUOmXMFxPDw8HEmXdgC67AAYh3g8XlJS0qpVqzZs2MBms52dnU+dOtXS0iLz5B1UKpW4k3B0dExPT+/q6srMzJShHE9Pz87OzoMHD8pWDTHd3d1Pnjwh3tcekqura0hISE1NzYEDB8Q2SdlEbm5uurq6hoaG/v7+3d3ddXV1CKGenp709HRvb28fHx89Pb2oqCgajSZbgwxmYWFhbm4eExPz0Ucf+fn5KaRM+UuW5tjExERTU9P4+Hix9XZ2dgih4d4clxkEJADUg2JzcIgRTdSiWkRuLWJyjeHEx8dPnTo1LS3t5s2boutH20SiiUgePnzI5XKdnJyITSwWy8TERFENUl9f39TU9NVXX3355ZezZs1S4BM4eUoe8Vgi5co333wzOOUKcYFevHghZ/3FQEACQD0oNQcHEknUolo9PT1IJLH9kJhMZmZmJoZhmzdv5vF4wvXyNFF3dzdCKCoqSvhqY21treiTfHnQaDRDQ0MPD4+srKyKigoiraXKS5Z8rOSUK8Q748TFUiAISACoB6Xm4BBN1KJaxC/diK9eurq6hoaGVlZWxsXFCVfK00RE1rHk5GTRRxolJSUynIIEHA5HQ0OjoqJCscXKWfLgY0+ePHn27NkbN2689tprQx5C5O1U+FQmEJAAUA8j5uCgUqlDpmOWhmiiFjmLkpORkRGGYR0dHSPuGRcX5+DgUFpaKlwjT5oSCwsLJpMpOomU/FpbW9etWye6prKyUiAQWFhYqLBkycfiOL5///7y8vL8/HwJCcCIC0RMaqxAEJAAUA8j5uDgcDgvX77Mz8/n8/nNzc21tbWihxsYGDQ2NtbU1HR1dRHBZrhELaMtqqioSIHDvjU1NW1sbIhcySM2SGZmpugbM/KkKWEymZs2bTp//nx6enpnZ6dAIGhoaHj27BlCyN/f39jYWIapibS0tK5du3bjxo3Ozk4+n19aWvr+++9raWmFhoYSO6ikZMnHSplyhbhAzs7Oo635CKQcjQfDvtUODPtWI1L+fUnO0NHa2rp06VImk2ltbb179+6wsDCEEIfDIQZz371718rKisViLVy48Pnz55ITtYyqqMLCQh0dnfj4+BHrL+V3MigoiEajCWdbHzJJilBYWJjosG8JTTRiIpLe3t79+/dbWlpSqVQiFVlFRQWO497e3gih6OjoIWtbUlKyYMECU1NT4hfVxMTEzc3t+++/J7Z6eXlZW1tra2szGAxbW1t/f//y8nLhsaoqWcKxIybEIXh6epqZmRE5wCSDqYMAjkNAUitj//elkkQtUn4nKysrqVSqDO/rKIlAIFi0aFFGRgaUTGhpaWEymSdOnJBmZ3gPCQAwMrIlahHicDixsbGxsbFSToSjVAKBID8/v6urS+H5BNSxZEJMTIyLi0tQUJDCS4aABAAgnfDwcF9fX39/f2lGNyhVcXHxhQsXioqKJL8aNUFKRgglJSWVlZUVFhbSaDSFFw4BCYAJJyIiIjMzs6Ojw9raOi8vT9XVGVpCQkJQUNCRI0dUWw13d/dz584JZ/ab4CUXFBT09vYWFxfr6+srvHCE0DjMGAsAkCwxMVGB72Yqj4eHh4eHh6prAf7XihUrVqxYobzy4Q4JAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKYxilF1eXh6GYcqrClAGuGRqZIJcrAlymkBo9erVUu6J4dIlRS8pKamvr5ejSgCMW8nJyQihkJAQVVcEADKysLBwdXWVZk9pAxIAYDhr1qxBCOXk5Ki6IgCoN3iGBAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUqCqugIAqJ87d+7cu3dPuPj48WOE0GeffSZcM3PmzDfeeEMFNQNAnWE4jqu6DgComcuXLy9fvlxDQ4NCoSCEiD8iDMMQQgMDAwKB4Ouvv/7HP/6h4loCoG4gIAEwanw+f/LkyZ2dnUNu1dXVbW5uptPpY1wrANQdPEMCYNRoNNratWuHDDkSNgEAJIOABIAs1q5d29fXN3g9n89ft27d2NcHgHEAuuwAkMXAwMBrr7324sULsfWGhobPnz8nni0BAEYF/mwAkAWFQtm4caNY1xydTg8ICIBoBIBs4C8HABkN7rXr6+tbu3atquoDgLqDLjsAZGdnZ1dVVSVctLGxqa6uVmF9AFBrcIcEgOw2bNhAo9GIf9Pp9Pfff1+19QFArcEdEgCyq6qqsrOzEy4+fPjQ3t5ehfUBQK3BHRIAsuNwODNnzsQwDMOwmTNnQjQCQB4QkACQy3vvvaehoaGhofHee++pui4AqDfosgNALo2NjRYWFjiO19fXm5mZqbo6AKgxNQ5IJSUlSUlJqq4FAKi4uBghtGTJEhXXAwCEQkNDXV1dVV0LGalxl119fX1eXp6qa6EeGhoaoK2E8vLyGhoaFFigpaWllZWVAgtUiNu3b9++fVvVtQBjKi8vr76+XtW1kJ3a50PKzc1VdRXUQE5Ojp+fH7QVAcOwkJCQNWvWKKrAly9fIoQMDAwUVaBC+Pr6IvgDmWCIHCjqS+0DEgAqR7ZQBICaUuMuOwAAAOMJBCQAAACkAAEJAAAAKUBAAgAAQAoQkACQSmFhIZvN/vrrr1VdEWW5fv16eHj4hQsXbGxsiMmQNm7cKLqDh4eHjo6OhobG9OnT7969q6p6IoQGBgaSk5Pd3NzE1h87dszBwYHFYmlpaTk4OBw8eLCzs1PlJUs+NjY21tHRUVdXl8FgcDicDz/88NWrV8SmS5cuHTt2TCAQjOoU1BuutrKzs9W6/mMJ2koUQig7O3u0R12+fFlXV/fSpUvKqJIyrF69evXq1VLuHB0dvXz58s7OTmLR1tZ20qRJCKHLly+L7lZUVLRixQoFV3SUHj16tGDBAoTQzJkzxTZ5enqeOHGiqampq6srJyeHRqO99dZbKi9Z8rGLFy9OS0trbW3t7OzMzs6m0WjvvPOOcGtKSsrixYvb2tqk/CzZvtvkocY/UvAjKz1oK1Ek/6Plcrmurq7ylyN9QDpy5Ii9vT2PxxOusbW1PXfuHIVCMTMza29vF65XeUAqKytbtWrV2bNnXVxcBocNb29v0bMg3sRqbGxUbcmSj/X09Ozv7xduJV6Pq6urE64JCgpydXXl8/nSfBbJv9sjgi47AMglIyOjqalpzD6uqqrq4MGDhw8fZjKZouvd3NyCg4OfPn26b9++MavMiGbOnHnhwoX169czGIzBWy9evCh6FsTUgsIeMFWVLPnYy5cva2hoCLdOnjwZIcTlcoVrYmJiysrKUlJSpPksdQcBCYCR3bx509LSEsOwTz75BCGUnp6upaWlqalZUFDw7rvv6urqmpubnz9/ntg5NTWVyWQaGRl98MEHpqamTCbTzc3tzp07xNagoCA6nW5iYkIs7ty5U0tLC8OwlpYWhFBwcPDevXurq6sxDONwOAihq1ev6urqJiQkKOnUUlNTcRz38vIavCk+Pt7e3v706dPXr18f8lgcx5OSkqZNm8ZgMPT19VeuXPnnn38SmyQ3EUJIIBBER0dbWlqyWKwZM2YQN/GKVVlZqaenp4xZneQpWfKxT58+ZbFY1tbWwjX6+vqLFy9OSUnB1Xbe0VFQ8R2aHKAbSnrQVqKQTN0axBRhJ0+eJBYjIyMRQt9++21HR0dTU9OiRYu0tLT6+vqIrYGBgVpaWg8ePOjp6amoqJg7d66Ojo6wH2b9+vXGxsbCko8fP44Qam5uJhZ9fHxsbW2FWy9fvqyjoxMbGzvaCkvZZWdjY+Po6Ci20tbW9smTJziO37p1i0KhTJky5dWrV/igLrvo6Gg6nX7mzJn29vb79+/Pnj178uTJz58/J7ZKbqJ9+/YxGIy8vLy2traIiAgKhfLLL79If3ZvvPHG4I41Ql9fX0NDw8mTJxkMxpkzZ6QvU6klS3Nsd3e3jo5OUFCQ2Prw8HCEUGlp6YifItt3mzzgDgkA2bm5uenq6hoaGvr7+3d3d9fV1Qk3UalU4tbB0dExPT29q6srMzNTho/w9PTs7Ow8ePCg4mr9v7q7u588eWJrazvcDq6uriEhITU1NQcOHBDbxOPxkpKSVq1atWHDBjab7ezsfOrUqZaWls8++0x0tyGbqKenJz093dvb28fHR09PLyoqikajydY+g1lYWJibm8fExHz00Ud+fn4KKVP+kqU5NjEx0dTUND4+Xmw9kZW4vLxctmqrEQhIACgAnU5HCPH5/CG3zpkzR1NTU9idRR5NTU04jmtqakrYJz4+furUqWlpaTdv3hRdX1FR8erVqzlz5gjXzJ07l06nCzsnxYg20cOHD7lcrpOTE7GJxWKZmJgoqn3q6+ubmpq++uqrL7/8ctasWQp8ICdPySMee/HixZycnG+++UZHR0dsE3GBXrx4IWf9yQ8CEgBjgcFgNDc3q7oW4np6ehBCQz7GF2IymZmZmRiGbd68mcfjCde3t7cjhLS1tUV31tPT6+rqGvFzu7u7EUJRUVHYX2pra0Wf5MuDRqMZGhp6eHhkZWVVVFQkJiYqpFg5S5Z8bFZW1tGjR4uLi6dMmTL4WBaLhf66WOMbBCQAlI7P57e3t5ubm6u6IuKIX7oRX710dXUNDQ2trKyMi4sTrtTT00MIiYUfKU/T0NAQIZScnCz6/KCkpESGU5CAw+FoaGhUVFQotlg5Sx587MmTJ8+ePXvjxo3XXnttyEP6+vrQXxdrfIOABIDSFRcX4zg+f/58YpFKpQ7XuTfGjIyMMAzr6OgYcc+4uDgHB4fS0lLhGicnJ21t7V9//VW45s6dO319fa+//vqIpVlYWDCZzLKyMtmqPaTW1tZ169aJrqmsrBQIBBYWFiosWfKxOI7v37+/vLw8Pz9f7F5TFHGBjI2NZTwB9QEBCQClGBgYaGtr6+/vv3//fnBwsKWlZUBAALGJw+G8fPkyPz+fz+c3NzfX1taKHmhgYNDY2FhTU9PV1cXn84uKipQ37FtTU9PGxkaa/LlEx53oGzNMJnPv3r0XL148e/ZsZ2dneXn59u3bTU1NAwMDpSlt06ZN58+fT09P7+zsFAgEDQ0Nz549Qwj5+/sbGxvLMDWRlpbWtWvXbty40dnZyefzS0tL33//fS0trdDQUGIHlZQs+dgHDx589NFHn3/+OY1Gw0ScOHFCtBDiAjk7O4+25upHJWP7FAIQgrN3AAAgAElEQVSGMksP2koUGv3Q2JMnTxJvDmlqanp5eaWlpRHPme3s7Kqrqz/77DNdXV2EkJWV1aNHj3AcDwwMpNFoZmZmVCpVV1d35cqV1dXVwtJaW1uXLl3KZDKtra13794dFhaGEOJwOMS48Lt371pZWbFYrIULFz5//rywsFBHRyc+Pn60pynlsO+goCAajcblconFixcvEoPuJk+evGvXLrGdw8LCRId9DwwMHD9+3M7Ojkaj6evre3t7P3z4kNg0YhP19vbu37/f0tKSSqUaGhr6+PhUVFTgOO7t7Y0Qio6OHrK2JSUlCxYsMDU1JX6+TExM3Nzcvv/+e2Krl5eXtbW1trY2g8GwtbX19/cvLy8XHquqkiUcO9zAuePHj4uW4OnpaWZmNjAwMGT5omT4bpOKGv9IwY+s9KCtRI3BH21gYKCBgYFSP2JEUgakyspKKpUqw/s6SiIQCBYtWpSRkQElE1paWphM5okTJ6TZWd0DEnTZAaAU6jJJM4fDiY2NjY2NlXIiHKUSCAT5+fldXV3+/v5QMiEmJsbFxSUoKEgZhZMNBCQAJrrw8HBfX19/f39pRjcoVXFx8YULF4qKiiS/GjVBSkYIJSUllZWVFRYW0mg0hRdOQhCQFE9CghMxW7Zs0dHRwTBsVMONhkvZoigPHz7cvXv39OnTdXR0qFQqm822t7f39PRU+KjcwSQ0nWieHgKdTjcyMlqyZMnx48fb2tqUXTfpRUREZGZmdnR0WFtb5+Xlqbo6UklISAgKCjpy5Ihqq+Hu7n7u3DnhRH8TvOSCgoLe3t7i4mJ9fX2FF05Squ4zlB1pn4tITnAihphuUppZqggSUrZIIH1bnT59mkaj/e1vf7t69WpbW1tPT091dXVWVpabm9u//vUv6T9RNiM2na2tLZvNxnGcGMP23XffBQQEYBhmamoq/UxoSM372aU0qnxIYHxQ9+82VZXBcJzS1tYODAwkBsiuWbPmwoULOTk59fX18r8Pce/evdjY2O3bt3d3d+NKmPr39u3bgYGBixcv/uabb6jU//lu2NjY2NjY6OnpVVZWKvwTxUjfdBiG6enpLVmyZMmSJZ6enn5+fp6eno8ePWKz2cquJABASaDLTvFGTHAiCsMw6UuWnLJFfvHx8QKB4MiRI8JoJPT222/v2rVLGR8qalRNJ7R69eqAgICmpqZTp04pt34AAGWaEAHpzJkzc+bMYTKZWlpaU6ZMIaY/wWVN5TJt2jQMwygUyuuvv078Vn744YdsNpvJZH7xxReDP10swQmO48ePH586dSqDwWCz2cQ7KGTQ19f37bffTpo0ad68eZL3VFXTSUC8c1pUVDSKEwYAkI1qewzlIeVzkeTkZITQkSNHWltbX758+a9//Wv9+vW4HKlc+vv7p0yZYmlpKZp4OCQkRGxiLsLgBCeRkZEYhn388cdtbW1cLjctLQ2N5hkSQULKliFJ01aPHj1CCM2fP3/E0lTVdLjIMyQxnZ2dCCELC4sRK4+rfz+7lOAZ0gSk7t/tcR6Q+vr69PT0li5dKlzT39+fkpLC5XK1tbX9/f2F63/++WeEkDANGvGryuPxiEUibFRVVRGLRJDLyckhFru7uy0tLTs6OgZXIDIy0t7evrOzk1jkcrmamppvvfWWcIfRDmogKCMgEZOSvfnmm5J3U1XTEYYLSDiOE0+VJFeeoO5/tFKCgDQBqft3e5x32d2/f7+9vf3tt98WrtHQ0NizZ488qVwQQlu2bGGz2cIs92fPnl25ciUxM4qowQlOqqqquFyuu7u7gs5PkYi5HUd8YKOqppOMGOUxuJzh+Pn5YeNdXl5eXl6eqmsBxpSU33/SGuej7IieHGKefFHypHIhDty2bdvx48d//vnnefPmffrpp4NfN8nKykpKSiouLhadUp6YJJGYe59spkyZwmQyiY47CVTVdJIR1XZwcJBy/+DgYFdXVyl3VlPEzWhISIiqKwLGjmIz5I69cR6QiF+0lpYWsfXypHIhBAUFpaSkJCcnb9++3cLCQiwJ9MmTJ7/55psbN26I/XAzmUyEUG9v7yjPYywwGIy33367oKDgp59+Il51EvXy5csPP/zw9OnTqmo6ya5evYoQevfdd6Xc39XVdc2aNdKXr45yc3MRQuP+NIEodQ9I47zLbsqUKQYGBteuXRNbL08qF4K5ufmaNWvy8vIOHjwYHBwsXI9LTHDi5OREoVC+//57mc5G6WJiYhgMRmhoqGhiUMLvv/9OjAVXVdNJ8Pz58+TkZHNz882bN0t/FACAbMZ5QGIwGBERET/88ENQUNDTp08HBga6uroePHggTyoXob179/b397e1tf39738XrpSc4ISYZj8vLy8jI6Ozs/P+/fufffaZ4k9bVi4uLufOnfv9998XLVpUWFjY0dHB5/OfPHny+eef//Of/yRm01JV0wnhOP7q1StiKv7m5ubs7OwFCxZoaGjk5+dL/wwJAEBGKh1SIRfpp8P55JNPnJ2dmUwmk8mcNWtWWloaLl8qF6GlS5eePn1adM2ICU66urq2bNkyadIkbW3thQsXRkdHI4TMzc3v3bs34olITtmikLbCcbyurm7fvn3Ozs7a2toaGhp6enqzZs365z//+dNPPxE7qKTpLl26NGPGDE1NTTqdTqFQ0F+TNcybNy82Nra1tVXKs8PVfySSlGCU3QSk7t9tDFfCDDRjIycnx8/PT33rP5agrURhGJadnT3uH674+vqiv54kgQlC3b/b47zLDgAAgLqAgEQWf/75p4TXC5SU+wsAUdevXw8PDxfN9LFx40bRHTw8PHR0dDQ0NKZPn3737l1V1RNJTMIiT34WZRTL5/MTExM5HA6dTtfT03NycqqpqRm8W09Pj4ODQ1RUFLF46dKlY8eOqUuaR0WBgEQWDg4OErpWs7KyVF1BMM4dOnQoNTU1IiLCx8fn8ePHtra2kyZNOnv27JUrV4T7XLt2LTc3d/ny5RUVFbNnz1ZVVSsrK//2t7+FhoYOfo9bwiZVFevn5/fvf//73LlzXC73jz/+sLW1HTJBWmRk5MOHD4WLXl5eTCbT3d2dePNvgoCABICC8Xg8RaVPVGBRkh09ejQrKysnJ0d0aozU1FQKhRIYGKjyTLKi7t27d+DAge3bt7u4uEi/SVXFZmVl5efn5+bmvvHGG1Qq1dTUtKCgwMnJSWy3W7du/f7772Ir9+zZM3PmzGXLlvX394/2c9UUBCQAFCwjI6OpqYlsRUlQVVV18ODBw4cPEy9uC7m5uQUHBz99+nTfvn3KroP0JCRhkSc/i5KK/fTTT2fPnu3s7CxhHx6PFxYWJpxPS1RMTExZWdmQm8YlCEgADAEfPsVGUFAQnU4XpqzeuXOnlpYWhmHEhCDBwcF79+6trq7GMIzD4aSmpjKZTCMjow8++MDU1JTJZLq5uQnn/RtVUQihq1ev6urqJiQkKPZkU1NTcRz38vIavCk+Pt7e3v706dPXr18f8lgJDSU5FwlCSCAQREdHW1paslisGTNmEC8njCd9fX23b98e8b4qMjJy586dQ84opq+vv3jx4pSUlAkyRBYCEgBDiImJCQ8Pj4yMbGpq+uGHH+rr6xctWvTixQuEUGpqquiw2rS0tMOHDwsXU1JSli9fbmtri+N4VVVVUFBQQEAAl8vds2dPTU3N3bt3+/v733rrrfr6+tEWhRAiHnEPDAwo9mSvXLkydepU4gUyMSwW64svvqBQKFu3bu3u7h68g4SG2rFjR0hICI/H09HRyc7Orq6utrGx2bp1q3Cm3QMHDnz00UfJycnPnj1bvnz5unXrRGcAGQcaGxv7+vp+++23pUuXEv8dmTZtGvEepHCfn376qbq6et26dcMVMmvWrKdPn967d29MqqxiEJAAEMfj8ZKSklatWrVhwwY2m+3s7Hzq1KmWlhaZp9WgUqnEPYSjo2N6enpXV1dmZqYM5Xh6enZ2dh48eFC2agypu7v7yZMnYjMKinJ1dQ0JCampqTlw4IDYJikbys3NTVdX19DQ0N/fv7u7u66uDiHU09OTnp7u7e3t4+Ojp6cXFRVFo9FkaxbSIgYvGBoaJiQkVFRUvHjxYuXKlbt27frqq6+IHXg8XnBwcHp6uoRC7OzsEELDvTY+zkBAAkDcaFNsjMqcOXM0NTWF/Voq19TUhOP4kLdHQvHx8VOnTk1LS7t586boenlykTx8+JDL5Qof77NYLBMTE/I0i0IQz5ymT5/u5uZmYGDAZrMPHz7MZrOFATsiImLbtm1mZmYSCiEuDXHTOe5BQAJAnJwpNkbEYDCam5sVUpT8enp60F8/ncNhMpmZmZkYhm3evFl04l15GoroAIyKihK+bFdbWyvDoGoyI2b5Es02QKfTraysqqurEUI3b94sLy/fsmWL5EJYLBb66zKNexCQABAnf4oNCfh8vqKKUgji927EFzBdXV1DQ0MrKyvj4uKEK+VpKOIZvlj2+pKSEhlOgbS0tbXt7OwePHggurK/v5/NZiOEMjIyvv32WwqFQsRjokESEhIwDBN9ltbX14f+ukzjHgQkAMSNmGKDSqUKn8yPVnFxMY7j8+fPl78ohTAyMsIwTJo3jeLi4hwcHEpLS4Vr5MlFYmFhwWQyy8rKZKu2uvDz8ystLX38+DGxyOVya2triVHgmZmZosGYuGmOjIzEcVy0F5S4NMbGxqqo/liDgASAuBFTbHA4nJcvX+bn5/P5/Obm5traWtHDDQwMGhsba2pqurq6iGAzMDDQ1tbW399///794OBgS0vLgIAAGYoqKipS+LBvTU1NGxsbIpexZETHnYaGhugamXORMJnMTZs2nT9/Pj09vbOzUyAQNDQ0PHv2DCHk7+9vbGys8KmJVFJsaGiolZVVQEBAXV1da2vr/v37eTze4OEhEhCXRvKbTOOHgmYNV4FRpVSY4KCtRCEppuiXkGIDx/HW1talS5cymUxra+vdu3eHhYUhhDgcTl1dHY7jd+/etbKyYrFYCxcufP78eWBgII1GMzMzo1Kpurq6K1eurK6ulq2owsJCHR2d+Ph4aU5T+vQTQUFBNBqNy+USixcvXiQG3U2ePHnXrl1iO4eFha1YsUKahhoxF0lvb+/+/fstLS2pVCqRKqyiogLHcW9vb4RQdHT0kLWVkIRFcn4WlRSL43h9ff3atWv19fUZDMa8efOKioqG3E30DkmUp6enmZkZkQBsRNJ8t8lMjX+k4EdWetBWosb4jzYwMNDAwGDMPk5I+oBUWVlJpVLPnDmj7CpJSSAQLFq0KCMjYyIXS2hpaWEymSdOnJByf3UPSNBlB4DSkXzOZg6HExsbGxsbO+Skn2NMIBDk5+d3dXUpdoZ79SpWKCYmxsXFJSgoSBmFkxAEJAAACg8P9/X19ff3V/k8qsXFxRcuXCgqKpL8atT4LpaQlJRUVlZWWFhIo9EUXjg5QUACQIkiIiIyMzM7Ojqsra3z8vJUXR1JEhISgoKCjhw5otpquLu7nzt3Tji/38QsFiFUUFDQ29tbXFysr6+v8MJJi6rqCgAwniUmJiYmJqq6FtLy8PDw8PBQdS0AQgitWLFixYoVqq7FWIM7JAAAAKQAAQkAAAApQEACAABAChCQAAAAkILaD2rIyclRdRXUADFnJbSV0DibxHNIxJQzcNGBOlH1m7myG38JjwEAQE5qPVMDhk+MVO0AKA+RhhzuRQCQEzxDAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoQkAAAAJACBCQAAACkAAEJAAAAKUBAAgAAQAoYjuOqrgMAaubcuXMZGRkDAwPE4pMnTxBC1tbWxCKFQvnnP/+5fv16ldUPAPUEAQmAUbt///7MmTMl7HDv3r0ZM2aMWX0AGB8gIAEgCwcHh4cPHw65icPhVFZWjnF9ABgH4BkSALLYuHEjjUYbvJ5Go23atGns6wPAOAB3SADI4vHjxxwOZ8g/n8rKSg6HM/ZVAkDdwR0SALKwsbGZPXs2hmGiKzEMmzNnDkQjAGQDAQkAGb333nsaGhqiazQ0NN577z1V1QcAdQdddgDIqKmpydTUVDj4GyFEoVAaGxuNjY1VWCsA1BfcIQEgIyMjo8WLFwtvkjQ0NJYsWQLRCACZQUACQHYbN24U7WPYuHGjCisDgLqDLjsAZNfZ2WloaNjX14cQotFoTU1Nenp6qq4UAOoK7pAAkJ2uru4777xDpVKpVOqyZcsgGgEgDwhIAMhlw4YNAoFAIBDA5HUAyAm67ACQS09Pz+TJk3Ecb2lpYbFYqq4OAOoMF5Gdna3q6gAAAJgosrOzRWMQdcg9xr5aQCX8/PyCg4NdXV1VXRH1VlZWhmGY5Pm/5VRSUpKSkjIR/jbhOzlx+Pn5ia35P112OTk5fn5+0Ik3cWAYlp2dvWbNGlVXRL319/cjhKjUIf57pygT528TvpMTx+BrrcQ/IQAmCKWGIgAmDhhlBwAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABMC4VVhYyGazv/76a1VXRFmuX78eHh5+4cIFGxsbDMMwDBOb39bDw0NHR0dDQ2P69Ol3795VVT0RQgMDA8nJyW5ubqPapJJi+Xx+YmIih8Oh0+l6enpOTk41NTWDd+vp6XFwcIiKiiIWL126dOzYMYFAMNqPEwUBCYBxa3wPEz906FBqampERISPj8/jx49tbW0nTZp09uzZK1euCPe5du1abm7u8uXLKyoqZs+eraqqVlZW/u1vfwsNDeVyudJvUlWxfn5+//73v8+dO8flcv/44w9bW9tXr14N3i0yMvLhw4fCRS8vLyaT6e7u3t7ePtpPFILhqgCMW56enh0dHWPwQTwez93d/datW2PwWYSjR49mZWXdu3ePyWQKV6ampm7cuDEwMLCiooLNZo9ZZSS7d+9ebGzs9u3bu7u7xf6LIGGTqorNysrKz8+/d++es7MzQsjU1LSgoGDwbrdu3fr999/FVu7Zs+fx48fLli374YcfZHsXAu6QAADyysjIaGpqGrOPq6qqOnjw4OHDh0WjEULIzc0tODj46dOn+/btG7PKjGjmzJkXLlxYv349g8GQfpOqiv30009nz55NRKPh8Hi8sLCwlJSUwZtiYmLKysqG3CQNCEgAjE83b960tLTEMOyTTz5BCKWnp2tpaWlqahYUFLz77ru6urrm5ubnz58ndk5NTWUymUZGRh988IGpqSmTyXRzc7tz5w6xNSgoiE6nm5iYEIs7d+7U0tLCMKylpQUhFBwcvHfv3urqagzDOBwOQujq1au6uroJCQlKOrXU1FQcx728vAZvio+Pt7e3P3369PXr14c8FsfxpKSkadOmMRgMfX39lStX/vnnn8QmyU2EEBIIBNHR0ZaWliwWa8aMGeNvJqe+vr7bt2+7uLhI3i0yMnLnzp2GhoaDN+nr6y9evDglJUW27mIISACMTwsXLhTtQ9uxY0dISAiPx9PR0cnOzq6urraxsdm6dSufz0cIBQUFBQQEcLncPXv21NTU3L17t7+//6233qqvr0cIpaamis7vkpaWdvjwYeFiSkrK8uXLbW1tcRyvqqpCCBFPtgcGBpR0aleuXJk6daqmpubgTSwW64svvqBQKFu3bu3u7h68Q0xMTHh4eGRkZFNT0w8//FBfX79o0aIXL16gkZoIIXTgwIGPPvooOTn52bNny5cvX7du3a+//qqkc1SJxsbGvr6+3377benSpcT/S6ZNm5aWliYaXX766afq6up169YNV8isWbOePn167949GSoAAQmAicXNzU1XV9fQ0NDf37+7u7uurk64iUqlErcOjo6O6enpXV1dmZmZMnyEp6dnZ2fnwYMHFVfr/9Xd3f3kyRNbW9vhdnB1dQ0JCampqTlw4IDYJh6Pl5SUtGrVqg0bNrDZbGdn51OnTrW0tHz22Weiuw3ZRD09Penp6d7e3j4+Pnp6elFRUTQaTbb2IS1i8IKhoWFCQkJFRcWLFy9Wrly5a9eur776itiBx+MFBwenp6dLKMTOzg4hVF5eLkMFICABMEHR6XSEkPC//2LmzJmjqakp7M4ij6amJhzHh7w9EoqPj586dWpaWtrNmzdF11dUVLx69WrOnDnCNXPnzqXT6cLOSTGiTfTw4UMul+vk5ERsYrFYJiYmJGwfeRDPnKZPn+7m5mZgYMBmsw8fPsxms4UBOyIiYtu2bWZmZhIKIS4NcdM5WhCQAABDYzAYzc3Nqq6FuJ6eHvTXT+dwmExmZmYmhmGbN2/m8XjC9cSIZG1tbdGd9fT0urq6RvxcogMwKioK+0ttba0Mg6rJzNTUFCFEPBok0Ol0Kyur6upqhNDNmzfLy8u3bNkiuRAiTSVxmUYLAhIAYAh8Pr+9vd3c3FzVFRFH/N6N+AKmq6traGhoZWVlXFyccKWenh5CSCz8SHmaxDP85ORk0YRyJSUlMpwCaWlra9vZ2T148EB0ZX9/PzGGPiMj49tvv6VQKEQ8JhokISEBwzDRZ2l9fX3or8s0WhCQAABDKC4uxnF8/vz5xCKVSh2uc2+MGRkZYRgmzftVcXFxDg4OpaWlwjVOTk7a2tqiv5537tzp6+t7/fXXRyzNwsKCyWSWlZXJVm114efnV1pa+vjxY2KRy+XW1tYSo8AzMzNFgzFx9xwZGYnjuGgvKHFpjI2NZfh0CEgAgP8xMDDQ1tbW399///794OBgS0vLgIAAYhOHw3n58mV+fj6fz29ubq6trRU90MDAoLGxsaampquri8/nFxUVKW/Yt6ampo2NTUNDw4h7Eh13Ghoaomv27t178eLFs2fPdnZ2lpeXb9++3dTUNDAwUJrSNm3adP78+fT09M7OToFA0NDQ8OzZM4SQv7+/sbGxwqcmUkmxoaGhVlZWAQEBdXV1ra2t+/fv5/F4g4eHSEBcGslvMg1LNOIRw+pxMGGgQTntATnJ8Ld58uRJ4s0hTU1NLy+vtLQ04mmznZ1ddXX1Z599pqurixCysrJ69OgRjuOBgYE0Gs3MzIxKperq6q5cubK6ulpYWmtr69KlS5lMprW19e7du8PCwhBCHA6nrq4Ox/G7d+9aWVmxWKyFCxc+f/68sLBQR0cnPj5ehjOV5jsZFBREo9G4XC6xePHiRWLQ3eTJk3ft2iW2c1hY2IoVK4SLAwMDx48ft7Ozo9Fo+vr63t7eDx8+JDaN2ES9vb379++3tLSkUqmGhoY+Pj4VFRU4jnt7eyOEoqOjh6xtSUnJggULiMczCCETExM3N7fvv/9e8iZVFYvjeH19/dq1a/X19RkMxrx584qKiobcTfQOSZSnp6eZmdnAwMBw5QsNvtYQkCY0CEjqYgz+NgMDAw0MDJT6EdKQ5jtZWVlJpVLPnDkzNlUakUAgWLRoUUZGxkQultDS0sJkMk+cOCHNzoOvNXTZAQD+h5xTNY8ZDocTGxsbGxs75KSfY0wgEOTn53d1dfn7+0/YYoViYmJcXFyCgoJkOxwC0v86ceIE8bz01KlTxBoFzt4fGxvr6Oioq6vLYDA4HM6HH3443N/Sli1bdHR0MAwb1eNTeWawl0B0Yv/h3nNMSkrCMIxCoTg4OPzwww/yfxCGYUTf0fr16//44w85qv8/VHVlxU4KwzA6nW5kZLRkyZLjx4+3tbXJ/+kTVnh4uK+vr7+//9jMHitBcXHxhQsXioqKJL8aNb6LJSQlJZWVlRUWFtJoNBmLEL1dgi67yspKhNCnn35KLF6+fFlXV/fSpUvyl7x48eK0tLTW1tbOzs7s7GwajfbOO+8MtzMxfVZpaamUhT969GjBggUIoZkzZ46qVki6Ljuig97ExKSvr09sU39/v5WVFULI3d19VB893Aex2Wwcx1+9enXp0iVLS0ttbe0///xT/pJVeGWFJ0UMGfjuu+8CAgIwDDM1Nf3ll1+k/BRl/22Gh4cTL4FOmTIlNzdXeR80Iim/k4Rvvvlm//79Sq0PkFJ+fn5iYmJ/f7/0hwy+1hCQ/g+xny0F8vT0FL1UxMxgxAPhwUYVkMrKylatWnX27FkXFxflBSRiXGxOTo7YpuzsbOK2TLEBifCf//wHIbRz5075S1bhlRU7KUJubi6FQjEyMmpvb5fmUybO3+aoAhJQa4OvNXTZKQuO47m5ucIpNy5fviw6/HTy5MkIoeFe88YwTPoPkmeqeent2LEDIfTpp5+KrU9KStq7d6+SPnTevHkIocFpV1RLnisrtHr16oCAgKamJmEvIgBg1AEpJSVFS0uLQqG8/vrrxsbGNBpNS0tr9uzZixYtIl4c09PT+/DDD4X7//jjj46Ojmw2m8lkOjs7f/PNNwihL774QltbG8MwfX39/Pz8X3/91crKSkNDQ8IMskKS58lHEqeXH3GrqFHN3o8QEggEiYmJU6dOZbFYkydPtra2TkxMFJ0jWdTTp09ZLJa1tbWwVsePH586dSqDwWCz2cSYWlL5+9//Pm3atO+++040R+RPP/3E5XI9PDzEdlbURe/v70cik8So45WVgHjFp6ioaMQ9AZgoRG+XpOwWOHToEELozp073d3dLS0t77zzDkLoypUrzc3N3d3dxPiKsrIyYufc3NyYmJiXL1+2trbOnz9/0qRJxPoHDx5oamq+//77xGJ4ePjp06elvNELDAzU0tJ68OBBT09PRUXF3LlzdXR0hJ0k0dHRdDr9zJkz7e3t9+/fnz179uTJk58/fy7NVrGOHWLu/ZMnTxKLkZGRCKFvv7t81cAAACAASURBVP22o6Ojqalp0aJFWlpawscqCQkJGhoaBQUFXC73t99+MzY2XrJkyZD17+7u1tHRCQoKEq6JjIzEMOzjjz9ua2vjcrlpaWloNM+QCG+88YbyuuyePHny//7f/0MIBQcHC9d7e3tnZmYSE7GIdtnJfNHFerfOnDmDEAoLCyMW1fHKDj4poc7OToSQhYXFkEWJgS47MP4MvtayB6Suri5i8csvv0QIlZeXE4s///wzQigrK2vwgYmJieivyXpxHP/Xv/6FEDp79uxXX30VGhoq/WkEBgaK/oX/8ssvCKHDhw/jOM7lcrW1tf39/YVbifrExsaOuBWX7meLx+MRi0TYqKqqIhbnzp07b948Ycnbtm2jUCi9vb2D6x8ZGWlvb9/Z2UkscrlcTU3Nt956S7jDaAc1EJQdkNrb27W0tPT19YkXEqurq83NzXt7ewcHJFGjuuiigxry8vKMjY2NjIwaGhpw9byyYic1GIZhenp6Q24SAwEJjD+Dr7Usac/FEINziN4VhBAx4G/Iaa+ITcJ3HbZt2/bf//73gw8+ePPNN/Py8mSugOg8+ZKnlx/t5POSic3e39PTI5pQWSAQ0Gg00acLhIsXL+bk5Fy7dk1HR4dYU1VVxeVy3d3dZajDWGKz2evWrfv888+zsrI2bdqUnJy8Y8cOOp1OzKU4nNFe9I6ODgzDNDQ0TExMli1bdujQIWKue3W8spJ1d3fjOE7MBSClnJwc6XdWX+NsxlIgPQUEJMmuXLly/PjxioqKzs7OwVEqISEhLy+vqalJzk8RzpMveXp5eSafH9GyZcuOHz9eUFDg4eFRUVGRn5//j3/8Q+xnKysrKykpqbi4+LXXXhOuJKZ+GjIfMNns2LHj888/P3XqlLe3d25u7nAvCclz0dlsNnGZxKjjlZXs0aNHCCEHBwfpa+Ln5yf9zuorJSUlJSVF1bUAKqDcUXZ1dXXe3t4mJiZ37tzp6Og4duyY6FY+n79nz56kpKSSkpL4+HiZP0V0nnzJ08vLM/n8iGJiYv7+978HBATo6uquWrVqzZo1n3/+uegOJ0+ePHv27I0bN8R+s4j/fff29spfB2VzcXGZP3/+zz//HBgY6Ovrq6+vP3gfJV10dbyykl29ehUh9O6770p/iIp6VsYUgi67CWPwN1y5d0jl5eV8Pn/Hjh02NjZo0Gjm3bt3b926ddWqVU+fPo2Li/Pw8HB1dZXhU0TnyZc8vbw8k8+PqKKiorq6urm5mUoVb1Ucxw8cONDW1pafnz94q5OTE4VC+f7777dv3y5/NZRtx44dt2/fzsvLI57KDKaki66OV1aC58+fJycnm5ubb968Wf4aAjA+KPcOydLSEiF0/fr1np6eyspK0Q79tLQ0MzOzVatWIYQSExMdHR3Xr19PjDuSxnDz5EueXl6eyedHtGvXLktLyyEnBHrw4MFHH330+eef02g00YlkTpw4gRAipg3Oy8vLyMjo7Oy8f/++8B0XElqzZs3kyZO9vb2JeDOYki66Ol5ZIRzHX716Rcx/3NzcnJ2dvWDBAg0Njfz8/FE9QwJgnBO9gZJmJE9KSgoxCdKUKVN+/PHHo0ePEskEjY2Nz507l5WVReRl0tfXP3/+PI7j+/fvNzAw0NPT8/X1JV77sLW1dXFxwTDMwMDg1q1bOI6HhIRQKBSEEJvN/vXXX0e80ZM8T76E6eUlb/3444+Jymtpaa1atWq0s/ffuHFj0qRJwoal0WjTpk27cOECjuPl5eVDNv7x48eJj+7q6tqyZcukSZO0tbUXLlwYHR2NEDI3N793796IrSF5qnnJ0EjdI0NO7P/hhx8SFw7H8aioKKKVKBSKo6Pjjz/+iMt00X/66Sd7e3viFExNTX19fQdXRu2u7KVLl2bMmKGpqUmn04mTJYbVzZs3LzY2trW1VZprRIBRdmD8GXyt1XLqIJLMky8mLS1N9B2d3t7ekJAQBoMhzNpCQvDHLw0yXFl1+duUH3wnJ47B11rpo+yUhGzz5D9//jwoKEh0fm46nW5pacnn8/l8vmzp5QEZwJUFYMyQbi67P//8ExueknJ4yI/FYtFotIyMjBcvXvD5/MbGxtOnT0dHR/v7+8vzkEBNW2M8UdKVBQAMRrqA5ODgIOEWLysrKyIiIjMzs6Ojw9raWp7XaRWLzWZfu3bt999/t7e3Z7FYjo6OmZmZR48eJaaxkNmIraGo+oPhKOnKAmW7fv16eHi4aEqqjRs3iu7g4eGho6OjoaExffr0u3fvqqSS8fHxYv/LdHJyGoNjEUJ8Pj8xMZHD4dDpdD09PScnp5qamsG79fT0ODg4REVFEYuXLl06duyY8jqo1K/LLjExkZiNhmwWLVr03//+V9W1AIoHV1btHDp0qLS09Ny5czo6Oj4+PhwOp729/ezZs/7+/p6ensQ+165du3r16qlTp/Lz81VbW5Xw8/N78ODBuXPnXn/99ebm5g8++GDIcaSRkZGi8yl7eXk9efLE3d09Pz+fePlPsUh3hwQAUAkej6eojMMKLEoGR48ezcrKysnJEZ3DKTU1lUKhBAYGqjzDrJgzZ86IdnuMKtmKzMdmZWXl5+fn5ua+8cYbVCrV1NS0oKBg8A3WrVu3Bpe5Z8+emTNnLlu2TDhdnAJBQAIAIIRQRkaG/JN4Kbyo0aqqqjp48ODhw4dFpx9ECLm5uQUHBz99+nTfvn0qqRipfPrpp7Nnz3Z2dpawD4/HCwsLG3IOp5iYmLKyMmVM7wQBCYDxAx8+KVRQUBCdTidewEII7dy5U0tLC8OwlpYWhFBwcPDevXurq6sxDONwOJKzjo2qKITQ1atXdXV1ExISxqAFUlNTcRz38vIavCk+Pt7e3v706dPXr18f8lgJrSdN0qzo6GhLS0sWizVjxgximD459fX13b5928XFRfJukZGRO3fuHHKOTX19/cWLF6ekpOBDTf8jF9E7vonzrgMgIHjnQ01I+bcpOSnU+vXrjY2NhTsfP34cIdTc3Ews+vj42NraCrdKzjo2qqIuX76so6MjTAUimZzfSRsbG0dHR7GVRP4UHMdv3bpFoVCmTJny6tUrHMeLiopWrFgh3E1y60lOmrVv3z4Gg5GXl9fW1hYREUGhUH755ZcRaxsXF2dubq6np0ej0aZMmbJixYqff/5ZyjOV+dgnT54ghFxcXJYsWWJiYsJgMBwcHD755BNiJhHCzZs3vby8cBwnJq2OjIwUKyQ8PByNPkWOmMHXGu6QABgneDxeUlLSqlWrNmzYwGaznZ2dT5061dLSIvNMVFQqlbhdcHR0TE9P7+rqyszMlKEcT0/Pzs7OgwcPylYN6XV3dz958oSYW2RIrq6uISEhNTU1Bw4cENskZeu5ubnp6uoaGhr6+/t3d3fX1dUhhHp6etLT0729vX18fPT09KKiomg0mjRt9f7771+6dKm+vv7Vq1fnz5+vq6tbvHhxRUWFNCcr87HE4AVDQ8OEhISKiooXL16sXLly165dX331lbApgoOD09PTJRRiZ2eHEBpuphKZQUACYJxQbFIoMaJZx0iLyANJTAQ1nPj4+KlTp6alpd28eVN0/WhbTzRp1sOHD7lcrnBQAIvFMjExkaatLCwsZs2apa2tTafT58+fn5mZyePxiPyQyjuWwWAghKZPn+7m5mZgYMBmsw8fPsxms4WhNyIiYtu2bUQesuEQjfzixQtpqio9CEgAjBNKTQqFRLKOkVZPTw/66wd3OEwmMzMzE8OwzZs383g84Xp5Wq+7uxshFBUVJXwlqLa2lsvljrb+zs7OGhoaRKIs5R1LzHtJPPAj0Ol0Kyur6upqhNDNmzfLy8u3bNkiuRBijhKiwRUIAhIA44RSk0KJZh0jLeJXcsTXNl1dXUNDQysrK+Pi4oQr5Wk94sl/cnKy6OMQGfLeDgwMDAwMSA6o8h+rra1tZ2f34MED0ZX9/f3ENNkZGRnffvsthUIhIitxagkJCRiGiaZ3IfJEK3zqLAhIAIwTIyaFolKpgxP4Skk065icRSmPkZERhmHSvGkUFxfn4OBQWloqXCNPSi0LCwsmkyk64aGU3n77bdFFYhyElBnC5DnWz8+vtLT08ePHxCKXy62trSVGgWdmZoqGVdFBDaL9mUQjE5PoKxAEJADGiRGTQnE4nJcvX+bn5/P5/Obm5traWtHDDQwMGhsba2pqurq6iGAzXNax0RZVVFQ0NsO+NTU1bWxsGhoaRtyT6LgTzUMvT0otJpO5adOm8+fPp6end3Z2CgSChoaGZ8+eIYT8/f2NjY2Hm5ro6dOnWVlZ7e3tfD6/pKRky5YtlpaWwkSdyjs2NDTUysoqICCgrq6utbV1//79PB5v8EAPCYhGlvwmkyxEgyEM+55oEAz7VhNS/m1KThnV2tq6dOlSJpNpbW29e/fusLAwhBCHwyEGc9+9e9fKyorFYi1cuPD58+eSs46NqqjCwkIdHZ34+HhpzlTO72RQUBCNRhNmBhkyoZdQWFiY6LBvCa03YtKs3t7e/fv3W1paUqlUIuVmRUUFjuPe3t4Ioejo6CFru3fvXltbWy0tLSqVam5uvnXr1sbGRuFW5R2L43h9ff3atWv19fUZDMa8efOKioqG3G24Yd+enp5mZmaiI8VlMPhaQ0Ca0CAgqYux/9tUVdYxOb+TlZWVVCpVbE4dFRIIBIsWLcrIyFCjY0fU0tLCZDJPnDghZzmDrzV02QEAhka2rGPS4HA4sbGxsbGxQ04VOsYEAkF+fn5XV5cMmWJUdaw0YmJiXFxcgoKCFF4yBCQAwLgSHh7u6+vr7++v8nlUi4uLL1y4UFRUJPnVKFIdO6KkpKSysrLCwkIajabwwiEgAQDEkTPrmPQSEhKCgoKOHDmi2mq4u7ufO3dOOOmfWhwrWUFBQW9vb3Fxsb6+vsILR+qYDwkAoGykzTomPQ8PDw8PD1XXYrxZsWLFihUrlFc+3CEBAAAgBQhIAAAASAECEgAAAFKAgAQAAIAUhhjU4OvrO/b1AKqSnJycm5ur6lqAERAztUyQv034Tk5YGC6Sg7akpCQpKUmFtQFAHRFzdM6aNUvVFQFAzYSGhopOCPt/AhIAQAZr1qxBCOXk5Ki6IgCoN3iGBAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUoCABAAAgBQgIAEAACAFCEgAAABIAQISAAAAUqCqugIAqB8ul9vb2ytc7OvrQwi1tbUJ1zAYDE1NTRXUDAB1huE4ruo6AKBm0tPTd+7cKWGHtLS0HTt2jFl9ABgfICABMGrNzc2mpqYCgWDIrRoaGs+ePTM0NBzjWgGg7uAZEgCjZmho6O7urqGhMXiThobGm2++CdEIABlAQAJAFhs2bBiydwHH8Q0bNox9fQAYB6DLDgBZdHV1GRoaig5tINDp9ObmZl1dXZXUCgC1BndIAMhCR0dn+fLlNBpNdCWVSl2xYgVEIwBkAwEJABmtX7++v79fdI1AIFi/fr2q6gOAuoMuOwBk1NfXN3ny5K6uLuEabW3tlpYWBoOhwloBoL7gDgkAGdHpdF9fXzqdTizSaDQ/Pz+IRgDIDAISALJbt24dMU0DQojP569bt0619QFArUGXHQCyGxgYMDExaW5uRghNnjz5+fPnQ76cBACQBtwhASA7CoWybt06Op1Oo9HWr18P0QgAeUBAAkAua9eu7evrg/46AOQHs31LUlJSUl9fr+paAFLDcXzSpEkIoSdPntTU1Ki6OoDULCwsXF1dVV0L8oJnSJL4+vrm5eWpuhYAgHFi9erVubm5qq4FecEd0gjG0xcoJyfHz89vHP8XxNfXFyE09tfrwYMHCCFHR8ex+bhxfx3HK+L7CSSAgASAvMYsFAEwvsGgBgAAAKQAAQkAAAApQEACAABAChCQAAAAkAIEJAAAAKQAAQlMdIWFhWw2++uvv1Z1RZTl+vXr4eHhFy5csLGxwTAMw7CNGzeK7uDh4aGjo6OhoTF9+vS7d++qpJLx8fHY/+Xk5DQGxyKE+Hx+YmIih8Oh0+l6enpOTk5DvuDc09Pj4OAQFRVFLF66dOnYsWMCgUD6DwIjgoAEJrrx/ULPoUOHUlNTIyIifHx8Hj9+bGtrO2nSpLNnz165ckW4z7Vr13Jzc5cvX15RUTF79mwV1lYl/Pz8/v3vf587d47L5f7xxx+2travXr0avFtkZOTDhw+Fi15eXkwm093dvb29fQwrO85BQAITnaenZ0dHx/Lly5X9QTwez83NTdmfIuro0aNZWVk5OTk6OjrClampqRQKJTAwsKOjYywrM6IzZ87gIn7//fcxODYrKys/Pz83N/eNN96gUqmmpqYFBQWDb7Bu3bo1uMw9e/bMnDlz2bJlYomDgcwgIAEwRjIyMpqamsbs46qqqg4ePHj48GEmkym63s3NLTg4+OnTp/v27RuzypDWp59+Onv2bGdnZwn78Hi8sLCwlJSUwZtiYmLKysqG3ARkAAEJTGg3b960tLTEMOyTTz5BCKWnp2tpaWlqahYUFLz77ru6urrm5ubnz58ndk5NTWUymUZGRh988IGpqSmTyXRzc7tz5w6xNSgoiE6nm5iYEIs7d+7U0tLCMKylpQUhFBwcvHfv3urqagzDOBwOQujq1au6uroJCQlKOrXU1FQcx728vAZvio+Pt7e3P3369PXr14c8FsfxpKSkadOmMRgMfX39lStX/vnnn8QmyU2EEBIIBNHR0ZaWliwWa8aMGdnZ2co4O4Xo6+u7ffu2i4uL5N0iIyN37txpaGg4eJO+vv7ixYtTUlLGd8fvmIGABCa0hQsX3rp1S7i4Y8eOkJAQHo+no6OTnZ1dXV1tY2OzdetWPp+PEAoKCgoICOByuXv27Kmpqbl7925/f/9bb71FTAmfmpq6Zs0aYVFpaWmHDx8WLqakpCxfvtzW1hbH8aqqKoQQ8Tx8YGBASad25cqVqVOnampqDt7EYrG++OILCoWydevW7u7uwTvExMSEh4dHRkY2NTX98MMP9fX1ixYtevHiBRqpiRBCBw4c+Oijj5KTk589e7Z8+fJ169b9+uuv0lQ4PDxcX1+fTqdbW1uvXLnyl19+kf5kZTu2sbGxr6/vt99+W7p0KfE/jGnTpqWlpYlGl59++qm6ulpCbpFZs2Y9ffr03r170tcWDAcCEgBDcHNz09XVNTQ09Pf37+7urqurE26iUqnErYOjo2N6enpXV1dmZqYMH+Hp6dnZ2Xnw4EHF1fp/dXd3P3nyxNbWdrgdXF1dQ0JCampqDhw4ILaJx+MlJSWtWrVqw4YNbDbb2dn51KlTLS0tn332mehuQzZRT09Penq6t7e3j4+Pnp5eVFQUjUaTpn3ef//9S5cu1dfXv3r16vz583V1dYsXL66oqJDmZGU+lhi8YGhomJCQUFFR8eLFi5UrV+7ateurr74SNkVwcHB6erqEQuzs7BBC5eXl0lQVSAYBCQBJ6HQ6Qkj4338xc+bM0dTUFHZnkUdTUxOO40PeHgnFx8dPnTo1LS3t5s2bousrKipevXo1Z84c4Zq5c+fS6XRh56QY0SZ6+PAhl8sVDgpgsVgmJibStI+FhcWsWbO0tbXpdPr8+fMzMzN5PF5aWtqIB8pzLIPBQAhNnz7dzc3NwMCAzWYfPnyYzWYLQ29ERMS2bdvMzMwkFEI0MnH7COQEAQkAuTAYjObmZlXXQlxPTw/66wd3OEwmMzMzE8OwzZs383g84XpiHLO2trboznp6el1dXSN+LtEBGBUVJXwlqLa2lsvljrb+zs7OGhoajx49Gu2BozrW1NQUIUQ85CPQ6XQrK6vq6mqE0M2bN8vLy7ds2SK5EBaLhf5qcCAnCEgAyI7P57e3t5ubm6u6IuKIX8kRX9t0dXUNDQ2trKyMi4sTrtTT00MIiYUfKU+TePKfnJwsOgi7pKRktPUfGBgYGBiQHFDlP1ZbW9vOzo5IZyXU39/PZrMRQhkZGd9++y2FQiEiK3FqCQkJGIaJPhXr6+tDfzU4kBMEJABkV1xcjOP4/PnziUUqlTpc594YMzIywjBMmjeN4uLiHBwcSktLhWucnJy0tbVFf3Pv3LnT19f3+uuvj1iahYUFk8ksKysbbYXffvtt0cVffvkFx3Eps33Lc6yfn19paenjx4+JRS6XW1tbS4wCz8zMFA2rxH1wZGQkjuOi/ZlEIxsbG0vzcUAyCEgAjM7AwEBbW1t/f//9+/eDg4MtLS0DAgKITRwO5+XLl/n5+Xw+v7m5uba2VvRAAwODxsbGmpqarq4uPp9fVFSkvGHfmpqaNjY2DQ0NI+5JdNxpaGiIrtm7d+/FixfPnj3b2dlZXl6+fft2U1PTwMBAaUrbtGnT+fPn09PTOzs7BQJBQ0PDs2fPEEL+/v7GxsbDTU309OnTrKys9vZ2Pp9fUlKyZcsWS0vL7du3E1uVd2xoaKiVlVVAQEBdXV1ra+v+/ft5PN7ggR4SEI0s+U0mICUISGBC++STT+bOnYsQ2r9//4oVK9LT05OTkxFCM2bMePz48eeff753716E0DvvvFNZWUkc0tPT4+zszGKxFi1aZG9v/9133wl7h3bs2LF06dK1a9dOnTo1Li6O6MZxdXUlxoVv377dyMjI0dFx2bJlL1++VPapeXp6VlRUCB8O/ec//+FwONXV1XPnzt29e7fonvPnzw8NDRVdc+jQocTExNjY2MmTJy9evHjKlCnFxcVaWloIoRGbKCUlJSQk5NixY5MmTTI1NQ0ODm5ra0MI9fX1NTU1FRQUDFnbd955JyoqytzcXFNTc82aNQsWLLh9+/akSZOIrco7Vl9f/8cffzQ3N3dxcTEzM/v555+vXLky4ptJon755RczM7MZM2ZIfwgYFg6Gt3r16tWrV6u6FgpDvKKo6loo0Rhcr8DAQAMDA6V+xIikvI6VlZVUKlVsTh0VEggEixYtysjIUKNjR9TS0sJkMk+cOCHNzuPs90QZ4A4JgNFRlwmeORxObGxsbGzskFOFjjGBQJCfn9/V1eXv768ux0ojJibGxcUlKChIGYVPQBCQFKO3t3fPnj0mJiaamppvvvkm8Uj51KlTqq7XqIkmKRAzZcoUhNCJEyfU9+wmmvDwcF9fX39/f5XPo1pcXHzhwoWioiLJr0aR6tgRJSUllZWVFRYW0mg0hRc+MUFAUoyPP/746tWrf/75Z0pKygcffCA6G416ESYpYLPZxE10f38/l8t98eIF8Se9b98+9T07OUVERGRmZnZ0dFhbW+fl5am6OlJJSEgICgo6cuSIaqvh7u5+7tw54UR/anGsZAUFBb29vcXFxfr6+govfMKCgKQY+fn5c+bM0dPT27Zt2+rVq6U8SiwfwdinJ5CGhoYGi8UyMjKyt7cf1YFqcXajkpiY2Nvbi+P4kydPpL/KKufh4XH06FFV12K8WbFiRXh4uOjoRCA/CEiK0dDQIMNtu1g+gjFOTzBa+fn5o9pfvc4OAKByEJDk9d///pfD4Tx79uzLL7/EMExswhXCjz/+6OjoyGazmUyms7PzN998gwblIxicnmDIafxHnPx/7I3vswMAjBkISPJ66623qqqqjI2N33//fRzHhxzR9OLFCz8/v5qamsbGRm1t7fXr16NB+QgGpycYchr/ESf/V4YbN26cOHFiuK3qfnYAAJKAgDQWVq9efejQIX19fQMDAy8vr9bW1hGn4xxxGn8J+REUoqOjQzi+zt3dXcKe6nh2AAASoqq6AhMO8ahpxHdZpJ/GX3J+BJmx2Wxi1meEUHFxsZQ51lR+drdv///27jwqimtbGPgp6Bm6GRSUoBCgEY04hDiBGnXxZEW5KjiB40VfXKgxLaJ8iAoCgsZggEcC8XlDcEWNUZSFJoDX5UvQ5Q24ciOIQaMIMigyGZGhwW6gvj/OSt0OQ3fTU1XT+/eXXae6etfpsjdVders4jVr1qi5spHCc9WM+N0ceYqLi6lpD8Gg4AzJEPLy8hYuXGhnZ8flcv/f//t/6rxFV9P468TChQv37ds3VKux7x0AgCHgDEnvamtrAwMDV65c+fXXX7/11luff/65Or/a1DT+YWFh+o9Rc0zbuzlz5mRnZ+t2m0xz8eLFoKCgEb+bIw+c1KoECUnv7t+/L5fLd+7c6erqihAiCEKdd2k8jb+Bjey9AwAYElyy0zsnJyeE0I0bN7q7uysqKhTrQPerR6D40tzcfKhp/BllZO8dAMCgaJrU1TioMztvdXX1u+++ixBisVheXl6XLl367LPPcLUuCwuLlStXkiQZGRlpa2trbW29Zs2aL774AiHk5uZWW1t79+5dZ2dnPp8/b968hoaGfi/fvHkTGRnp5OTEYrHs7OxWrVpVXl6enp6Op/Bxd3evrKw8deqUSCRCCDk7Oz9+/Fh5qOrMEv2vf/2LmpFh7Nixvr6+/VZg7N6RJjOb8oiftX2kMpHjUxsESZI0pEEjga/5jpiL9fjewwj+xkfY9zWUEf89jlQmcnxqAy7ZAQAAYARISACYtBs3bkRFRSmWHdm0aZPiCn5+fkKh0NzcfPLkyUMVAjeMvr6+lJSUQafolcvlR48eFYvFHA7H2tra09Ozurqaar19+/bcuXMFAoGDg0NkZOSbN28U3ztU69WrV48fP24s5a9GBkhIAJiuw4cPp6WlHThwgCo7MmrUqLNnz+bl5VHrXL9+PTs7e9myZeXl5V5eXnSFWlFR8f7774eHhw/6vFpQUNA333xz7tw5qVT68OFDNzc3o1znRwAAIABJREFUahKv8vJyPz8/X1/f5ubmnJycr7/+eseOHdQblbQuX76cx+P5+vpST4gDfYOEBMAw6LCIBu31OD755JPvvvvu4sWLQqGQWpiWlmZmZhYaGkp7TT9F9+7d279//44dO6ZPnz6w9bvvvsvNzc3Ozp49ezaLxXJwcLhy5Qo1D8iRI0fGjh0bFxdnYWHh7e0dGRl5+vRpaloQ5a27d++eNm3a0qVLe3p6DLOnJg4SEgDDoMMiGvTW43jy5El0dHRcXByPx1Nc7uPjExYW9vz5cyVzcxjetGnTLl++vGHDBi6XO7D1yy+/9PLymjJlysCmnp6evLy8BQsWUE/ILVmyhCTJK1euqGzFYmNjS0tLU1NTdb9XYABISMDkkCSZnJw8adIkLpdrY2MTEBBA/UUskUg4HA5VYPSjjz6ysLAgCKKlpQUNqKmRlpbG4/Hs7e23b9/u4ODA4/F8fHyoJ7GGtSmE0LVr10QiUWJiomE6IS0tjSTJ5cuXD2xKSEiYMGHCV199dePGjUHfq6QDVRYQGbTsiDZkMllxcfGgZ04Ioaqqqo6ODvy0HObm5oYQKisrU9mK2djYLFiwIDU1FYY1GgAkJGByYmNjo6KiDh482NTUdOvWrbq6uvnz5zc2NiKE0tLS1q5dS62Znp4eFxdHvexXREMikYSEhEil0t27d1dXV9+9e7enp2fx4sV1dXXD3RT6c0bavr4+/XcAQgjl5eV5eHjgp7764fP5p0+fNjMz27ZtG551sB8lHaiygMigZUe02ZH6+nqZTPbrr78uWrQI/1kwadKk9PR0nD8aGhoQQorXJHk8Hp/Px9Eqb6W8++67z58/v3fvnjZxAnVAQgKmpaurKzk5eeXKlRs3brSyspoyZcrJkydbWlpOnTql2QZZLBY+V3jnnXcyMjLa29sV62ioz9/fv62tLTo6WrMwhqWzs/Pp06f4bGBQ3t7ee/bsqa6u3r9/f78mNTtw0AIiKsuOaAAPXrCzs0tMTCwvL29sbAwICNi1a9e3336LEMJD5voVGmez2V1dXSpbKe7u7gih+/fvaxMnUAckJGBaysvLOzo6ZsyYQS2ZOXMmh8NRnPRIYzNmzBAIBIPW0WCUpqYmkiQHPT2iJCQkeHh4pKen3759W3H5cDtQsYCI+mVH1IfvKk2ePNnHx8fW1tbKyiouLs7KygonSHyHrN+QBJlMxufzVbZScEf1O20C+gAJCZgWPIS3X6V5a2vr9vZ2nWyfy+WqrE9Iu+7ubvTnT/lQeDxeVlYWQRBbt25VPGPQpgP1UXbEwcEBIYTvzGEcDsfZ2bmyshIhhO/htbW1Ua1SqbS7uxu/S3krBecn3GlAryAhAdNibW2NEOr369na2jpu3DjtNy6Xy3W1Kb3Cv7AqH/n09vYODw+vqKg4cuQItVCbDqTKjihOX1ZUVKTBLlAsLS3d3d0fPHiguLCnp8fKygoh5OLiIhQKa2pqqCZ8x27q1KkqWykymQz92WlAryAhAdPi6elpaWmpeCP9zp07Mpnsvffewy9ZLJbG5XcLCwtJkqSqgmqzKb2yt7cnCEKdJ42OHDkyceLEkpISaonKDlRCT2VHgoKCSkpKqqqq8EupVFpTU4NHgbNYrKVLl966dYsaLVJQUEAQBB5eqLyVgjsKzykM9AoSEjAtPB5v7969OTk5Z8+ebWtru3///o4dOxwcHEJDQ/EKYrH4jz/+yM3Nlcvlzc3Nin8+owE1NRBCfX19r1696unpKSsrCwsLc3JyCgkJ0WBTBQUFBhv2LRAIXF1dcSl05fCFO8Xb/io7UPnWhio7EhwcPGbMGM2mJgoPD3d2dg4JCamtrX358mVkZGRXVxc1HCM6OrqxsfHw4cOdnZ1FRUVJSUkhISEeHh7qtGK4owZ9zgnomOEmFjdCI2y6+BFftkDN76uvry8pKcnd3Z3NZtvY2AQGBj569Ihqffny5aJFi3g8nouLy8cffxwREYEQEovFtbW1JEn2K6IRGhrKZrMdHR1ZLJZIJAoICKisrNRsU/n5+UKhMCEhQWX8OvkeJRIJm82WSqX4ZU5ODh50N3r06F27dvVbOSIiYsWKFdRLJR2osoDIoGVHSJIMDAxECMXExAwabVFR0dy5c6lbO2PHjvXx8bl58ya1Ql1d3bp162xsbLhc7qxZswoKChTffvPmzVmzZnG5XAcHh4iIiO7ubvVbSZL09/d3dHTs6+tTu3cHN8J+T/RhJP88aW+EHUCQkHQuNDTU1tbWkJ9I6uh7rKioYLFYZ86c0UlI2uvt7Z0/f35mZibdgfTX0tLC4/FOnDih/aZG2O+JPsAlOwC0YqSzQYvF4vj4+Pj4eGoSUhr19vbm5ua2t7cHBwfTHUt/sbGx06dPl0gkdAdiEiAhAWCioqKi1qxZExwcTPs8qoWFhZcvXy4oKFD+aJThJScnl5aW5ufns9lsumMxCZCQANDQgQMHsrKyXr9+7eLicunSJbrD0URiYqJEIjl27Bi9Yfj6+p47d46a948hrly58ubNm8LCQhsbG7pjMRUsugMAwFgdPXr06NGjdEehLT8/Pz8/P7qjYKIVK1asWLGC7ihMC5whAQAAYARISAAAABgBEhIAAABGgIQEAACAESAhAQAAYAQYZafCpUuXCIKgOwpdGmG7M9CI30HMRHZzhFm9ejXdITAaQUKh+KEVFRXhctQAKJGSkoIQ2rNnD92BAKYbP368t7c33VEwFyQkALS1du1ahNDFixfpDgQA4wb3kAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAiQkAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAiQkAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAiQkAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAiQkAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAiQkAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAiQkAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAiQkAAAADACJCQAAACMAAkJAAAAI0BCAgAAwAgsugMAwPjcuXPn3r171MuqqiqE0KlTp6gl06ZNmz17Ng2RAWDMCJIk6Y4BACPzww8/LFu2zNzc3MzMDCGE/xMRBIEQ6uvr6+3t/f777//2t7/RHCUAxgYSEgDDJpfLR48e3dbWNmirSCRqbm7mcDgGjgoAYwf3kAAYNjabvW7dukFTjpImAIBykJAA0MS6detkMtnA5XK5fP369YaPB4ARAC7ZAaCJvr6+t956q7Gxsd9yOzu7hoYGfG8JADAs8N8GAE2YmZlt2rSp36U5DocTEhIC2QgAzcD/HAA0NPCqnUwmW7duHV3xAGDs4JIdAJpzd3d/8uQJ9dLV1bWyspLGeAAwanCGBIDmNm7cyGaz8b85HM7f//53euMBwKjBGRIAmnvy5Im7uzv18tGjRxMmTKAxHgCMGpwhAaA5sVg8bdo0giAIgpg2bRpkIwC0AQkJAK1s3rzZ3Nzc3Nx88+bNdMcCgHGDS3YAaKW+vn78+PEkSdbV1Tk6OtIdDgBGzFQSUnJyclFREd1RgJGpsLAQIbRw4UKa4wAjlLe3d3h4ON1RGIKpXLIrKioqLi6mO4oR5dmzZ5cuXaI7CpoVFxcXFxc7OTk5OzvTHYsewXdNo+LiYtP5Y9pUzpDWrFmDEMrOzqY7kJHj4sWLQUFBJnL8DAUfV//7v/+LELK1taU7HH2B75pGJvXbBQX6ANDWCE5FABiSqVyyAwAAwHCQkAAAADACJCQAAACMAAkJAAAAI0BCAsDQ8vPzraysvv/+e7oD0ZcbN25ERUVdvnzZ1dUVz6u0adMmxRX8/PyEQqG5ufnkyZPv3r1LV5wIob6+vpSUFB8fn4FNcrn86NGjYrGYw+FYW1t7enpWV1dTrbdv3547d65AIHBwcIiMjHzz5o3ie4dqvXr16vHjx3t7e/W5T0YMEhIAhjayx08fPnw4LS3twIEDq1atqqqqcnNzGzVq1NmzZ/Py8qh1rl+/np2dvWzZsvLyci8vL7pCraioeP/998PDw6VS6cDWoKCgb7755ty5c1Kp9OHDh25ubh0dHbipvLzcz8/P19e3ubk5Jyfn66+/3rFjB/VGJa3Lly/n8Xi+vr6tra0G2EHjQ5qG1atXr169mu4oRpQLFy6YzvEzFIYfV1Kp1NvbW/vtqP9dHzt2bMKECV1dXdQSNze3c+fOmZmZOTo6tra2UssLCgpWrFihfWwaKy0tXbly5dmzZ6dPnz5t2rR+refPnycIoqysbND3BgUFubi49PX14ZdJSUkEQTx8+FCdVpIkJRKJt7e3XC5XJ06GH2O6BWdIAIxYmZmZTU1NBvu4J0+eREdHx8XF8Xg8xeU+Pj5hYWHPnz/ft2+fwYJRadq0aZcvX96wYQOXyx3Y+uWXX3p5eU2ZMmVgU09PT15e3oIFCwiCwEuWLFlCkuSVK1dUtmKxsbGlpaWpqam63ysjBwkJAIO6ffu2k5MTQRBffPEFQigjI8PCwkIgEFy5cmXJkiUikWjcuHHnz5/HK6elpfF4PHt7++3btzs4OPB4PB8fnzt37uBWiUTC4XDGjh2LX3700UcWFhYEQbS0tCCEwsLC9u7dW1lZSRCEWCxGCF27dk0kEiUmJupp19LS0kiSXL58+cCmhISECRMmfPXVVzdu3Bj0vSRJJicnT5o0icvl2tjYBAQE/P7777hJeRchhHp7e2NiYpycnPh8/tSpU/H5nDZkMllxcfH06dMHba2qquro6HBycqKWuLm5IYTKyspUtmI2NjYLFixITU0lR/TFWw1AQgLAoObNm/fzzz9TL3fu3Llnz56uri6hUHjhwoXKykpXV9dt27bJ5XKEkEQiCQkJkUqlu3fvrq6uvnv3bk9Pz+LFi+vq6hBCaWlpa9eupTaVnp4eFxdHvUxNTV22bJmbmxtJkrjOOr6X3tfXp6ddy8vL8/DwEAgEA5v4fP7p06fNzMy2bdvW2dk5cIXY2NioqKiDBw82NTXdunWrrq5u/vz5jY2NSFUXIYT279//6aefpqSkvHjxYtmyZevXr//3v/+tzY7U19fLZLJff/110aJF+O+ASZMmpaen4/zR0NCAEBIKhdT6PB6Pz+fjaJW3Ut59993nz5/fu3dPmzhHHkhIADCCj4+PSCSys7MLDg7u7Oysra2lmlgsFj51eOeddzIyMtrb27OysjT4CH9//7a2tujoaN1F/R+dnZ1Pnz7FZwOD8vb23rNnT3V19f79+/s1dXV1JScnr1y5cuPGjVZWVlOmTDl58mRLS8upU6cUVxu0i7q7uzMyMgIDA1etWmVtbX3o0CE2m61Z/1Dw4AU7O7vExMTy8vLGxsaAgIBdu3Z9++23CCE8ZM7c3FzxLWw2u6urS2UrBRcavn//vjZxjjyQkABgFg6HgxCi/vzvZ8aMGQKBgLqcxRxNTU0kSQ56ekRJSEjw8PBIT0+/ffu24vLy8vKOjo4ZM2ZQS2bOnMnhcKiLk/0odtGjR4+kUqmnpydu4vP5Y8eO1bJ/8F2lyZMn+/j42NraWllZxcXFWVlZ4QSJ75D19PQovkUmk/H5fJWtFNxR/U6bACQkAIwMl8ttbm6mO4r+uru70Z8/5UPh8XhZWVkEQWzdulXxjAGPgba0tFRc2draur29XeXn4guAhw4dIv5UU1Mz6DBu9Tk4OCCE8K04jMPhODs7V1ZWIoTwTbu2tjaqVSqVdnd343cpb6Xg/IQ7DVAgIQFgTORyeWtr67hx4+gOpD/8C6vykU9ca66iouLIkSPUQmtra4RQv/Sj5m7a2dkhhFJSUhRHD2tZQMjS0tLd3f3BgweKC3t6eqysrBBCLi4uQqGwpqaGasK36KZOnaqylSKTydCfnQYokJAAMCaFhYUkSc6ZMwe/ZLFYQ13cMzB7e3uCIF6/fq1yzSNHjkycOLGkpIRa4unpaWlpqTgS4c6dOzKZ7L333lO5tfHjx/N4vNLSUs3CHkpQUFBJSUlVVRV+KZVKa2pq8ChwFou1dOnSW7duUcNDCgoKCILAwwuVt1JwR40ZM0a3YRs7SEgAMF1fX9+rV696enrKysrCwsKcnJxCQkJwk1gs/uOPP3Jzc+VyeXNzs+If5gghW1vb+vr66urq9vZ2uVxeUFCgv2HfAoHA1dX12bNnKtfEF+4Ub/vzeLy9e/fm5OScPXu2ra3t/v37O3bscHBwCA0NVWdrW7ZsOX/+fEZGRltbW29v77Nnz168eIEQCg4OHjNmjGZTE4WHhzs7O4eEhNTW1r58+TIyMrKrq4sajhEdHd3Y2Hj48OHOzs6ioqKkpKSQkBAPDw91WjHcUYM+52TSDPwgLl1M6mlnw4CZGkiNjqvPP/8c32YQCATLly9PT0/H97fd3d0rKytPnTolEokQQs7Ozo8fPyZJMjQ0lM1mOzo6slgskUgUEBBQWVlJbe3ly5eLFi3i8XguLi4ff/xxREQEQkgsFtfW1pIkeffuXWdnZz6fP2/evIaGhvz8fKFQmJCQMNzdVPO7lkgkbDZbKpXilzk5OXjQ3ejRo3ft2tVv5YiICMWZGvr6+pKSktzd3dlsto2NTWBg4KNHj3CTyi568+ZNZGSkk5MTi8Wys7NbtWpVeXk5SZKBgYEIoZiYmEGjLSoqmjt3LnVrZ+zYsT4+Pjdv3qRWqKurW7dunY2NDZfLnTVrVkFBgeLbb968OWvWLC6X6+DgEBER0d3drX4rSZL+/v6Ojo7UbA5KmNRvl6n8oJjUl2oYkJBIgxxXoaGhtra2ev0IldT8risqKlgs1pkzZwwQkjp6e3vnz5+fmZlJdyD9tbS08Hi8EydOqLOySf12wSU7AJjOWCaHFovF8fHx8fHx1CSkNOrt7c3NzW1vbw8ODqY7lv5iY2OnT58ukUjoDoRxICEBAHQmKipqzZo1wcHB6oxu0KvCwsLLly8XFBQofzTK8JKTk0tLS/Pz89lsNt2xMA4kpCF9+OGHQqGQIAidD+AxMLlcHhMT4+rqyuFwHB0d9+3b1++hcaS0Kow2FCviYBwOx97efuHChUlJSa9evdLtx408Bw4cyMrKev36tYuLy6VLl+gORy2JiYkSieTYsWP0huHr63vu3Dlqoj+GuHLlyps3bwoLC21sbOiOhZHovmZoIJpdh8UTOJaUlOgjJIPZuXMnj8c7f/58W1vbTz/9JBKJ1q9fr7jC48eP586dixAaOAm/EurfQ3Jzc7OysiJJEo8W++mnn0JCQgiCcHBw+OWXX4a1L0xjItf34X4hjUzkGMPgDMkodXV1qXk2U1VVdfLkyc2bNwcHBwuFwoULF0okkm+//fbhw4d4hXv37u3fv3/Hjh1DzW2sQwRBWFtbL1y4MCsr6+LFi42Njf7+/rRf2xlI/e4FAOgQJCRlqIomTKN+nZtffvmlr69v9uzZ1JIPPvgAIfTPf/4Tv1ReFUZ/Vq9eHRIS0tTUdPLkSUN+rjoMXEYIAIBBQvoLkiSTkpI8PDy4XK6VlRV+qgP79NNPBQKBUChsamrau3evo6Mjfk5iqCIuyivZIKUFYIZb50YJMzMz9NcZSvA0w9QZEo3w050FBQXIaLsXAKBLtF4wNBw1r8MePHiQIIjPPvvs1atXUqk0PT0dKdxDOnjwIEJo9+7dn3/++cqVKx8+fBgTE8PhcM6cOdPa2lpWVubl5TV69OiGhga8fmhoqIWFxYMHD7q7u8vLy2fOnCkUCvETiyRJKn/vhg0bxowZQwWWlJSEEGpubsYvV61ahevcqITLgkVHR1NL8DzEgYGB/dacPXu2vu8h9YMnoBw/fjx+aYzdayLX9+EeEo1M5BjD4AzpP7q6ulJSUv7rv/4rPDzc2tqaz+fb2toOXO2TTz7ZtWvX5cuXnZ2dVRZxGaqSjZoFYLQ3ZcqUDz74ID09/ccff+zu7m5oaMjJySEIggkToOFBjP2m1DSu7gUA6BCL7gAY5MmTJ1Kp1NfXV831h1vERbGSzXDfq43vvvsuMjJy8+bNf/zxh4ODw+zZs0mSHDVqlM4/aLg6OztJksTTwAxkLN176dIlxt5r1C0T2U0GWr16Nd0hGAgkpP/A0x3i2ezVoUERF6qSjTYFYIbLyspKceDAixcvzp8//9Zbb+n8g4br8ePHCKGJEycO2mos3Ttnzpw9e/bofLOMUlRUlJqaii/cAQNLSUmhOwTDgYT0H7jUI65ArI7hFnFRrGSjTQEYLf3yyy8IoUWLFun7g1S6du0aQmjJkiWDthpL944bN27t2rU63yzTpKammsJuMlB2djbdIRgO3EP6D09PTzMzs5s3b6q//rCKuChWslH5Xv3VufnHP/7h4uKyYMECfWxcfQ0NDSkpKePGjdu6deugKxhp9wIANAYJ6T/wxPWXLl3KzMxsa2srKytTfg9cnSIuQ1WyUfneYdW5Ub5fs2bNqqmp6enpqa6u3rdv340bNzIzMzkcjma9pBmSJDs6OvBk+83NzRcuXJg7d665uXlubu5Q95CMpXsBADpD5xA/A1Jz6GR7e/uHH344atQoS0vLefPmxcTEIITGjRt3796948eP46d5xo8fT02wr6SIC6mqko3y9w6rzo3ynVq8eLG1tTWLxbKxsfH39+83W4/KqjBDUWco8NWrV6dOnSoQCDgcDn4iCk/WMGvWrPj4+JcvX1JrGmn3msiQXBj2TSMTOcYwgiRJejKhYa1ZswYZ/Grs9u3bs7OzX758acgPNZiLFy8GBQXRePwwoXtpOa4Mj/bv2pSZyDGGwSU7/TKWSjZGCroXgJEEEpJx+/3334mhMbA0GTAFN27ciIqKUiw+smnTJsUV/Pz8hEKhubn55MmT7969S1ecSGnhFblcfvToUbFYzOFwrK2tPT09q6urqdbbt2/PnTtXIBA4ODhERkb2G507VOvVq1ePHz8Of0gNieZLhoZi+OuwUVFReODA22+/nZ2dbciPNgx67yswpHtN5Pr+sL7rmJiYZcuWtbW14Zdubm74KewffvhBcbWCgoIVK1boONBhUl54JTAw0MPDo7i4WC6X19fXL1++/P79+7jpt99+4/P50dHRHR0dP//88+jRo7ds2UK9UXlramrqggULXr16pWaQJnKMYZCQgIbgRjdpkONKKpV6e3vTuyn1v+tjx45NmDChq6uLWuLm5nbu3DkzMzNHR8fW1lZqOe0JqbS0dOXKlWfPnp0+ffrAhHT+/HmCIMrKygZ9b1BQkIuLCx41SpJkUlISQRAPHz5Up5UkSYlE4u3tLZfL1YnTpH674JIdAIymw1oY+i6r8eTJk+jo6Li4OPyMOcXHxycsLOz58+f79u3T36cPl/LCK19++aWXl9eUKVMGNvX09OTl5S1YsICaS2nJkiUkSV65ckVlKxYbG1taWpqamqr7vTJykJAA0DtSR7UwlBfdGG5ZjWvXrolEosTERF3tZlpaGkmSy5cvH9iUkJAwYcKEr7766saNG8PtooyMDAsLC4FAcOXKlSVLlohEonHjxuFqzlhvb29MTIyTkxOfz586dar2UxzJZLLi4uKhSlZWVVV1dHQ4OTlRS9zc3BBCeGZ95a2YjY3NggULUlNTSRi4+FeQkADQu9jY2KioqIMHDzY1Nd26dauurm7+/PmNjY0IobS0NMUpedLT0+Pi4qiXqampy5Ytw7Uwnjx5IpFIQkJCpFLp7t27q6ur796929PTs3jx4rq6uuFuCv05RrGvr09Xu5mXl+fh4SEQCAY28fn806dPm5mZbdu2rbOzc+AKSrpo586de/bs6erqEgqFFy5cqKysdHV13bZtG/XM8v79+z/99NOUlJQXL14sW7Zs/fr1ipN0aKC+vl4mk/3666+LFi3CiX/SpEnp6ek4fzQ0NCCEhEIhtT6Px+Pz+Tha5a2Ud9999/nz5/fu3dMmzpEHEhIA+qXzWhhDFd0YLn9//7a2tujoaM3C6Kezs/Pp06f4bGBQ3t7ee/bsqa6u3r9/f78mNbvIx8dHJBLZ2dkFBwd3dnbW1tYihLq7uzMyMgIDA1etWmVtbX3o0CE2m61Zh1A6OjoQQnZ2domJieXl5Y2NjQEBAbt27fr222/Rn9NdmpubK76FzWZ3dXWpbKXgOpn379/XJs6RBxISAPql11oYikU36NXU1ESS5KCnR5SEhAQPD4/09PTbt28rLh9uF+EBlvgM6dGjR1Kp1NPTEzfx+fyxY8dq2SH4rtLkyZN9fHxsbW2trKzi4uKsrKxwgsR3yHChS4pMJsNTjShvpeCO6nfaBCAhAaBf+q6FQRXdoFd3dzf686d8KDweLysriyCIrVu3Kp4xaNNF+ALgoUOHqMfvampqpFKpZnuB4Zm08L03jMPhODs7V1ZWIoTwXTpc7xiTSqXd3d34XcpbKTg/4U4DFEhIAOiXXmthKBbdoBf+hVX5yKe3t3d4eHhFRcWRI0eohdp0ES5glpKSojh6uKioSINdoFhaWrq7uz948EBxYU9Pj5WVFULIxcVFKBQqTsiL78lNnTpVZStFJpOhPzsNUCAhAaBfeq2FoVh0Q8tNacne3p4giNevX6tc88iRIxMnTiwpKaGWDLfUiKLx48fzeLzS0lLNwh5KUFBQSUlJVVUVfimVSmtqavAocBaLtXTp0lu3blHjQQoKCgiCwMMLlbdScEeNGTNGt2EbO0hIAOiXzmthDFV0Y7ibKigo0OGwb4FA4Orqissuq+yQrKwsxdv+6pQaUbK1LVu2nD9/PiMjo62trbe399mzZy9evEAIBQcHjxkzRrOpicLDw52dnUNCQmpra1++fBkZGdnV1UUNx4iOjm5sbDx8+HBnZ2dRUVFSUlJISIiHh4c6rRjuqEGfczJpBn0Mlz4m9bSzYcBMDaTax5UOa2EoL7oxrE3l5+cLhcKEhASV8av5XUskEjabLZVK8cucnBw86G706NG7du2kyQa9AAAbfklEQVTqt3JERITiTA1Kuig9PR0PAXB3d6+srDx16hSuoeXs7Pz48WOSJN+8eRMZGenk5MRisXBVs/LycpIkAwMDEUIxMTGDRquy8EpdXd26detsbGy4XO6sWbMKCgoU337z5s1Zs2ZxuVwHB4eIiIju7m71W0mS9Pf3d3R0pGZzUMKkfrtM5QfFpL5Uw4CERNJxXIWGhtra2hryE0m1v+uKigoWi0WVs6Jdb2/v/PnzMzMz6Q6kv5aWFh6Pd+LECXVWNqnfLrhkB4CRYexc0WKxOD4+Pj4+Hj/HQ6/e3t7c3Nz29nYGznkfGxs7ffp0iURCdyCMAwkJAKAzUVFRa9asCQ4OVmd0g14VFhZevny5oKBA+aNRhpecnFxaWpqfn89ms+mOhXEgIQFgNA4cOJCVlfX69WsXF5dLly7RHc7gEhMTJRLJsWPH6A3D19f33Llz1Mx+DHHlypU3b94UFhba2NjQHQsTsegOAACgrqNHjx49epTuKFTz8/Pz8/OjOwomWrFixYoVK+iOgrngDAkAAAAjQEICAADACJCQAAAAMAIkJAAAAIxgQoManj17dvHiRbqjGDnw/JUm3qV4ApgR3wnwXdPo2bNnTJg810DofjLXQFavXk13TwMAgCZMZ6YGgoSi7gBoBxcOhxMIALQE95AAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAiQkAAAAjAAJCQAAACNAQgIAAMAIkJAAAAAwAkGSJN0xAGBkzp07l5mZ2dfXh18+ffoUIeTi4oJfmpmZ/fd///eGDRtoiw8A4wQJCYBhKysrmzZtmpIV7t27N3XqVIPFA8DIAAkJAE1MnDjx0aNHgzaJxeKKigoDxwPACAD3kADQxKZNm9hs9sDlbDZ7y5Ytho8HgBEAzpAA0ERVVZVYLB70v09FRYVYLDZ8SAAYOzhDAkATrq6uXl5eBEEoLiQIYsaMGZCNANAMJCQANLR582Zzc3PFJebm5ps3b6YrHgCMHVyyA0BDTU1NDg4O1OBvhJCZmVl9ff2YMWNojAoA4wVnSABoyN7efsGCBdRJkrm5+cKFCyEbAaAxSEgAaG7Tpk2K1xg2bdpEYzAAGDu4ZAeA5tra2uzs7GQyGUKIzWY3NTVZW1vTHRQAxgrOkADQnEgk+uCDD1gsFovFWrp0KWQjALQBCQkArWzcuLG3t7e3txcmrwNAS3DJDgCtdHd3jx49miTJlpYWPp9PdzgAGDFISH/R7zlHAADQK/gFVsSiOwDGCQsL8/b2pjsK45OSkoIQ2rNnD92B6EZRUVFqauqFCxfUWbm0tJQgCOXzfzNWUFAQHPO0wMcY3VEwC5wh/QVBEBcuXFi7di3dgRifNWvWIISys7PpDkQ3Ll68GBQUpOb/jp6eHoQQi2WUf97BMU+XYR1jJsIo/wsBwChGmooAYBoYZQcAAIARICEBAABgBEhIAAAAGAESEgAAAEaAhASAzuTn51tZWX3//fd0B6IvN27ciIqKunz5squrK0EQBEH0m0/Wz89PKBSam5tPnjz57t27dMWJEOrr60tJSfHx8RnYJJfLjx49KhaLORyOtbW1p6dndXU11Xr79u25c+cKBAIHB4fIyMg3b94ovneo1qtXrx4/fry3t1ef+zTyQUICQGdG9hDew4cPp6WlHThwYNWqVVVVVW5ubqNGjTp79mxeXh61zvXr17Ozs5ctW1ZeXu7l5UVXqBUVFe+//354eLhUKh3YGhQU9M0335w7d04qlT58+NDNza2jowM3lZeX+/n5+fr6Njc35+TkfP311zt27KDeqKR1+fLlPB7P19e3tbXVADs4YpFAAULowoULdEdhlFavXr169Wq6o9AZ/Egs3VEMSSqVent762RTah7zx44dmzBhQldXF7XEzc3t3LlzZmZmjo6Ora2t1PKCgoIVK1boJDbNlJaWrly58uzZs9OnT582bVq/1vPnzxMEUVZWNuh7g4KCXFxc+vr68MukpCSCIB4+fKhOK0mSEonE29tbLperEyfDjzFawBkSAMYnMzOzqanJYB/35MmT6OjouLg4Ho+nuNzHxycsLOz58+f79u0zWDAqTZs27fLlyxs2bOByuQNbv/zySy8vrylTpgxs6unpycvLW7BgATWF2JIlS0iSvHLlispWLDY2trS0FOZf0BgkJAB04/bt205OTgRBfPHFFwihjIwMCwsLgUBw5cqVJUuWiESicePGnT9/Hq+clpbG4/Hs7e23b9/u4ODA4/F8fHzu3LmDWyUSCYfDGTt2LH750UcfWVhYEATR0tKCEAoLC9u7d29lZSVBEGKxGCF07do1kUiUmJiop11LS0sjSXL58uUDmxISEiZMmPDVV1/duHFj0PeSJJmcnDxp0iQul2tjYxMQEPD777/jJuVdhBDq7e2NiYlxcnLi8/lTp05VcyYnJWQyWXFx8fTp0wdtraqq6ujocHJyopa4ubkhhMrKylS2YjY2NgsWLEhNTSVH9MVb/YGEBIBuzJs37+eff6Ze7ty5c8+ePV1dXUKh8MKFC5WVla6urtu2bZPL5QghiUQSEhIilUp3795dXV199+7dnp6exYsX19XVIYTS0tIU5/JJT0+Pi4ujXqampi5btszNzY0kySdPniCE8L30vr4+Pe1aXl6eh4eHQCAY2MTn80+fPm1mZrZt27bOzs6BK8TGxkZFRR08eLCpqenWrVt1dXXz589vbGxEqroIIbR///5PP/00JSXlxYsXy5YtW79+/b///W9tdqS+vl4mk/3666+LFi3CfwdMmjQpPT0d54+GhgaEkFAopNbn8Xh8Ph9Hq7yV8u677z5//vzevXvaxGmyICEBoF8+Pj4ikcjOzi44OLizs7O2tpZqYrFY+NThnXfeycjIaG9vz8rK0uAj/P3929raoqOjdRf1f3R2dj59+hSfDQzK29t7z5491dXV+/fv79fU1dWVnJy8cuXKjRs3WllZTZky5eTJky0tLadOnVJcbdAu6u7uzsjICAwMXLVqlbW19aFDh9hstmb9Q8GDF+zs7BITE8vLyxsbGwMCAnbt2vXtt98ihPCQOXNzc8W3sNnsrq4ula0Ud3d3hND9+/e1idNkQUICwEA4HA5CiPrzv58ZM2YIBALqchZzNDU1kSQ56OkRJSEhwcPDIz09/fbt24rLy8vLOzo6ZsyYQS2ZOXMmh8OhLk72o9hFjx49kkqlnp6euInP548dO1bL/sF3lSZPnuzj42Nra2tlZRUXF2dlZYUTJL5DhqfKpchkMlzmSnkrBXdUv9MmoCZISAAwBZfLbW5upjuK/rq7u9GfP+VD4fF4WVlZBEFs3bpV8YwBj4G2tLRUXNna2rq9vV3l5+ILgIcOHSL+VFNTM+gwbvU5ODgghPCtOIzD4Tg7O1dWViKE8E27trY2qlUqlXZ3d+N3KW+l4PyEOw0MFyQkABhBLpe3traOGzeO7kD6w7+wKh/59Pb2Dg8Pr6ioOHLkCLXQ2toaIdQv/ai5m3Z2dgihlJQUxWHBRUVFGuwCxdLS0t3d/cGDB4oLe3p6rKysEEIuLi5CobCmpoZqwrfopk6dqrKVIpPJ0J+dBoYLEhIAjFBYWEiS5Jw5c/BLFos11MU9A7O3tycI4vXr1yrXPHLkyMSJE0tKSqglnp6elpaWiiMR7ty5I5PJ3nvvPZVbGz9+PI/HKy0t1SzsoQQFBZWUlFRVVeGXUqm0pqYGjwJnsVhLly69desWNTykoKCAIAg8vFB5KwV31JgxY3QbtomAhAQAbfr6+l69etXT01NWVhYWFubk5BQSEoKbxGLxH3/8kZubK5fLm5ubFf8wRwjZ2trW19dXV1e3t7fL5fKCggL9DfsWCASurq7Pnj1TuSa+cKd425/H4+3duzcnJ+fs2bNtbW3379/fsWOHg4NDaGioOlvbsmXL+fPnMzIy2traent7nz179uLFC4RQcHDwmDFjNJuaKDw83NnZOSQkpLa29uXLl5GRkV1dXdRwjOjo6MbGxsOHD3d2dhYVFSUlJYWEhHh4eKjTiuGOGvQ5J6CagR/EZTgEMzVoCmZq+Pzzz/FtBoFAsHz58vT0dHx/293dvbKy8tSpUyKRCCHk7Oz8+PFjkiRDQ0PZbLajoyOLxRKJRAEBAZWVldTWXr58uWjRIh6P5+Li8vHHH0dERCCExGJxbW0tSZJ37951dnbm8/nz5s1raGjIz88XCoUJCQka7Kk6x7xEImGz2VKpFL/MycnBg+5Gjx69a9eufitHREQoztTQ19eXlJTk7u7OZrNtbGwCAwMfPXqEm1R20Zs3byIjI52cnFgslp2d3apVq8rLy0mSDAwMRAjFxMQMGm1RUdHcuXOpWztjx4718fG5efMmtUJdXd26detsbGy4XO6sWbMKCgoU337z5s1Zs2ZxuVwHB4eIiIju7m71W0mS9Pf3d3R0pGZzUAJmahgIuuMvICFpDBLScIWGhtra2ur1I9ShzjFfUVHBYrHOnDljmJBU6u3tnT9/fmZmJt2B9NfS0sLj8U6cOKHOypCQBoJLdgDQxlgmhxaLxfHx8fHx8dQkpDTq7e3Nzc1tb28PDg6mO5b+YmNjp0+fLpFI6A7EWEFC0sqHH34oFAoJgtD5rVctDTX3/vHjxydOnMjn8y0sLCZOnBgdHa04jBWpmntfS4plCzAOh2Nvb79w4cKkpKRXr17p8LOAbkVFRa1ZsyY4OFid0Q16VVhYePny5YKCAuWPRhlecnJyaWlpfn4+m82mOxajRfcpGrOg4V+yw1NvlZSU6CkkDTx+/Hju3LkIoYFTHfv7+584caKpqam9vf3ixYtsNnvx4sVU62+//cbn86Ojozs6On7++efRo0dv2bJFzQ9V/5Kdm5ublZUVSZL4lv5PP/0UEhJCEISDg8Mvv/yi5sfpm74vp0RFReGHQN9+++3s7Gz9fZBKwzrm//nPf0ZGRuo1HiOVm5t79OjRnp4e9d8Cl+wGgu74ixGQkJTPvR8YGKhYQWDNmjUIofr6evxS5ez6SmiQkBRlZ2ebmZnZ29srFjKgken8WGhwzAOdMJ1jTH1wyU5b1Fz0DKF87v2cnBzFCgKOjo7ozwm+1JldX39Wr14dEhLS1NR08uRJA3wcAICBICENG0mSSUlJHh4eXC7XysoKj8elDDphvspp9vFYUoFAIBKJpkyZgu/r6Hzu/YEqKiqsra2dnZ2RerPr6xV+BKegoAC/NK6eBADoAN2naMyC1Lh8cfDgQYIgPvvss1evXkml0vT0dKRwyW7fvn1cLvfSpUuvXr06cOCAmZkZvi9y8OBBhND//d//vX79uqmpaf78+RYWFjKZjCTJjo4OkUh0/Pjxrq6uhoaGlStXNjc3K9mUmmbPnj3wkh0mk8mePXv2+eefc7lcaizvzZs3EUJJSUmKa/L5fF9fX3U+TstLdiRJ4uQxfvx4/JLenjSdyynqHPNAH0znGFMfdMdfqPzPKZVKBQKB4kAAxXtIXV1dAoEgODiYWpnL5e7cuZP882eUun+D09iTJ09Ikvztt98QQj/88IPiBynZlJqUJCQ8r8moUaP+53/+B/+UkyR5/fp1hFBycrLimiKRyMfHR52P0z4hkSRJEIS1tTXJgJ40nR8LSEh0MZ1jTH0sQ56NjQBPnjyRSqW+vr6Dtqo/Yb7iNPuurq729vYbN27cvXt3SEjI22+/PaxNaaCurq61tbWkpCQqKurUqVM//vijvb29mrPr609nZydJkvhZfYb05MWLF3WwY4yn5YylQDPQ7YOgOyMyC1L112J+fj5CSPERccUzpH/9618De3jOnDnkgL/r//GPfyCEqAFsv/3229/+9jcWi0UQRFBQkFQqVbIpNSk5Q6I8fvwYIbR7927q34cOHaJa8fz/GzduVOfjtD9DwlOT+fn5kQzoSbjPBAxDnf8ypgMGNQwPPo0Y6nFRjSfMnzx58vfff19fXx8ZGXnhwoUTJ07oY+79gcRisbm5eXl5OVJ7dn39uXbtGkJoyZIliDE9qb//eMyB4JIdTeCPnoEgIQ2Pp6enmZkZvv8/kGYT5tfX1+MCLXZ2dseOHfPy8nrw4IE+5t5/+fLl+vXrFZdUVFT09vaOHz8eqT27vp40NDSkpKSMGzdu69atiPE9CQDQB0hIw4OnHL506VJmZmZbW1tZWRkufowpmTBfifr6+u3bt//+++8ymaykpKSmpmbOnDmabUo5CwuL69ev//jjj21tbXK5vKSk5O9//7uFhUV4eDheQZ3Z9XWCJMmOjg78BG5zc/OFCxfmzp1rbm6em5uL7yExvCcBAHpB92krsyA1Ll+0t7d/+OGHo0aNsrS0nDdvXkxMDEJo3Lhx9+7dI4eYMF/5NPvV1dU+Pj42Njbm5uZvvfXWwYMH8QQkQ829r5zyufeXL1/u4uJiaWnJ5XLd3NyCg4Pv37+v+HaVs+sPRZ17SFevXp06dapAIOBwOGZmZgghPKxu1qxZ8fHxL1++VFyZ3p40nRFQ6hzzQB9M5xhTH0GSJB15kKEIgrhw4cLatWvpDsT44FmIsrOz6Q5ENy5evBgUFGQK/zvgmKeL6Rxj6oNLdgAAABgBEpIx+f3334mhMbA8DAAAqA8SkjGZOHGiksuv3333Hd0BApN248aNqKgoxapXmzZtUlzBz89PKBSam5tPnjwZP3ZGl6EKhqGhS4JdvXr1+PHjxlJT0UhBQgIA6MDhw4fT0tIOHDiwatWqqqoqNze3UaNGnT17Ni8vj1rn+vXr2dnZy5YtKy8v9/LyoivUioqK999/Pzw8XCqV9msqLy/38/Pz9fVtbm7Oycn5+uuvd+zYgZuWL1/O4/F8fX1bW1sNHrKpgIQEAD26uroG/Qud3k1p5pNPPvnuu+8uXrwoFAqphWlpaWZmZqGhobQXmVV07969/fv379ixY/r06QNbjxw5Mnbs2Li4OAsLC29v78jIyNOnT1MTTe3evXvatGlLly7tN8MW0BVISADQIzMzs6mpiWmb0sCTJ0+io6Pj4uIUS20hhHx8fMLCwp4/f75v3z66YhtIScEwdUqCxcbGlpaWpqamGi5iUwIJCQDNkSSZnJw8adIkLpdrY2MTEBBA/TUtkUg4HM7YsWPxy48++sjCwoIgiJaWFoRQWFjY3r17KysrCYIQi8VpaWk8Hs/e3n779u0ODg48Hs/Hx+fOnTsabAohdO3aNZFIlJiYaJhOSEtLI0ly0Bk9EhISJkyY8NVXX924cWPQ9yrpQJW1r3Re5kqdkmA2NjYLFixITU2F4dp6ofcnnYwKgocENaX+5KpGQc2HFmNiYjgczpkzZ1pbW8vKyry8vEaPHt3Q0IBbN2zYMGbMGGrlpKQkhBAu0USS5KpVq9zc3KjW0NBQCwuLBw8edHd3l5eXz5w5UygU1tbWarCpH374QSgUxsfHq7On2h/zrq6u77zzTr+Fbm5uT58+JUny559/NjMze/vttzs6OkiSLCgoWLFiBbWa8g5UUvuK1EPBMDVLgkVFRSGFEmgagwdjB4IzJAA01NXVlZycvHLlyo0bN1pZWU2ZMuXkyZMtLS2Ks0kNC4vFwucK77zzTkZGRnt7e1ZWlgbb8ff3b2tri46O1iyMYens7Hz69Ck+kxiUt7f3nj17qqur9+/f369JzQ708fERiUR2dnbBwcGdnZ21tbUIoe7u7oyMjMDAwFWrVllbWx86dIjNZmvWXRQ8oM7c3FxxIZvN7urqUlzi7u6OELp//742nwUGBQkJAA2Vl5d3dHTMmDGDWjJz5kwOh0NdatPGjBkzBAKBripg6U9TUxNJkng+p6EkJCR4eHikp6ffvn1bcflwO1Cx9pU+CoapWRIM72xjY6M2nwUGBQkJAA3h4b+WlpaKC62trdvb23WyfS6X29zcrJNN6U93dzdCaOAAAUU8Hi8rK4sgiK1btyqebWjTgbhY16FDh6gHw2tqagYO4x4WfJeura2NWiKVSru7u6mZITGcn/COA92ChASAhqytrRFC/X49W1tbx40bp/3G5XK5rjalV/jXWeXjot7e3uHh4RUVFUeOHKEWatOB+igYpmZJMJlMhv7ccaBbkJAA0JCnp6elpeW///1vasmdO3dkMtl7772HX7JYLHx9SQOFhYUkSc6ZM0f7TemVvb09QRDqPGl05MiRiRMnlpSUUEtUdqAS+ihzpWZJMLyzY8aM0eFHAwwSEgAa4vF4e/fuzcnJOXv2bFtb2/3793fs2OHg4BAaGopXEIvFf/zxR25urlwub25uVvzTGyFka2tbX19fXV3d3t6Ok01fX9+rV696enrKysrCwsKcnJxCQkI02FRBQYHBhn0LBAJXV9dnz56pXBNfuFMcMqCyA5VvbagyV8HBwWPGjNFsaiJ1SoLhnZ0yZYoG2wcq0DO4j6kQDPvWlGkO++7r60tKSnJ3d2ez2TY2NoGBgY8ePaJaX758uWjRIh6P5+Li8vHHH0dERCCExGIxHsx99+5dZ2dnPp8/b968hoaG0NBQNpvt6OjIYrFEIlFAQEBlZaVmm8rPzxcKhQkJCersqfbHvEQiYbPZUqkUv8zJycGD7kaPHr1r165+K0dERCgO+1bSgcprX5FDl7kKDAxECMXExAwarfKCYaQaJcH8/f0dHR1xeUltwLDvgaA7/gISksZMMyHpUGhoqK2trSE/EdP+mK+oqGCxWGfOnNFVSFrq7e2dP39+ZmamPjbe0tLC4/FOnDih/aYgIQ0El+wAYAojnUlaLBbHx8fHx8d3dHTQHQvq7e3Nzc1tb2/XUzWW2NjY6dOnSyQSfWwcQEICAGgrKipqzZo1wcHBtM+jWlhYePny5YKCAuWPRmkmOTm5tLQ0Pz+fzWbrfOMAQUICgAkOHDiQlZX1+vVrFxeXS5cu0R2OJhITEyUSybFjx+gNw9fX99y5c9S8fzp05cqVN2/eFBYW2tjY6HzjAGPRHQAAAB09evTo0aN0R6EtPz8/Pz8/uqPQlxUrVqxYsYLuKEY4OEMCAADACJCQAAAAMAIkJAAAAIwACQkAAAAjECTUPVRAEMScOXOYP6MlAxUXFyOEqLnXjN2zZ8+Ki4tXr15NdyB6d+nSJTjmaYGPMfgFVgQJ6S/WrFlDdwgAABOSnZ1NdwgMAgkJAAAAI8A9JAAAAIwACQkAAAAjQEICAADACJCQAAAAMML/B74phe3KFKhKAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "execution_count": 16 } ] }, { "cell_type": "markdown", "metadata": { "id": "S0I5GDlr77GW" }, "source": [ "The second step of creating a neural network involves specification of the loss function, optimizer and possible validation set metrics." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iJww-DJ9Py2p", "outputId": "7bea9fee-3585-44df-c460-7a802fb5f2e3" }, "source": [ "batch_size = 128 # How many images at once are we considering?\n", "epochs = 3 # How many forward-backward passes are we considering?\n", "\n", "# Model compilation -> this translates the specification above to machine code.\n", "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n", "\n", "# Call to sklearn-like fit function. Additional hyperparameters need to be specified.\n", "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/3\n", "422/422 [==============================] - 13s 12ms/step - loss: 0.3637 - accuracy: 0.8893 - val_loss: 0.0833 - val_accuracy: 0.9770\n", "Epoch 2/3\n", "422/422 [==============================] - 4s 10ms/step - loss: 0.1095 - accuracy: 0.9667 - val_loss: 0.0557 - val_accuracy: 0.9852\n", "Epoch 3/3\n", "422/422 [==============================] - 4s 11ms/step - loss: 0.0837 - accuracy: 0.9744 - val_loss: 0.0470 - val_accuracy: 0.9878\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o9B4MD9oP8WI", "outputId": "419ae78a-ccae-4c6c-a159-a9990b7f606f" }, "source": [ "score = model.evaluate(x_test, y_test, verbose=0)\n", "print(\"Test loss:\", score[0])\n", "print(\"Test accuracy:\", score[1])" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Test loss: 0.044322285801172256\n", "Test accuracy: 0.984499990940094\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "YzsDXXAqQsVT" }, "source": [ "**Problemsets:**\n", "\n", "\n", "1. Try increasing the batch size. What do you observe?\n", "2. Try printing out some examples where the network is wrong. Comment on its mistakes.\n", "\n" ] }, { "cell_type": "markdown", "source": [ "# Transfer learning\n", "\n", "One of the key features of contemporary deep learning models is their _transferability_. Models, (pre)trained on a given task can be used to initialize a _fine tunning_ phase, which commonly consists of only a few epochs of _fine-tuning_ to a given task. This way, an average user does not need grid-scale computing for training the models from scratch, but can jump-start the learning. A powerful tool to master is the [HuggingFace](https://huggingface.co/) library, of which main focus is language modeling." ], "metadata": { "id": "tbmRZwLlYQ1H" } }, { "cell_type": "code", "source": [ "!pip install datasets\n", "!pip install transformers" ], "metadata": { "id": "NUWIyCOkZkWi", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3493cdb0-21e8-40c7-923b-751cbe95ff69" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-1.16.1-py3-none-any.whl (298 kB)\n", "\u001b[K |████████████████████████████████| 298 kB 5.4 MB/s \n", "\u001b[?25hRequirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.4)\n", "Collecting fsspec[http]>=2021.05.0\n", " Downloading fsspec-2021.11.1-py3-none-any.whl (132 kB)\n", "\u001b[K |████████████████████████████████| 132 kB 39.5 MB/s \n", "\u001b[?25hCollecting huggingface-hub<1.0.0,>=0.1.0\n", " 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"print(f\"Approximate average text len (tokens): {average_text_len}\")" ], "metadata": { "id": "9Jjb84R4Zz3v", "colab": { "base_uri": "https://localhost:8080/", "height": 365, "referenced_widgets": [ "7bb6b172b47b49dc835de39fe029e05c", "ec41dd3e7664418891c79c31e8077776", "0374053ff9a44d618f48af423b098cf8", "c92e8c3714c74f71b3dfbe19581fcc37", "b77c555fe7a14af6927705831e9ef662", "d35b48452b494a77bdadcd1b7b14f506", "3130a44e732f4442ab10fc4143826553", "044ebf8df76a4e20ba66a4f83e514956", "01dfe679b3db4045bee62406964fa93b", "a572cff698924e5a8e02fb3374fb6be5", "38007032162844d9b8fbab101c2117a8", "953da0e7063c45f1923f58c29cff3f58", "a731be2239164d4c9b1ed4cced60aeaa", "d3d49c4e7a3447d4ae8a26db3de79797", "047f7849544e4fc59b95322f8b6c553d", "262640aca5f949fbbe07d1b5468fdb61", "11ee51f1be2040cfbb6b1c3bb4608425", "e4f725598b2240c187aaf4f09d1224af", "f8f687ad1db6400ea102bb7266b7c9e2", "621bdc32f7274f779f0784374d8831f3", "c29bc44eafae42cb88c29c202c7477fa", "88a7d69906f44b1189ed47bb8d2a8ba8", "b101aa95a8cf48e3a590088b8e0c2457", "80bdea8d2e934e0cbab982b8b84fac3f", "3f14ff3c278249c0b727f1e3718839da", "13f40c0d6261429cbdb8b38fc8deff60", "9d8de6081a9f4331b7a388a1c14ed0e1", "03ab13f301f841f9a525a9ff65ea2d95", "2d1db78cb1ee4592b90e17615038e1b3", "15ba00df363a47bfabaf215053eefe25", "d53d6f775c4549fe98de675692f0b29c", "0dfa96eddf8a42ddb810cc2edcc347aa", "1a87eada69704bc385b5a7cce543a782", "0718442a057c41f7824f1f29eddbd592", "652201d7282b4535b7815c28ac800ea4", "ed084c48b6914aeb927d703640dac25d", "b3d3820327504c81b0bb66aa37bd92c4", "4d163955f6854cf4abe53bcdb651dfda", "981ebe9a32a54e5684ba076f86508154", "fc186d2da9e04e0795b2418c551011ab", "fc987887cc5f41d9bf67e0aa5f3d44cf", "feec4fb5ded74cc6b522c12e93cc0095", "2ee998c593eb4ee1b75f8b4d4df99ecc", "7f5b6d95b5c24ec68940c9ab0f981e8f", "c8df909954284fc8897bdac4780276fd", "80c54c6f792a42efad6b43701f42863b", "b5c904c6a46c47e0bea44ecf8b98cd2a", "fd868059edde492aab9121c4ae250fde", "b903a3f437ae4e44b9e4c65c8dc7c482", "f2735e63260847b589f688e522455bb6", "84a5f64901ac4c7b80b7ddea74aea1ff", "c93a469e393549c09d92845b1194a7df", "434f0f7510ec49758791910596e9d88d", "9b796eb69927444596d2dee230dac501", "25112ea0db284cab9c3ba1d4be63f3fe", "c8d103f09f42481fbb2533b74f25c559", "d98ff6e6761e49f6b5706543fdcbde0f", "4d4d0e59413f4aa6b3e3545f5f970dfb", "4c5c9eb029c14e45a5be7202047ebb85", "91dc6069ff09469dbd5c52eb6241ec5e", "e60d7b5312424860a1dad08296e7653a", "9c1d32ad09674c1b901165cabd51072c", "663e0b7f869b42a6830b4c24541b184d", "6ba4fb83f06240deb6df0dd19399c7e7", "e3652a33b8434b208a06f388f6adf298", "730a3b6f414b4065a6f4277d31e3bc00", "fa805b641abe4899a04b1734ea67fe10", "c095ae603ab243159de3fd8a9ab1ef7a", "9c875ad0feb8464aa31a924a0b6286f2", "bd0b27afcb9c4a8baca8af68b5aa8d04", "24b0c74a659249859dd1dee43f35f281", "e93323f99e6443bbacd83942df5b9b64", "eb8c93cddc0448ecb6793003673f301d", "974d8c0af60945d7bf5a72ec97c07d0e", "7cae07a53885431da2a2d44a08574584", "28700f3fd7924908b48612600921e6f2", "9a27d40f9768492aaecd8d549f486d60", "e1f2aa7d1d3740fd88cf4658e72cbe42", "a978d3f0f1cc404d98a768e87b0da98d", "6e78978d164f46e897e41ffcc89f239a", "31e06223c361466fa50a839bb6ac5440", "afddd4d0285b4c7397969bd33ec4fe41", "0e1b832b7d5a46fcb09020540906b65e", "0c8744d66fca43cf8cfcf2c96f408d44", "be8eee3a9b2a42948ced18721bdbb846", "97b5fcfd80c24205b6b847b2d23b5d1f", "428fd2de3c23415593e578d40e99a057", "4211c69a469841eabc9d590fff55deb3", "9f70ca1b53e74c429d8a542c1c6f30de", "bc29a4aad8274af2b923c28dc25e60ca", "67b2285e71554595a9a574733f00176e", "0e90e4c8ddb540f1b9d18bb073e76a71", "b28ab25da33f4b57951301f197181f77", "6937a0a138d84e68a421d147f98e4911", "16ee13fdd0ad41b98f6b5f80da7d2695", "50ae22b758a84d7e8516d4549027d6b4", "61107875549549f4a1caafc8c8fc776e", "6d2239ed55be45979b2c72dd0397262e", "976175fd17d34f629df43af52de7af28", "c033086affff4e8295da5f154800f52b", "1dc932d7556d431ab84db0d689b5a9cd", "ae897d11423b44858afc5155aa506e83", "637c82bf022c4380b85d629e0f276288", "e9e812b15def44d7b6c24279ced392e8", "6ff31418069243cdb860953af25fbdcf", "59982e4d0a8b4424acbc85dd7f04606e", "2023486314e74e4cbaa587b8a929e177", "b6b5e146529047839f5abac951dce365", "f98125c238104bc0b81063adad923650", "c620a4c1196042399965004b06301f05" ] }, "outputId": "c2bad49f-edce-4e07-93d0-ff8af0164dc4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7bb6b172b47b49dc835de39fe029e05c", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.38k [00:00\n", " \n", " \n", " [28/28 00:04, Epoch 1/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=28, training_loss=0.8950928960527692, metrics={'train_runtime': 5.0102, 'train_samples_per_second': 178.037, 'train_steps_per_second': 5.589, 'total_flos': 4983228704160.0, 'train_loss': 0.8950928960527692, 'epoch': 1.0})" ] }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "code", "source": [ "from datasets import load_metric\n", "from collections import Counter\n", "\n", "# Let's obtain the predictions first\n", "model_predictions = np.argmax(trainer.predict(tokenized_data[\"test\"])[0], axis=1)\n", "\n", "# Accuracy is in-built with HFTransformers\n", "metric = load_metric(\"accuracy\")\n", "final_score = metric.compute(predictions=model_predictions, references=tokenized_data[\"test\"]['label'])\n", "print(f\"Final test accuracy: {final_score}\")\n", "\n", "\n", "# Did fine-tuning actually work?\n", "train_labels = tokenized_data['train']['label']\n", "counter_labels_train = Counter(train_labels)\n", "most_frequent_label = counter_labels_train.most_common(1)[0][0]\n", "\n", "# Predict \"2\" for each test instance\n", "model_predictions = np.repeat(most_frequent_label, len(model_predictions))\n", "majority_score = metric.compute(predictions=model_predictions, references=tokenized_data[\"test\"]['label'])\n", "print(f\"Majority classifier (label=2): {majority_score}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 173, "referenced_widgets": [ "141ecb76e34b44d28e5d79c0792a3c84", "95cc0902f1e04c0687222925c3e2440f", "e51419de6bc2464fb3e975f91bbbc663", "1a850a336a174604a808e2206672e936", "2eb3dda07de64ab39805ab5aaec0e4c9", "1ce7df913b6a4cb6b0f722fbc15a625e", "3083e4917d9d4c2bb60daea2e49321d8", "59b2d63874f640a4bcdd85fe1627bbaa", "1983b7a1db7a4096831d4c6bb7130d10", "4db92959dbf04e2aa071e694a120e858", "cc57bc2171d5459981f63e49c5ed4131" ] }, "id": "RccfvBTqftVL", "outputId": "72252f3f-6a5b-4cfa-f8be-ff855ee22a80" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: id, verse_text.\n", "***** Running Prediction *****\n", " Num examples = 104\n", " Batch size = 32\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "

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\n", " " ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "141ecb76e34b44d28e5d79c0792a3c84", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.42k [00:00" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "Km-L_YRhSx3t" }, "source": [ "**Problemsets:**\n", "\n", "\n", "1. How does the effect of sequence length impact the performance?\n", "2. Try to speed up the training by selecting different hyperparameters. What do you observe?\n", "\n" ] } ] }