{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "HTML(\"\"\"\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Классификация рукописных цифр с помощью TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/mnist.jpeg)\n", "\n", "Классическая задача распознавания рукописных цифр на основе набора данных [MNIST](http://yann.lecun.com/exdb/mnist/)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Постановка задачи\n", "\n", "- 60_000 изображений с цифрами\n", "- Отцентрированы\n", "- Черно-белые\n", "- Размер 28x28 пикселя" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Простейшая однослойная нейросеть" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![](images/one_layer.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Изображение цифры, размером 28x28 уплощается в вектор размера 784 пикселей, который подается на вход однослойной нейросети. Каждый нейрон по отдельности проивзодит взвешенную сумму всех входных сигналов, добавляет некоторый bias-порог и пропускает результат через некоторую функцию активации." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/perceptron_schematic.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Функция перцептрона\n", "\n", "$$Y = g(X.W + b) $$\n", "\n", "$$sigmoid(z_n) = \\frac{1}{1 + e^{z_n}} $$\n", "\n", "$$softmax(z_n) = \\frac{e^{z_n}}{||{e^z}||} $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/activation_functions.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Так как функции активации по определению возвращают числа от 0 до 1, то мы получаем, что фактически наш перцептрон возвращает вероятности принадлежности к классу." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/eval_single_layer.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/softmax-regression-vectorequation.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Тренировка сети\n", "\n", "$$H_{y'}(y) = - \\sum{y'_i log(y_i)}$$\n", "\n", "y - оригинальный вектор\n", "\n", "y' - предсказанный вектор" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/cross_entropy.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Практика 0\n", "\n", "- импортировать TF\n", "- переменные, константы, подстановки в TF\n", "- запуск вычислений" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import tensorflow as tf\n", "tf.set_random_seed(0)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Создание констант" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"Const:0\", shape=(), dtype=float32) Tensor(\"Const_1:0\", shape=(), dtype=float32)\n" ] } ], "source": [ "node1 = tf.constant(3.0)\n", "node2 = tf.constant(4.0)\n", "print(node1, node2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Запуск вычислений" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3.0, 4.0]\n" ] } ], "source": [ "sess = tf.InteractiveSession()\n", "print(sess.run([node1, node2]))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "node3: Tensor(\"Add:0\", shape=(), dtype=float32)\n", "sess.run(node3): 7.0\n" ] } ], "source": [ "node3 = tf.add(node1, node2)\n", "print(\"node3:\", node3)\n", "print(\"sess.run(node3):\", sess.run(node3))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Создание подстановок" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "a = tf.placeholder(tf.float32)\n", "b = tf.placeholder(tf.float32)\n", "adder_node = a + b # переопределенный оператор для tf.add" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.5\n", "[ 3. 7.]\n" ] } ], "source": [ "print(sess.run(adder_node, {a: 3, b: 4.5}))\n", "print(sess.run(adder_node, {a: [1, 3], b: [2, 4]}))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Создание переменных" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"add_1:0\", dtype=float32)\n" ] } ], "source": [ "W = tf.Variable([.3], dtype=tf.float32)\n", "b = tf.Variable([-.3], dtype=tf.float32)\n", "x = tf.placeholder(tf.float32)\n", "linear_model = W * x + b\n", "print(linear_model)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.30000001 0.60000002 0.90000004]\n" ] } ], "source": [ "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "print(sess.run(linear_model, {x: [1, 2, 3, 4]}))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "23.66\n" ] } ], "source": [ "y = tf.placeholder(tf.float32)\n", "squared_deltas = tf.square(linear_model - y)\n", "loss = tf.reduce_sum(squared_deltas) # функция потерь - или ошибки\n", "print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "# Поправим вручную потери линейного алгоритма.\n", "fixW = tf.assign(W, [-1.])\n", "fixb = tf.assign(b, [1.])\n", "sess.run([fixW, fixb])\n", "print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Практика 1\n", "\n", "- импортировать TF\n", "- загрузить dataset\n", "- описать структуру сети\n", "- запустить обучение" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Формулы для реализации\n", "\n", "$$Y = softmax(X.W + b) $$\n", "\n", "$$softmax(z_n) = \\frac{e^{z_n}}{||{e^z}||} $$\n", "\n", "$$H_{y'}(y) = - \\sum{y'_i log(y_i)}$$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "scrolled": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# reset jupyter kernell to avoid variables duplications\n", "%reset -sf\n", "\n", "import tensorflow as tf\n", "tf.set_random_seed(0)\n", "tf.reset_default_graph()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with tf.name_scope('X'):\n", " X = tf.placeholder(tf.float32, [None, 784], name='X')\n", " x_image = tf.reshape(X, [-1, 28, 28, 1])\n", "with tf.name_scope('weights'):\n", " W = tf.Variable(tf.zeros([784, 10]), name='weights')\n", "with tf.name_scope('biases'):\n", " b = tf.Variable(tf.zeros([10]), name='biases')\n", " \n", "tf.summary.image('input', x_image, 3)\n", "tf.summary.histogram(\"weights\", W)\n", "tf.summary.histogram(\"biases\", b)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with tf.name_scope('Wx_plus_b'):\n", " # Модель Y = X.W + b\n", " Y = tf.nn.softmax(tf.matmul(tf.reshape(X, [-1, 784]), W) + b, name='labels')\n", "\n", "# Подстановка для корректных значений входных данных\n", "Y_ = tf.placeholder(tf.float32, [None, 10], name='Y_')\n", "\n", "with tf.name_scope('total'):\n", " # Функция потерь H = Sum(Y_ * log(Y))\n", " cross_entropy = -tf.reduce_sum(Y_ * tf.log(Y), name='xentropy')\n", "\n", "with tf.name_scope('accuracy'):\n", " with tf.name_scope('correct_prediction'):\n", " # Доля верных ответов найденных в наборе\n", " is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))\n", " with tf.name_scope('accuracy'):\n", " accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32), name='xentropy_mean')\n", "\n", "tf.summary.scalar('accuracy', accuracy)\n", "tf.summary.scalar('cross_entropy', cross_entropy)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "with tf.name_scope('train'):\n", " # Оптимизируем функцию потерь меотодом градиентного спуска\n", " # 0.003 - это шаг градиента, гиперпараметр\n", " optimizer = tf.train.GradientDescentOptimizer(0.003)\n", " # Минимизируем потери\n", " train_step = optimizer.minimize(cross_entropy)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Загружаем тренировочный датасет MNIST\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True, reshape=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def save_summary(sess, mnist, writer, merged, accuracy, i):\n", " if i % 10 == 0:\n", " test_data={X: mnist.test.images, Y_: mnist.test.labels}\n", " summary, a = sess.run([merged, accuracy], feed_dict=test_data)\n", " writer.add_summary(summary, i)\n", " if i % 200 == 0:\n", " print(\"Test: {}\".format(a))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.4043999910354614\n", "Test: 0.8966000080108643\n", "Test: 0.9027000069618225\n", "Test: 0.9115999937057495\n", "Test: 0.9158999919891357\n" ] } ], "source": [ "with tf.Session() as sess:\n", " merged = tf.summary.merge_all() # Merge all the summaries and write them out to\n", " writer = tf.summary.FileWriter(\"/tmp/tensorflow/one_layer\", sess.graph)\n", " tf.global_variables_initializer().run()\n", "\n", " for i in range(1000):\n", " # загружаем набор изображений и меток классов\n", " batch_X, batch_Y = mnist.train.next_batch(100)\n", " train_data={X: batch_X, Y_: batch_Y}\n", "\n", " # train\n", " sess.run(train_step, feed_dict=train_data)\n", " \n", " save_summary(sess, mnist, writer, merged, accuracy, i)\n", " \n", " writer.close()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[36mone_layer\u001b[m\u001b[m \u001b[1m\u001b[36mone_layer.log\u001b[m\u001b[m\r\n" ] } ], "source": [ "!ls /tmp/tensorflow/" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def single_layer(mnist):\n", " X = tf.placeholder(tf.float32, [None, 784], name='X')\n", " W = tf.Variable(tf.zeros([784, 10]), name='weights')\n", " b = tf.Variable(tf.zeros([10]), name='biases')\n", "\n", " Y = tf.nn.softmax(tf.matmul(tf.reshape(X, [-1, 784]), W) + b, name='labels')\n", " Y_ = tf.placeholder(tf.float32, [None, 10], name='Y_')\n", "\n", " cross_entropy = -tf.reduce_sum(Y_ * tf.log(Y), name='xentropy')\n", " is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))\n", " accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32), name='xentropy_mean')\n", "\n", " optimizer = tf.train.GradientDescentOptimizer(0.003)\n", " train_step = optimizer.minimize(cross_entropy)\n", "\n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", " for i in range(1000):\n", " batch_X, batch_Y = mnist.train.next_batch(100)\n", " train_data={X: batch_X, Y_: batch_Y}\n", "\n", " # train\n", " sess.run(train_step, feed_dict=train_data)\n", "\n", " if i % 100 == 0:\n", " test_data={X: mnist.test.images, Y_: mnist.test.labels}\n", " a, c = sess.run([accuracy, cross_entropy], feed_dict=test_data)\n", " print(\"Test: {}, {}\".format(a, c))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.5440999865531921, 20443.087890625\n", "Test: 0.8896999955177307, 4244.2392578125\n", "Test: 0.8974999785423279, 3746.39013671875\n", "Test: 0.9070000052452087, 3413.471435546875\n", "Test: 0.9103000164031982, 3245.257080078125\n", "Test: 0.9106000065803528, 3193.984375\n", "Test: 0.9143000245094299, 3084.836669921875\n", "Test: 0.9118000268936157, 3124.13232421875\n", "Test: 0.9151999950408936, 3025.076416015625\n", "Test: 0.9143999814987183, 3015.42431640625\n" ] } ], "source": [ "single_layer(mnist)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 0.91\n", "\n", "![](images/tiny_success.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/go_deeper.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Практика 2\n", "\n", "- Многослойная нейросеть\n", "- проблема переобучения\n", "- регуляризация" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Многослойная сеть\n", "\n", "![](images/deep_neural_network.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/deep_mnist.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "$$sigmoid(z_n) = \\frac{1}{1 + e^{z_n}} $$\n", "\n", "![](images/sigmoid.png)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# reset jupyter kernell to avoid variables duplications\n", "%reset -sf\n", "\n", "import math\n", "import tensorflow as tf\n", "tf.set_random_seed(0)\n", "tf.reset_default_graph()\n", "\n", "# Загружаем тренировочный датасет MNIST\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True, reshape=True)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "def draw(a, c):\n", " plt.figure(1, (15, 5))\n", " plt.subplot(121)\n", " plt.title('accuracy')\n", " plt.plot(a)\n", "\n", " plt.subplot(122)\n", " plt.title('cross_entropy')\n", " plt.plot(c)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# Объявим размеры слоёв\n", "\n", "layer_sizes = [28*28, 200, 100, 60, 30, 10]\n", "\n", "W1 = tf.Variable(tf.truncated_normal([layer_sizes[0], layer_sizes[1]], stddev=0.1))\n", "B1 = tf.Variable(tf.zeros(layer_sizes[1])) # 200\n", "\n", "W2 = tf.Variable(tf.truncated_normal([layer_sizes[1], layer_sizes[2]], stddev=0.1))\n", "B2 = tf.Variable(tf.zeros(layer_sizes[2])) # 100\n", "\n", "W3 = tf.Variable(tf.truncated_normal([layer_sizes[2], layer_sizes[3]], stddev=0.1))\n", "B3 = tf.Variable(tf.zeros(layer_sizes[3])) # 60\n", "\n", "W4 = tf.Variable(tf.truncated_normal([layer_sizes[3], layer_sizes[4]], stddev=0.1))\n", "B4 = tf.Variable(tf.zeros(layer_sizes[4])) # 30\n", "\n", "W5 = tf.Variable(tf.truncated_normal([layer_sizes[4], layer_sizes[5]], stddev=0.1))\n", "B5 = tf.Variable(tf.zeros(layer_sizes[5])) # 10" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "X = tf.placeholder(tf.float32, [None, 784])\n", "\n", "Y1 = tf.nn.sigmoid(tf.matmul(X, W1) + B1)\n", "Y2 = tf.nn.sigmoid(tf.matmul(Y1, W2) + B2)\n", "Y3 = tf.nn.sigmoid(tf.matmul(Y2, W3) + B3)\n", "Y4 = tf.nn.sigmoid(tf.matmul(Y3, W4) + B4)\n", "\n", "Ylogits = tf.matmul(Y4, W5) + B5\n", "Y = tf.nn.softmax(Ylogits)\n", "Y_ = tf.placeholder(tf.float32, [None, 10])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def train_deep(X, Y, Ylogits, Y_, epoches=1000):\n", " cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)\n", " cross_entropy = tf.reduce_mean(cross_entropy)*100\n", " \n", " is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))\n", " accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n", "\n", " learning_rate=0.003\n", " optimizer = tf.train.AdamOptimizer(learning_rate)\n", " train_step = optimizer.minimize(cross_entropy)\n", "\n", " accuracy_vis, cross_entropy_vis = [], []\n", " \n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", " for i in range(epoches):\n", " batch_X, batch_Y = mnist.train.next_batch(100)\n", " train_data={X: batch_X, Y_: batch_Y}\n", "\n", " # train\n", " sess.run(train_step, feed_dict=train_data)\n", "\n", " if i % 10 == 0:\n", " test_data={X: mnist.test.images, Y_: mnist.test.labels}\n", " a, c = sess.run([accuracy, cross_entropy], feed_dict=test_data)\n", " accuracy_vis.append(a)\n", " cross_entropy_vis.append(c)\n", " if i % 100 == 0:\n", " print(\"Test: {}, {}\".format(a, c))\n", " return (accuracy_vis, cross_entropy_vis)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.08919999748468399, 232.40162658691406\n", "Test: 0.4142000079154968, 149.98367309570312\n", "Test: 0.5758000016212463, 107.17176055908203\n", "Test: 0.767300009727478, 78.7203369140625\n", "Test: 0.8797000050544739, 53.51210403442383\n", "Test: 0.9056000113487244, 39.718955993652344\n", "Test: 0.925599992275238, 31.1849365234375\n", "Test: 0.9391000270843506, 24.913511276245117\n", "Test: 0.9437999725341797, 22.77108383178711\n", "Test: 0.9538000226020813, 18.920372009277344\n" ] } ], "source": [ "a, c = train_deep(X, Y, Ylogits, Y_)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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1M39LhddRRETCjkqbdIrlRdU0tPiYPijT6ygiItIFjh+eQ3x0JK+uKPE6iohI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\nQqaXZVBZMgKfx9hY38amhjZW1DTxnRe28OOlNXx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw(a,c)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 0.94???\n", "\n", "![](images/fry.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### sigmoid + softmax + 5 layers\n", "- Качество классификации: 0.94\n", "- Медленно сходится\n", "- Есть резкие перепады при обучении\n", "- Нужна другая функция активации" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Relu = Rectified Linear Unit\n", "\n", "![](images/relu.png)\n", "\n", "```python\n", "tf.nn.relu(tf.matmul(X,W1)+B1)\n", "```" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "X = tf.placeholder(tf.float32, [None, 784])\n", "\n", "Y1 = tf.nn.relu(tf.matmul(X, W1) + B1)\n", "Y2 = tf.nn.relu(tf.matmul(Y1, W2) + B2)\n", "Y3 = tf.nn.relu(tf.matmul(Y2, W3) + B3)\n", "Y4 = tf.nn.relu(tf.matmul(Y3, W4) + B4)\n", "\n", "Ylogits = tf.matmul(Y4, W5) + B5\n", "Y = tf.nn.softmax(Ylogits)\n", "Y_ = tf.placeholder(tf.float32, [None, 10])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.1647000014781952, 226.0742645263672\n", "Test: 0.9204999804496765, 26.77494239807129\n", "Test: 0.9391999840736389, 20.49483871459961\n", "Test: 0.953000009059906, 15.976889610290527\n", "Test: 0.9564999938011169, 13.962605476379395\n", "Test: 0.9606000185012817, 12.418688774108887\n", "Test: 0.9613999724388123, 12.344167709350586\n", "Test: 0.9634000062942505, 12.622851371765137\n", "Test: 0.960099995136261, 13.433085441589355\n", "Test: 0.9661999940872192, 11.935070991516113\n" ] } ], "source": [ "a, c = train_deep(X, Y, Ylogits, Y_)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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N/WMhFy2s423HTgFg/tRxhSxPRERkVJVEwwC6V5uISBEoymvakukMN/9xNcdP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\nM7MNZrbPzJrM7J5MxyMiItOLmjYREZEpZGYe4D7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw(a,c)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### relu + softmax + 5 layers\n", "\n", "- качество классификации 0.97\n", "- быстро сходится\n", "- относительно стабильно??" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/fry.png)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.14419999718666077, 228.2206573486328\n", "Test: 0.9193000197410583, 26.626399993896484\n", "Test: 0.921999990940094, 26.062955856323242\n", "Test: 0.9225000143051147, 24.187725067138672\n", "Test: 0.9524000287055969, 15.917776107788086\n", "Test: 0.9563999772071838, 13.650592803955078\n", "Test: 0.9650999903678894, 11.619589805603027\n", "Test: 0.9623000025749207, 12.328930854797363\n", "Test: 0.9704999923706055, 9.90969467163086\n", "Test: 0.968500018119812, 10.409608840942383\n", "Test: 0.9639000296592712, 11.868613243103027\n", "Test: 0.9728000164031982, 9.583634376525879\n", "Test: 0.9699000120162964, 10.106823921203613\n", "Test: 0.9704999923706055, 9.33514404296875\n", "Test: 0.9678999781608582, 10.709968566894531\n", "Test: 0.9690999984741211, 10.103565216064453\n", "Test: 0.9688000082969666, 10.650016784667969\n", "Test: 0.9746999740600586, 8.89111042022705\n", "Test: 0.9740999937057495, 9.634437561035156\n", "Test: 0.9733999967575073, 9.40545654296875\n", "Test: 0.9735999703407288, 9.46670150756836\n", "Test: 0.9736999869346619, 9.855304718017578\n", "Test: 0.9754999876022339, 8.992805480957031\n", "Test: 0.9771999716758728, 8.519488334655762\n", "Test: 0.9753999710083008, 8.675346374511719\n", "Test: 0.9731000065803528, 9.259878158569336\n", "Test: 0.9794999957084656, 7.672606945037842\n", "Test: 0.9761000275611877, 8.738158226013184\n", "Test: 0.9753999710083008, 8.693212509155273\n", "Test: 0.9758999943733215, 8.78188705444336\n", "Test: 0.977400004863739, 8.437352180480957\n", "Test: 0.9747999906539917, 9.55250358581543\n", "Test: 0.9753999710083008, 10.29079532623291\n", "Test: 0.9758999943733215, 9.100419998168945\n", "Test: 0.9629999995231628, 13.843968391418457\n", "Test: 0.9745000004768372, 8.774469375610352\n", "Test: 0.9771999716758728, 8.424749374389648\n", "Test: 0.9776999950408936, 8.575775146484375\n", "Test: 0.9783999919891357, 8.854340553283691\n", "Test: 0.9786999821662903, 8.306488037109375\n", "Test: 0.9772999882698059, 8.445571899414062\n", "Test: 0.9793999791145325, 7.498820781707764\n", "Test: 0.9757000207901001, 9.733086585998535\n", "Test: 0.9760000109672546, 9.674837112426758\n", "Test: 0.9753999710083008, 9.854654312133789\n", "Test: 0.9768999814987183, 8.75963306427002\n", "Test: 0.977400004863739, 9.908486366271973\n", "Test: 0.9761000275611877, 9.34334659576416\n", "Test: 0.9782999753952026, 9.207597732543945\n", "Test: 0.9750999808311462, 10.171501159667969\n" ] } ], "source": [ "a, c = train_deep(X, Y, Ylogits, Y_, 5000)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Шумы в кривой точности" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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5SV+xeT/W7xkK7ktz5qTrprdtSEe4hmXFWGTopve/FL0be4ZV83fO0Zt66N9R\n9YZujkeYZbiQFcMwDJMcFnrMpGM7AlfetQ6DXnl2mU82no5eoQGO3nk/uB+vv/KBisv86y8exTt/\n8agKS1x2+bk49uBO1ZJAFzf1FGNJa87TQRVy9EaKNo7y3C4gOnRT5nut2ekKPdmuIcxda/bgwzc8\nripUZlOVQzelaGnNmGoyKQWZGRO62RYqxlJKMIFsSZtKqJRsB/c8uwd/18RcUrfBESJwXiW6qNKF\nXjZlwjQIJdtRE+QooSfP/11r9gS3a7mO3rHzO/HqY+ehPZuqGrp56uJudLem8dzuIdy1JihY5a6v\nuns9Ht/SF3hPd419R88XsFFCL+zoSdFXsIM5ev2jJdUGYy/n6E059O+NWl002TB9PNw3dvQYhmHq\nh4UeM+ncsXoXvve35/CNpcFcsLDQGy3a+PhNT2B7lZ5qSYgqYqHz1b88g2UVWi8kbf2wZqdbml8W\nC5HhlZ1euFyf1gx8pA7xqTtPqo+e5WD1jn5c9vuV6gn7cMHC7PYsNn3z9XjLyQsqOnpyzHGTdSnI\n942472erOHqybUQubZbl6JkUHbrZmjEDDtaP7l6Pv6zcEdhu2D3IZUx1PixH4D2/fEzl7gHJC/A4\nDiIdPd3V0t2PbMpAyjDU+cuYBhxRfo2Metdc32iw4mm+ZCOXMrH038/Ez951GtpzKSXEn9kxgE/d\n8pS7Xe18ZFOGEtjv/3WwUlslV2VjzzBMg9DVmlafb6CwTER4aDZlRrdXkI6eI9DuVdsc8Mbdki4P\nAWWaG/3hRa2tbcLFWOrdThSco8cwDFM/LPSYSCaih51ETjCGClZgohAWenes3oU/PbUD3/5rdHGQ\nWshHFLHQ+cUDz+Oin8ZXXdRD05Kcq6FCCWmT1JPvzpw7udYn/fU4enKC1Z5NqdL2BcvB+3+1HDc/\nthW7B908tOGCX/q+PZeqKPR29LtCeld/tKCW45QhtrmaHD0p9NyRmwapz3ykgtB79Pl9+NiNTwS2\nG85Pa834jl6UGywnjNU+L0eIgLP1kbMPBxDv6MmG6fLhgXQij/js7Vi+aZ9abtRzbwfKhF4wdFN3\n9N77q0fxmJcbqJ+PTMrAcEyob6X8qoc27MVxB3didntWfc6y1QcQXfAlkzICDrhqryAdPUfgiLm+\nW/yuMxbjyotPjh0D05x05tK48QOnA6gjdDOmYXoj3DiuuskwDFM/LPSYSA69fCn+7aYnqi/YAEzD\nb7Yrw8lYwTg6AAAgAElEQVSA8hBEOdFORUxGayWq0bQkiXDTm4wPjFbPrRsu2IECH0rojfiCsZ4c\nPRnS2N2WDuSnyXNq2QKOIzBSstHmVURsz6YwlLfKJlBSqMjS+HFNymV+Y78UeunKffSkIGnNpPwc\nPRW6SUqY6KGbliMiRYde+TJccdIN3XSPe0PPkAplldiOwJ+f2oFDL1+qXpvbkcXnXn9McDkhkDJ8\nR69bNbcPjk8ihICpOYBZrTjNj7XWFPL4+qMcPc0Ba8+mlDjWz2nY0Xtmp9/IXX9IUGlevHbXIE5d\n3I1ZbRnIy3yOFqIbG7oZ1V5Ba0S/oLtFvc9u3tQkkzJwxuGzANTfMD0s7BrhxrGjxzAMUz8s9JhY\n/vzUjuoLNQCTZLidg+c1ARV2ZWTlyKh+YrWiO3phYReeVzy9vR+7QqJnY49f5KJnqHqFwYF8KdD0\nW4Zu7tdCN8OVMZMgwxG7WzMwDELKIBQtX+g5QuDOZ3ZBCChHb/6MHCxHlI17tGSjYNnKTYqrSqkc\nPS9Mr1oxFpkX1pIxy3rCmQapiZwuVoqWE2ivIDnr2/fg4Q1uSG04/FbP0fv3m5/ErtD4LUfg1ieD\n1/SS2W14x+mLA68JIQIPE2Z4rQ90BzFchVIXhro7V9RCPOXxDRUs3Ll6l1sVUwgUQseqh27qYjxr\n6kLPxIsWz1S/79Dc12qT9JMWdWFWu1+AZW6HL4ijQjczKQMl74GBPF79f8cRaE2bSuBFfW7M1EBG\nHNTbMD38HZCkWm41OEdv/MiXbPz8/o18jhlmGsNCjyljPIugRGEoURJ0ysLj6Bl0hUmV3tmJ0EPR\nws5h+Kb3hv95AK/41t2B17bu8yfWVyxdE9muQGeoYAXcEpnTJB29toxZNXTzluVbsXXfCFZs3o97\nnt0D2xFKlErXKW0aAaG3ans/Lr3+cQBAq7fPRV71zS37RgLbz5fsQEhqVIsCuRyghW5KR88R2DtU\nwHUPbwqIZ+lUtqRNVT1Tngu311u5o1e0nEhHD4ByssIVJ3MZs6wtwttOXajCCC3bQXiTGdMICDMA\nCM9NO3NpEAWvGcvxwy2LlhPYr+7O6dVCRzVh+sHrVuDGR7co8aiPoS1jwnIEipYTEG1hR+8rbzoO\nP7zoJADAzj5f1Fb7+53VllX98rpb0yrHEkDgYYS/L/d4VFuFUnmOnmmQas3Ajt7UxnXZa1snrhiL\nFfMdUgvs6I0fP/j7OnzttjW49cntkz0UhmHGCRZ6TBlR+Vvjiak9RdadsrAA2+M5NP0JQiWrobsz\nYWcoyhEJj6Vv1BdEd63dg988trXi/oYLVqDAR8o00JYxVTGW9lxKCaJNe4fx2T+sChQPsR2BT/9u\nJf7pqgdw4dUP4T2/fCzwtPyso+YAkO6LA2kw6dU1ZeimFHpbI4Reb0wBFv0JvxRkA3kp9DxHTwhc\n9vuV+PytqwNhhUOeo6dX3ZTtFVKB9gruckTAW09dGOvcSiETzrNsSZvIpIJCJZs2cLjXJkIKEh3T\ncPMmjzu4E2ceOds9ViEC5741YyKbMgItOUq2UOK6aAdDPfVx69dNWJjuHsir12Seo7u/lHc+rIqh\nm9mUiZMXdQOAqnYZtZ8wbVkTs9rccM3utkxApMaFbgJayKYV/N8R7nmVAjcsnJmphUH1hG66hNcr\nNaA1gs3tFcYN+R1e7UElwzBTF74jM2XIqooThconcwQ29gzjBfM6AJQX25CFRfS8tnrRJ8PhiXGS\nMJb+UStQxKJd+zmKwbxVNonuyKVVMZb2bEo5eu//9WO44ZEt2KTlo0lRp1fplE+6P/u6Y/C+lx8K\nwBUDRS1HT88Hk6GbC7paQFTu6I2WbNVbLezs6IJFjrM/VIzFdoQSa7ozOFKQIZ5+6KbcvmGQeuov\ni5Ws/ep5OGxOe2QYIeALvXCeZdo0yhy9bMp3+WxHwAy9Lyemt338TLzzJYvVa7qIbsmYyKbMYDEW\ny1F5liXLCeToBRy90Hmb3R7Mh5MPGfR1WjOy9YYdENj69SPPzbwZWRABK7f7bRRGqjjD7dmUCt1M\nGRRwOeOKsQBAwfacPFmMxQo6ei1K6LGjN5UhorKiKtXXcf8Pu+Hs6DU38vkUn2KGmb6w0GPKiOqx\nNp6QutkIPN87jMPmtCFtUll+x+4BN3QzXMyiHnQXr0zoJZjkDIyWsHhWW+D3SuMaihB6nS0pJSrb\nc2k1QZfhq/oYw6IX8MMCdacwY7rNraWg2a8JrjbPKcqlTczryAXCTwFXOO0bds/x/Bktgff0z0I6\nWzJ0U4b+OUKgwxO8eoEamXsoe80Bfv8/k4LFWEyDYkM2/XPhbi/KuQqf42zKCDxICEcm6sdlKGc5\nmFvXkjaRSxsBYVlyHBWqWDFHT/vcRooWDprhC73hgqXc85aMv44MsR0pWoFrMejomer/t56yENcv\n26IqfI5WdfRSKnTTIAqIX8OICt0MO3p24H9bCr0Mh25OB0wi1Kjz1Hd4uLl5Q4ReA7bBRCO/8yay\nyjbDMBMLCz2mDCn0qk24G4Wc6DtCYDBvoavVrSCpT5Kf2LJfVVzUXS3Jym19NTmResXGstDNRI5e\nCV2tafz0nacCAL522xqc+OU7A8voN8+hohUQA4BfedP9OaWEnlxNF9xRRQ2kSAg7PW6Onvv7fs39\nlMIEABZ2t2B7Xyh00/JDN+fPCFasLEUUFZFiznf09LYR/n5lWJAQMp+NlKBImcH2Cq1pU+X7xD3J\n90M3y89J2Il0HT1ZgdQpEzL6JFIvYKOf71zaQC5tBkJFS7ZQDmnJDuXopaIdvXzJwTyt8MnKbf34\nk5cbc5TnYgN+iO1wwQ7m6EU4egDwxTceB4OA+9btBVC9TYcu9IioLG8xjHL0LAeOI9S1ID8H2xEw\nSXf0+LYylTGojmIs8P92dBoRullrGCmTHCX0JnkcDMOMH3xHPkBZv2cQ9z7XE/meFEzZiAnbis37\nsWLz/oaORc6FLVsgX7KRTZlKsBQsG9c+vAk/vGsdulozeP0J8wPixV1f4I1XPYh3X/No4n3q4i7s\ngCQJFRoYLaGzJY1zj5kXu4y+XSHK3aYOPfQz6+bo6WJV/zlK6O2LCLPMmG6Onsx73B8higGgsyVd\nlos5WnRDN1MGBUruA0FnSjppqoKm1k5AhrDuHdSEXlG2CnAFlC5SDKJAw/RcxhdJcbk596/bixWb\n9ydz9NJGoIm6Earko59XvReYFRB6Xo6etz/HEbAdgUNnu47uBSctCLiqwdBN99h++9hWFG0H8zQB\n/fDGXlz5j/VIGaTClQE/R+/3j28LXKd67p+ea9ieTeGY+Z1YsXkfipZT9fpty/gVMg1CWThr+fLu\neIYKVrDNgi70TN/RC4fPMlMLI2Ho5l9W7sB7f/mot477GoduTk34FDPM9IXvyAcor/refbHCaEjL\nqQpz4dUP4cKrH2roWOREXwh4peYNJfS+c8ez+MKtq3HPsz04dHYbjpjTjsG8FZiIy8n641v6Ircf\nRaUcPf1pdlxIS/9oCTNa0jANii0aMhQKgQ27TZ0tvqPnCj070LtOF2JRlRT3eu0Rohw96Vzp+YyH\nzfabWrdlUxgu2IF8xHzJwf7hIrrbMio0UBLINQtXu/QeCAghlBvZM+Qfx0jBVu+X7GAzclMrxjJa\nslV+GgCcuLCr7JgB4Nndg7jw6ociw1lTZY6eEcjRC6+jO5V6GFOpLHTTVOtKl2JeZw7Pfe18vO9l\nSwLCS78eSraD3QN5/NfvVwIA5od6+wHuRFZv5yCd12sf3hxYLi5nEQBOXdyNJ7b0JSqklDINHDG3\nHdmUgf98zVHqoUAcskfe9v2jgfDVQOim5uiFi+QwUwsiJArdXL5pP+5+tsf9vlRVN8N/X9xeoZnh\n0E2Gmf6w0GPKGAz1RxtvZGhOyXFQtBzkpKNnO7ht5U61XC5tosvraabnw0VN+KuhrxMWLvrT7Kin\nyUIIDORdoQcgIE50wpPucKN3PXSzPZdCwXICE2k9dDNK6MkwS327aZPcHL2Qo/f0l1+r+sEBrqsz\nXLACOTWjJRt9I6WykvtAdDEWwH2SL0MKP3zD47hluVt9VHf0BgvuGIS3nTKhZ/uhm3p+16z2LDZ9\n8/Vlxy2JcvTC4cbZlKk1j3fKwhr145fL2U7weFsyJnJaMRbpUqRNQiZlgIiCOXra9RBukdDVlsHF\nLz4kINqufscpgTHFXU+VhN7RB3VipGgHGspXoiOXxrNfOx/nHD2vrBJpGL0dhx7yrEI3vQbzMhyU\nJ+ZTG7e9QvXPUP4t5S1bVd0sc/Qa0TCdc/TGDfmnzzqPYaYvLPSYMlToZmpsRRUuvPoh/HZ55bYD\nAMoaZmfTBjKmGyq3Q3O4cikD8zxHRG+GneSp8ZV3rcOSy25Tv+siQe+P9r2/PYczrviH+l2ftMpS\n1KMlGyVbKKEmQ+3ChIVe2NFr0Sb0xxzUCQB4zCuoAbifgxACn7j5CZz3w/vLti9dorQ2UZ/TkcWu\ngbyavEtHL1wgw3X0LOwf9gVzvmSjb7SIrpZMmUsZCN3UJvtp0wjkvcnCK7IZ+3XLNuPB9W6Dc0cI\nlCwHGe08mFqY2GjRDpyTahQihF5YTLuOnh+6WQi5TfokUi8KFAjdTJnIpv0KmfJ600MU9Z9lj0TA\nDVvVC9O0pk1c8ZYX4jAv7PPGS07H+S+cHxhT3PWUMQ3c+tGX4VsXnlD2nqyiuXX/aNl71agm9Nq9\nnL7ndg+qvwHAz9kTwi3i8v/OPxofOftwvC50PMzUwqBkQm/E+/sbKdp+6GZoPYsdvaZG/85jGGZ6\nwkLvAOP5vcNVq1YOegJlrH3Jn9rahycShFPKUEmZy+U6emZZ0ZVs2sSi7vIecEkavH/vb88B8Cce\nwaqb/s9X3rUusJ4uIk/40p3YpJ2/ao6e3mIAKM8f092ns4+eg4xp4I7Vu9RrgwULD2/oxR+f3FFx\nsqNv9/A57djS65+bfcPFQOVJSVvGxHDRxkuuuEu9VrBcR29Ga1qJfJlHGCgqorliGdOIDP3b6fV1\ne2aH309PCM/RC+Wa+cVYrNhzGUW4iA4QUYxFy9GzHVHm6OnFIlQ/x1DopmEQsik/dFO1iNCOQw8Z\n1Y8hX3Lw2h/cp36XQvaFC2YAQCA3T9IWI/RSpoETF3Xhn1+0qOy9WZ6bFu6NmIRqQg9wi/f84Ynt\n+JefLFOvFS1HTexTBqEzl8Z/nXd0ZC8+ZupARGXOXBSybcpo0fYbptvh0M1G5OhxH73xgouxMMz0\nh+/IBxiv/M49eONVD1RcRoYMjiXsRggByxHo9ZydStghRy+XNpExqUyQ5lIGDlHNvn3nopY8EOk4\nFSxbuVZy4r5HcwnDY5M8X4PQe2prP3QNFK66qU+IO3NpnHxIFx55Xnf0LOwZrH7+dJFx2Jx2WI7A\njn73/Dgienyt2XIxUbSEW020Ja3OjTzGmx/bqtwwPdQ1nTLKCpwAwI7+PH5+/0Zs7xvFobPbMKcj\nC0e4n1VZ6KZ3jvtGSujympBXo7s1jcF8CeFdd+TS+OsnzlS/6330LEeUF94JCTpAtlcIXlNue4Vg\n6GbAmdQ+2zihBvjO6lffdDz++okzMas9W75MDWJXIsMmdZGflPB1GYXsudirPbwoWI767KLaMjBT\nE9NIlrMlqwTrf1NhXdcIkcaO3vhB2sMthmGmJyz0DkA2V5kMSqEXdspqSdiWN+ewq1Vp2WGvaEc2\n5RZjKRN6aRMzWtPozKUCzb71ccaFCsnJrAynzJecMrdq5bb+svXCYldAqFC8zhZ3/ThHZPnmfXjB\nvA50eKKqzNELVZ/sCDVdH8xbVZtfA0Fn8LA5bkigLoSjQgHbooSe7Xhiy8/Rk6Llxke24IKrHsQt\ny7cGhZ5JiCuy+LXb1uC+53rQnk2B4F4/RUsExqsXY9k/UkS3lkdYie7WDAbyViBMUnK0FwYLhPro\n2U6Z0Av20XP/l9VBzzhsFp75ymsBuNeeDPetFroZdW4lUpjm0mZgnDqVcvHikIJx6/7xcfT+8zUv\nKHutqAm9JGKRmRrUE7opP/1wW4ZG5Nex0Bs/5PcR6zyGmb4kmlEQ0XlE9CwRrSeiyyLeX0xEdxHR\nSiK6h4gWeq+/koie1P7liehN3nu/IqLntfdOauyhMfUi+56FnbKwy1EJqxahp1VdBDxHL2UE8oEA\nv5rhopmtgQmtPq64dgJyXVkJs2DZ6PBy7GTj8aiKheFzIAQw5BUXkSIjai5z5V3rcP+6vTh1cbcS\nTZWEnmlQmTM2lC+VCZNPv/YFOO+4gwKv6Xlph2uVNSVRjl5bxGtyf12tGTVx192ltbsG8enfrQyE\nTKbN8rDQUxd3B35vz6ZgeE2YLccJOJCm4b1uO+gbLWFmFUdvbkcWbz55AUqOg4F8CZ25NN5yygJ8\n+63leWuAey3pOXp62OnLjpiFH150svpdnn+3vYJAS8ZUIjmbMvyqm7IYix66qTt62XhHLolw15Eh\nntXozKWQNinwACQpSYTeO1+yGG8//RD1e3s2hYJlq7/zKFeXmZoYCUM3R71Q+5GipT7/fSPFQB4s\nV91sbuRfLVfdZJjpS1WhR0QmgB8BOB/AsQAuJqJjQ4t9B8C1QogTAHwFwBUAIIS4WwhxkhDiJADn\nABgBoHeV/rR8Xwjx5NgPh2kEI8VooSfL5CdBTgD31hC6Kcl5xVgGQ+0JZLuHQ2a2xjp6vcPR+5Pr\nSpGWL9lKqMmxDkYIvajm01LoSBEU1Vz4Lyt3AAAufvEhKt8tXPpfD/1z+5n5vxO5jp6cTEnOOmoO\nfvzOU/H5N/h/gnpe2gyv2bxOZOhmhMu316viOaMlrc5JtZy5qBy99mwKX/wnf3ztuRSIXKfMdkTg\nOOW6uwbyEAJVQzeJXFFl2a6z2pFL4Xv/fBLedlp53hrgCjTDIBjk5ehpwvmGD7wELztitvpdTlbf\n+8vHsGp7f+C85tImBgsWipaj/i70IjhmIEcv2tE7al47zj1mbsXjC/Pnf3t5ouWI3KqX27xiLFe9\n/WS84qg5idZNIvQAP4wXAObPyKliLAA7etMJo9bQzaKtFMPV92zAO372iFqmXpGm75/76I0fhgrd\nnOSBMAwzbiRx9F4MYL0QYqMQogjgZgAXhJY5FoAsVXh3xPsA8FYAtwshan/kzIwbUSJF3sDDYTcj\nEVUOAWD9nqGyG7oMoRzIW1Wf6pYLPTMyfE22e1g0sxXb9o2qsesJ/3EOonT0BpWj54duSkcw3PcO\nKHdghNByCVN+o3DJhp4hWLaDkaKNN5+8AMcvmKHGXcnRI6JAntOMlnRk6KZc5/0vP1RN0MuahIfO\nXVTOV1TIY4+XD9jVmlYtD+KEnhxquOqmHON7X3Yo5nvNwZWjB/dc6aJArvvy/74bgJ9rFgeBkDIN\nlGyBwXwp0IswCummpgzDdfRKDl597Dxc857TypYNCx7dKS3ZbuuPS65d7gs9M8bRizlnX73g+DFX\nsq3EzDY/3+/MI+bg0lccVrbM4V5or05dQq+rJdCcPek2mHKI6Boi2kNET2uvfdWLkHmSiO4kooMn\najyJQzeL5aGbALB88371c6lOBaF/p7KjN37o4epMc8DuKtNokgi9BQD0GvnbvNd0ngLwFu/nNwPo\nIKJZoWUuAnBT6LWvezez7xNReVUCAET0QSJaTkTLe3p6EgyXqYWop6UydDMcqhl2lwBgY88QXvW9\ne/F9r6pl1Hb3VwnfDN/I3Ry98gmxnCQvmtmKou2oQiW6oxfXUy+rHD2Zo2erXKqSJbz3ysM+h0Mu\nn+U4qr2AcvS0L+Zzv3svvn3Hs8iX/FYBctzhipBhMas7Y7Pb3TYJ4f0HC7hE5/6Fe+BFOUytEeGF\newbdYjRdLRn1+bXEuFMypHCoYJU5euFCLu1Z39GzHBEI8ws7Qd3VhB6559FyHAzkLXUO4pDn3jQI\nJcvN0TtmfifOOXpe5LZ1dGd08SxXIN37XI96sKA7tHqOXjomxy7u9WocFNFkPYrZ7f65a82WPyz5\nwMsPxe8//NKy9ao1TJfofR9nt2dcR09Iocfp3mPgVwDOC732bSHECV40zF8AfGGiBuMKverLycgP\nt+pm9DL1tleo1suUaQyyGAtri+aBL3em0TTq7vwpAGcR0RMAzgKwHYCyIohoPoAXArhDW+dyAEcD\neBGAmQD+X9SGhRA/FUKcJoQ4bc6cZKFITHKinpbKJ7VloZsRYYwyJ+6B9XsDr+tuoAwJjCP8NNGt\nulnB0etuAeBXAtTHGZf8L8XHsFaMpTXjNtOWleGSOHoFy1Ghm9LRC49/2cZejBRttHriMqe5SjoZ\nMyi2dFfkzCNno3+0hIc29AbX0Sbv0s0Kn6qwaxTl6EVVhpSXQldrWp2TVq3/3qOfORenHNKFM4+c\njY++8ggAwPa+0TJHT+5fCgMZuimE6yCHc/R0qhVjIXjunHT0clUcPe98pUxSFVdz6eivvfBYdGH+\n7jMW49TF3ThqXru63vRrNBUIw/V/vuTMQ9XPUdd0HCcsnIHjF7jFWu7+1NlY9aXXVF1HtljIpAyk\nTaNM6B2/YEZkaGw9jl4u7bab8B29RJtgIhBC3AdgX+i1Ae3XNkxgBXyi8n54YRzPHQdcwRc3Oa23\nGIterNPm9grjhl+MhdVFs8AONtNoKj8Od9kOQE+AWei9phBC7IDn6BFRO4ALhRB6A7V/BvAHIURJ\nW2en92OBiH4JVywy40jUl7k7oQ8KASluHIFATlWU0JOT5ny4bL12c65WkCUyRy/C/ZACwm+xMIIX\nHzoz4OLFPUGWjt6gVowllzKRNkk5l1E5esMhF9MVen5j96jx28LNBZMCS+YHhnP0wg6fLhDOOXou\nrnt4M9btGYpdp7s1g829I7HhnZLWdITQCzl6BvlCrz2bUk94dedvbmcOv7v0pSAKVm4N6wR5XnLe\n8fvFWFxHr4WiRZE8pkoQEdImoWQ7GBhNELophZ5Bys0NN4+PG0sqIOQMHNzVgtXbi2ryGu4HqJbV\nfj64q0XbRvLwxj99zM/Nc6+j6iGf8zznry3kJEeNUSfpuHShlzENFCxbhU+zo9d4iOjrAN4FoB/A\nKydqv6b3t1oJPdd1tOTELl+qU6Q57OhNCATuo9dscBgt02iS3J0fA3AkER1KRBm4IZh/0hcgotlE\nJLd1OYBrQtu4GKGwTc/lA7mxA28C8DSYioz1qVvUk6JoR89ST/p0t0wvTGKHcnPCIZP6k9ze4QLy\nJRtnfftu3Ptcefht+EaeTZmBPDPp8kgBsaC7BUTRjl5cTkjWm7Tr7RVyaQNpw1Chm+EwyajXip7Q\nI/JFxOxQL7TRog0hfCftwlMW4swjZ+PVxwbDBctCN7Vfu1oyOGJueQXNrOYC/s/FJ+NfX3IIjp0f\nLNMfztFb0N2CMGFHTw/vzKVNfOgVh+NfTluE97/80MByhkEgIizUthkWENK5kvpBtldwhHsT04VQ\nWGRUC92U65RsB4MFq6wlRRgp8E3DwJBXxTUXK/SCv4cdOPlQwG+vEC3u9POhh9WOdyPxuZ7QU33+\nyq6vaEGXNHRTF3rZtMGO3jgjhPisEGIRgBsAfCxqmfFIbTCIUE2f6Q+XRsfB0dMdxag8cqYxCE/i\nsbhoHvijYBpN1duzEMKCe5O5A8AaAL8VQqwmoq8Q0Ru9xc4G8CwRPQdgHoCvy/WJaAlcR/De0KZv\nIKJVAFYBmA3ga2M6kmnOB369HEd89vYxbSMqHCcsstyqgkJN6nQRpbtb8nV5Iy939DShN1TEzv48\nNveO4N9ufLxsDOEbeTZtBCbwMtxMTtCzKRMHdeZUi4UkffTkjUyGZ+ZLNrJpE+mUoY4lqr3CcKE8\ndHO06LqBMr/hqrefgiM1USbXkU7ahacuxHXvPx0vPXx2YFuVJuKG4btb+nLplL/Mopmt+NqbXhhw\nnoByoXfa4pllxxXO0dOLrmRTBma0pvHfbz0htgqmvs+40vryePRiLJYdrLoZXjeukIla3nBDNx3h\n3hATh24apFyIuBDKMkcvHMppuNdKdDGWaHdP31d6nF2veZ3uAwfpTCcWeglDN2XfSMB9cOL20XO8\nbbDSG0duAHBh1BvjkdqQJHRzRLsXjBTtWKFQb3sF/Z7Ajt74Ic9zA7pgMA2CRTfTaJKEbkIIsRTA\n0tBrX9B+/h2A38WsuwnlxVsghDinloEe6Px9ze4xbyPqKW3Y0ZM38BktafSNlAIVLfWnuCXbQS5t\nqvWrhW7KqeRARB5ceFKRS5sBgdElHT1t4nrQjBz2DBTQO1TA8s1+ekvcpEAXc0IIFCwHuZShCnsA\ncTl64dBNG3nLDuS9zenI4l1nLMbnb10NwHcBo3LjdMKCQxcapkGqAmV3axq7B9zCM0lcoXDI3kmH\ndFVdJiD00roLFS8C7vyPVwQakkvkNSGPpyVjqmIsjgi1V9B+vvEDpyvxHMc5L5gbGFOco/eqY+bi\n72v2KLGWMkm5znGhimGhF76U0im3rYPqoxeTo6cLxDiRPh7MCxVtCX92cc6d/Ayq6b2go+deLzJP\nK6kryCSDiI4UQqzzfr0AwNqJ2rfb27Ka0PO/70dKdryjx1U3mxp57+U8yOaBhR7TaBIJPaZ5GC5Y\nqlpkrUR9gZQLPfcG3tWSxmYEHTI9dFM6eeFm5+H3AaB3uBi44Y8Wg0IpqurmzDZ/UiknmHrI3byO\nHDb0DOHN//tQoKdeXKiQnJwPFSw14c+mTaQMA0UvdHOwYAVy1QCoAh6SoleMJVfmlhjaOlLoVf6c\nKjkuJpESuN2tGSX0kvQr04VaNmVEtlIAgEc+cy5O/8ZdZWPVBWilAiJHzesAUO7IymtCHo8jBEjL\n0YsTeqcfFi7UG+TBy87BvI4sfv7A8+q1uBy9q95+CvpGSko4pgxCQYqShM5W+JpOm0YgdDMdU1TG\niAndDBfjaTTzOoJCLyy+zBiBK8dezdnr8NzTj5x9uLou5PcFt1eoHyK6CW5UzGwi2gbgiwBeR0Qv\nAN3ok5IAACAASURBVOAA2Azg0okaT5Kqm7rQu/GRLbHL1Vt1U98/O3rjhzy1pTpDbJnGw5c702hY\n6E0x9gwWcGidQi8qHEeKrN+v2IaUSTjuYDffa4bnqOktFsKOnr6+fLIvCYZuFgJibsu+Efzhie04\nal47NvYMBwQokSsu9KIcsrplQOh1ZvHQhr1lDuHSVTuRMgj//KJgA2053sG8pSb8bhsHLXQzb6Gr\nNRMoHhN2+Qpeif5wnpeuh+ShRhVB0akk9GQDbCAoZqo5XoAvzt59xmJ84Z+Oi11Od4Cko5cyKBCW\nmWR/4aqbfoEOKfRct0hW3dRFsUnRYknnk68+Cv9YuwcLvMImgZ51MX8LubSJg2b45980CAWvLUac\n+xR+OexSZ8xKoZvRjp4uBmupulkPczuDuaIzWtKY3Z7F3qHKDwl8R6/yZ20ahOeveB0A4LplmwH4\njjcLvfoRQlwc8fIvJnwgHgZVd9FGIwpzRVGvgNDvCezojR9+6Caf42aBK6AyjYYTK6YYuwfyda8b\nldR+7nfvxQPr9uI/b3kK/37zkyq/zM/R0504LUfP21acg2aHQjf1UM4d/aP48b0b8MnfPoWr7l4f\nEIky9y0g9DyHSg/dnNuZiwwDfWD9XvzX71eWvS6FZ89gQU34c2lTVXAEXLevK+QQRRVjKUQKvfI/\npWqhm+EwzLDokeGrSVw8HdUk3CwPqwwj327NlItpyYfOOgw3XfKSxPuXk7TPvf5YXHDSwXj1MfNA\nINVHTzeWkgiEj597JP740Zep3/XzFnZW40gZhnJy4/YZFjrhyaxbBCYudDM6ZzE9gaGb4c8uZRpY\n/rlXqQcKcQJXjndWgkI4RG4xHvm3KM9Rrdco07xQgobpSe9DVp0hgbrwqLegC1MdeZ7r/ZyYxsOa\nm2k0LPSmGJVusL9bsQ1LLrsNfSPR7QyintoVbQdf+JNf8HRY5eh5zcQ1R08XViWvKEn/aPS+5GR4\ndrvrkOn7HhgNNibXQ+TkhHFmmy70ohy9ZE2kJbJgy9Z9I34fvLSJtGmgZAvYjsBI0caMUB+3cMuF\ngmWrip06URPdWnP0wqGbMny11gd8Mv8uyeRbihW/0E35V8Ll5x+DMw6vHFapIy+Zg2bk8MOLTlY5\nekK164guXJIUPR8uW8U11deplqOXJHTTvU7K2zTEVRLNTGDoJgD88KKTcKsmigGo2ulx5zqXNvHV\nC47DzR88I/F+pHiUoc1hV5eZurg5epWXeeT5XsxoSeOqt59ccbm6++gFHD0WIeOFjPJhMd08sLvK\nNBoWelOMSkLvGi93adv+0cj34yqp6Q7Ern53+10trtDShZ4uIC3HwbnfvQeXXl9eRRPwv6zmdOSw\nd6gQCOUMN1DXQ+TkULpag82Z3f/9y3VeKEwtTNFysGLz/sB4AVe47R7Mq+2lvHA8OXkPV3EMh24W\nvdDNsIiLmkS3VhF6YVGlT5YNw682Wmtytt4kvBpSiPi918b+lRA1MSMv78cVev7r9Qg9vXplXPPz\nMG6Onswni14nbHhFCT0A2O/9Hei9CMMiXRIoxlJDH716ueCkBThxUXnxHaDy9fDOM5bgkFmtifcj\nHyZIl58dvemDQdW/cx7e2IuXHDYTbzjh4IotTuoO3dQbpnMo27ghTy2Li+aBQzeZRsNCbwqgiy1Z\nlCMKeXOOS7WJ+/7Q52hrdw0CiA7d7NOcuJItsKM/XnTKMc/rzGIgbwXEXO9Q8Bj0ypZS3OgFRLIq\ndDO5o/flP6/GhVc/hE17h9V453a44nDd7iG1vYwXuikdv7A4C7dckA3Tc6GqlZGOXq05eqHQzU5v\nAhXuUVgNKUiSOEhy8i+LscQV7KiFV4X6BQLyGhOwRdDRq5YXFkXA0Uslc/RMg5CXjl7CfnKvO/6g\nwO8ZJfRKyKaMYEP1BH30kuQ7jif1nOs4wsVYGrltZnIhoooT//7RErbuG8Uph3QDqCzy6w7d5Ibp\nE4L8nOP6zzITD38UTKNhoTcF0PPE9seEZQK+kIsTdHE3b32SVi70dEdPF3rlN3ARkUA/qy1btm5v\nyNGTk8W5HVn87ztOARCcFPvFWPzLVRbmCB6H//PDG3oB+BONku3gsDltAIB1ewbV9tKeoycnE+E8\np8F8MMxUNkwvz9GrPXQznKNnhFwhKWLCRUGqIceSxEGSYkU6eWOdsP/pYy/DG044uOx1I8bRq8cJ\n0gVWckfPUGI+SY7ep1/7Arz7pUtC+3Xf7x8plVUy1ccUFHqTL4BkU+RGho7Khy/ybzeJe8xMDUyq\nHLqpHop5fwOVeihGOXrLNvbiLyt3VBxDoL0ChxWOG9xeofng9gpMo2GhNwUY1MIHowqqSOSXdjGm\npHWS8Ixndw0A8Cs96oKuf7QU6fRJdOdJvi/D2/SKnb3D5aGbczuyePSzr8LxC2aUbfcVR83G205d\nGHDI2rIpdFSYbPd77qOcvLtCz21qLh29XNr0QjeFmryEhUO0o+ckEnqttbZX0ISGYRBecFAHzjvu\nIHznbSdW3E4YeczhRupRyOIzSSsvViOuCqbso2c7Ira5eFLSRu2OXlyfOx1daLekzTIHTg/dDB9n\nnKM33pU2a6GRKYIqdLPEjt50wzAqTzble/L7qqKjF3Evuuiny/CxG5+oOAbBjt64UbBs9SDUqVJU\njZl4WOgxjYbbK0wBhrXQxvBN769P78KP7l6PWz/6MvUFUShFC724LxDdMdo9UAARMKtd5uhpoZsj\nRcxuz6B/tBTp6A0VLCWApKiUrpae79Q7HA7dtCMn/D96+ylozZo4dfFMnLp4Ztn75x1/EG5ZsU39\nnjYIUkLKMFNbu5F1t6aRSRkqzzGbMlTopjyvYeEwWNZewfYcvfhCKpJqoZvhCZKuCQwipE0DP37n\nqRW3EbndGtyVa97zItz65HYl7Mc6X0/H5r+RKsZihARtregCNmlOYVyfOx395ahzqIduhoWe3P4R\nc9tjQzcnC/ln30hHTz6kGOEcvWmHQVQxL05+V8rPvNLDmrobpnN7hXHjK39+Bjc8sgV3fOIVak7A\nYrp5YJ3HNJrJn4UwVdELgoTF2jM7B7Bqez8KlqO+IPJWdKhf3Hd5uAded2tGTaBLntPlOAL9oyXM\nbi8PxZToIaYyZLI17U6I9dYMe0M5eqNFO9IReP0J8/HKF8yNHjSAr77peHz+Dceq33UB4JeNFqpR\nd8owkEsZSgT6VTcd9eQ57NSF8+OKMX30wpPoTKp6a4OwYxQO3awXeS6TTJAWzWzFx845UomYsToz\ncSKToDl6Cdy1pPtIKvTi+tzpBARoxHmQ7RH6RopozwY//7Rp4GfvOg03XnJ6UOg1oLhNo2hkr7ts\nKhi6yX30pg/VGqZLF8hIIPSiHggmIdBegUVIQ3nWS8/oHy2pCsl8jpsHdvSYRtM8sxAmFr3EfzjE\nQoYcFi2nqqMXN/EPC8NZbRnlRKhG4wULjgDmeAVNegbLC7HoN3U5TlncJBC6GcrRGy1FO3rVyKVN\nvFfLo4rKh7Idv+9ZJmWgJWOq6qG5lCv0LFuocFfdhYuqmimLsYTduvD4q1XcjCIYulnz6tpY3P8r\nhfmGkQ7NWKfrcUJPnp4yR68OYam7hknCU4FgHlHctWbGhF+qfXnb6Itw9ADg1cfOw9yOXED0N1Po\nZiPFWCbUR4+F3vTBoMqV/8KOXqWHNcUaC0lJ9JSxqPBPpjE4nKPXdLDmZhoNh242Odct24wdfW67\nhEzKKHvaI2+kBdv2hV6so1c9dBNwe9gpoed96/R7Dp509PYMllf/LFr6U1iZsF8eujkSakQ9UrQx\nq612YQQEXbCo0DTLEUqApgxCS9pUwi+bNpAyCUXbUcJUD8lsy6bKxjrkCd6yPnohgRNu05CESkLj\nxIUz0JFwm1I81XLDSDfI0YsL3TS8Jsy2EJE952rRCfUU/kibla8TIBi2GiVc5DkaLdllxVh09FWb\nQejJy6CR4ZXs6E1fjIiqm39/ZjcOm9OGw+a0q/eSOHrh78+kONM0dLNoOegbLWJuR219YMcLVXWT\nc/SaBnb0mEbDQq/J+fwf/WbmM1rSZSEWRdu9kRYtRz0FjSvHL7/Uf/T2U3BwVw5v/t+HAPihmwd1\n5rBrII9Z7X7opuw91uc1RpeO3p6INg8BR88JOnqjFW74o0UbRsfYJ4pRAsB2fBGXNo1AyGUuZSKj\nqm6Wh262ZUz0hLYnBW+1YiyyaE0tVHK6bv3Yy2veTi39p6SIGWuOXmzoJrlP6W1HBPsFqoIOyQVR\nPZUsq7l1QKi9RcSJyKT81yoJPT0kN51qHgHUyKbm7OhNX2TPywfW7cUx8zswqz2LD1y7HACw6Zuv\nVxPRajl67dlUWTGrpOjfXdOp9P8nfvMElq7aheeveN2kt1sBdEdv+pzjqU4tkTgMk4TJf9zMJCKX\nNnBQZ67sC1kP3RTK0atcjCVlUmSRiEUz3ZYFs9qyqny67D223xM4c5Sjlyx0s8XL0av0ZLdoO2PK\nSZNEHZMelpk2KSDQWjIyR08oNzLs6IWR7S3CFTXDbkk9Qi+JGEmCHEst4ThSxIx1wh5XfETvzRVV\nobKW/dZTVEQfV5IcvSjBqm8jrrpopXUmm8Y6el5IdsmdyDfi75dpDkzDDZd8zy8fxU2PbgmEXwoh\n1He7X903+rOf0ZIO5G3Xgj7ZnU6hm0tX7QLQPAU3VDGWaXSOpzqs85hG0zyzEKYMPU/iuINnIJc2\n4oWe7agviEJM3zU57zeIIm/OC7tbAbihm1IQyW3t91oizO30cvSGIkI3Ezh6cU5IIxyBqG3YjlBu\nXdo0VG5dxjSQSbmhm7GOXsRY5TluCxXjCDtwdTl6jSrGooRe8nUaFboZJyYIQMkp72OXpHJf2T7G\nGLoZ20cvwmkM7NeoXeg1QzVK+T3CxViYJBhEyFs2LEdguGirnGYA2LZ/1G+voP52o6cRnWMRetpt\nbjqGFdYSbTFeEAWLljHNQTOHbtqOwOX/txLP7x2e7KEwNcBCr4nRRV1XSzoyd0KKq0AxlrjQTXWD\njp6ALux2Hb3Z7RkliKRAk73vDprh5hYM5ctv4PoNWT4hbAvl6IUFkmQsE8V7PnU27v+vV0YWELAc\ngZIlnUxDtXuQ41ChmzJvT2uvEO7TpxMuxlKWo1ePozfGtgNqO4bM0Zv40M1KzcjlOTYjBFUth1uP\nSxbX0DyOqGWCoZvJckqbITxL0kjXTeYebuwZBlHlUFZmamEQqXB+N6fMr7C8clt/4mIsXS3p+kM3\ntftcvZU7m5lmCZWUp7ZZxsM0t9B7ens/bnp0K/795sp9MJnmgoVeE6M/ZXv3S5fANCi2GEvRctSX\ndbUcPYMo8insIuXoZZE23fYAsiJn71ABKYMws83trxcVilmyyh29FtVeobKjNxZhs2R2GxbNbI0M\nh7EdoZyktElKoElHRoVuyqqbmcqOnqRa6GZnLtnEd0FXC04+pAtAsI/eWJBDqSXW3xd6YxMDcesb\nhj9hC+TCycliDQdfj0uWjnARK1GpGAuQzNG76EWLEo5ufJFXgVmHExqH/vd62XlHo9v7XmCmPoZB\nyBf93G8ZzQG4fROTFmOZ0ZLGSNGuWMEzDv0+Z03DipCTOZfXdy0/m+kopqcqTazzFFNhjIwPP4Zt\nYqRY+szrjsYrjpqDn92/EaOlsKMnvP8dVT0zruqmHsIV9XT/RYfOxEmLunDiohkAgFzKwGjRvQHs\nGy6iuy2DrBlfXKViewVvbO0xlSMbEeIW9d2jV93Ui7G0a0LPdoQSzLmUPpn3Rd/8GTmcuLALf13t\n5li0ZMLFWIJCJfx+HA9edo76eaxhk5J/OvFg/OTejbj49ENqXne8IvAI5Au9iBy9Wo69HkcvXauj\nVyV0s5qDtembr69hdBPDeOXRfeDMw8Zlu8zkYJDfcqdoOSo/G3AfIoZzbeO+u2XxsILllBWvAtz7\nUdyDIbmPtEkcujmO2FyMpeloZkeviQJUmBpgodfEWKotgDvBNA0qc2mK2g1ZiqlqffQMosin+4u6\nW/DHj75M/d6SMX1Hb7jo9tfzwtdGIvIA9Rw923FA5Oe8ySfEcU5XIyahUU+ObcdRoZuu0HPPpXRk\nZMilDC0NVt10l+nIpvDw5efi5ke3KKEX7pMXnuyEQzuT0Kg8p/kzWrDi86+uaR157holNsMQ+Q8u\nooReLdqtnhy9VKAYS/WdVQvdrKd9xmTT6Dy6j597JE4+pIvz86YZBvniqmQ7gRy9B9fvxaPP71PL\nAfHX1ewO1+UdLliRQs8RQNyfspzsZkxjWhYKaRZhxTl6zcdU+ChE5GN1pllhodfEqFwI039yWtZe\nwXOiBvOWstOr5egZRGXCJG1SWfhcNmUql7B3qIBZ7Rk1SY66UenV2UqO2y9NlmGX1fk2945Ejm0s\nDcIlUV+Qlha6mYoI3ZS5RiNeLkkuomG6FMV6uF5bKHQzPNlJ6uhV2sZE4j8EGJ/tU0yOnhT4tVTS\nrK/qprbPBEKxWujmVApVlN8LjS4M88lXH9XQ7THNgf6wp2AHc/Ruf3qX+lm/L8n/53XmsN3r+ypT\nAYYLNma1l+/HchyYRvT3pBR62bQ5LUVIPeGs44FfdbM5xsM0t6PHTE04R6+JkV++cmJbqRhL/6ge\nXlO56qZpUNlEVi9CImnJ+EJv33DRy92LnyzqITa2I5AyDLW8DPU8+qAOAMDFLw7mL9UzeQ8T9ZTJ\ndoTKHcxoxVhkMQ05vhHl6PnjyHnLynOuh+uVh24Gz0vUE+xqjJebloR5nW6RnTMOnz0u2zcIkaGb\n6v0aPv56+ugFQjcTnOeqQq+1NkdvdvvkCcPXHDsPAFfGZJKhXyZu6GYx+m9WOXru38X7X34ovvzG\n49T7svJwXEGWSuJCmni5lDE9QzebQLwK4c8JpqOYnqpMVh+9fMnG0lU7J2XfzPjCjl4TI5PQ1RNT\nkzCYt3Dtw5vwzpcsBhEpFy0g9GJCN50KVTejJs+5tKGqr8nQTfLcwKgbg56jV7KdgKMnhd6333oi\nvlS0cNOjWwLrNqKZc9SDMMsWgSpxUoDJSbt0MeX4dIEmw/MGvQqjuqMXDt1shNCbzIn4ktltuPfT\nZ6sWG43GIIoM3VSVYGsQubUUbvHXqd5eQSeyvYK2jVocvcc++6rAA4SJ5sqLT8a+4WJTVQBlmhf9\n2i9aDvqGS5jVlsHeoUIgasJvjeL+TkRIB3Kc3e/L4WKM0KswoZVCKJs2p2WhkGbQVUIILUdv+p3j\nqcpkXRtXLF2DXz+8Gb/54Etw+mGzIpch8D1kKsKOXhOjHD3TL1ixvW8UX7h1Ne5+dg8AP1xyIIGj\nJ7/UicodvajJcy5lYrRoo2g5GMxbmOVNbjOp6Mvm+mWb8fdndquxp0zyQyM9x6wjl8LBXS3qdUkj\nCgLGVd1UDdNTfh89efxyHLIRelY7thMXdgW2pRdnKWuvoJ3PjGngjJgvykpMtuOyeFbbuI2BEF11\n047orVeN+qpuVm+YHthHxAWpX7OVWm+EmdORRcck5vTl0iYO7mqZtP0zUwvdXS/ZDvpGi+huzZRF\nfaiKuSriJFjdVgq9OEevkqslpnmOXjOE59lCKPeIQzebh8m6Nrbsc9NqkrREaYLLl6kBFnpNjJ+j\n57lP2k10RCt/DQAD+WBltHW7B/Hq790bSKSXX+pmhNALCy/AL8Yy6G1b9oaLE3rr9gzhA9cuV2M3\nDQNErtgTwi3IIZ278Dbimu7WQlwfPXkTSxsGsp6zIsXGSYd0IZMycP0y12HUw/OOmd8R2JYeuhl2\nIPXz+dzXz8ecjmzN4x+vqojNgJ6jp4uoQ2e3480nL8BVbz8l8bbqq7rp7zOJexzl6On7ZXeMma6E\nHb2Roo3WrBnxnU1l/0tHL5sy1PdlXNP0vpEirlu2uex7+/+z9+bxklT12fhzqqq77zL3zg4MDAwg\nICAiwoiAEhT1Jy4/F+LG6xvxjYkxidF8EhNRE00wBowmxkSNr5qJkkQxEtcIKiK7bKPs+zDALDD7\ncvfurqrz/lH1PXXOqVPV1ff27eq+9zyfD8zt7uqqU9W1fJ/zfL/Pd9f4DH7/P38dradiUzc7DTre\nnCeTvzZ1s3dQFtGjy6wT2VUWvQVL9HoYeuqmfAHSvYDUqjGpgXm9GeLz1z2Ox3dN4MbHdov3Ayl1\nTid6ptTNyIwlxAy1HohJkokUpsYehGKdFa1oHzARvZarbAnTsyoIw6S9guSaSPt/wqEjeOsZa8X7\nMgnRVc683mmdqDEsW9GbT0Sum9HvIAeSrsPwubefhpPWjBZeFx2ndSuLp5m2m+7ZqkbPwmKhQiF6\ncduewUo20aP7OmNMXCMjAxWRATFVN2eY/MX3H8Bffv8B3L55n/K+/LrmOQuyj15PKHohF8/MhXiM\n+xVlnRq8QBmFnd/sT9gavR5GYsaS3a+oLrluAtGDse4HUq+j5OEcKq6b6kPbFMRGNXqBMGShurOa\nod7IdVSjmCDkSXqk52CyESjBc1bQMBdkmrFIbSpCQ52YnFZX0Y7LdX96nqh/zOud1onxL+SZtMiM\nRT2f54L/eM+L8dzDRlovGKNdAxfTGGdjAmNh0W/QzVhCzjE6UFHS2gGpB6bohZlcN6ODnnAmzkoF\n2zk2AwDi+UKQjY5qnrsg0wp7gVeFUuqmFfR6B2VNAsjxYSv0wDyFRRuwU9RdwFN7JhVlrSj09goy\nmeAArrn/WUHw5PTKuh+KoFoOTkMhzadt9E2Kx2DFVYge1WiYFD09CG6GXJBHInXyTFGqRq8jqZvp\n96KG6Um9R2AgerKxik62nrN6CU4/ajkApAIdGZ0gev2cunnY6EBuawb54dEJQvvS41e1lR7brhpn\netjZdE2L+QBjbANjbBdj7AHpvc8wxh5hjN3HGPseY2xZ3jo6Cfn6bPghphsBBkyKHksIXvQvExOP\nIwMV8dzKSlOkt/UJOvleWvMcpT9rv4NuIb3QMJ3z5LfpBYXRIkJZpHu+WyxZlAdL9LqAl332Bly8\n4c62vxeEiRIFqMHndMMXdQwAMBETvtEBD3U/FKkY8k0jkGr09KC1anTdJKKnpW4aWjHoSlgQhuKB\nTUF2rqLXgZuLOXWTJ43nXZbkoTMz0ctDXqDfiZtjJ3oJloWbP/xyPPLJ12R+Lh+6TvdzK4JOpG5a\nWMwTvg7gAu29awGcwjk/FcBjAD7SrcGkUzfDiOilJueYsrzDEpOq049aJj7PIjX0PNI/lmvyahVn\nYSp6PUCsotTN5Dfold5+ix2lKXrxfMpCzixarLCpmz0MPdVNDpAPTDWVZUnZWzJQwZ7xung4ymkx\nQpovWH80UHEw3QxQ11I3hUInpWvKD/NbN+3B1ffvwHMPHVGWl4NtPWjozM3FbMYiavRcBxe+8Aj8\n7MEd+L3zjhXLzKa5uY5OqD39rOi1UsxYhxW9dlFpc5tZRO8dLzoS552wuhNDsrAAAHDOb2KMHa29\n9zPp5e0A3tKt8ciTcA0/RBByDFYd1Cpm1026thljOPnwUVz53rNwxrrlybMhQ6LIJHp+ouDVPHdB\n1o+V1StNGQPnCqkIQm50G7boLso6N4I2Ujct+guW6HURnPO2CIGos4uDaDlAll02gaQOYrjqYqdE\nbqYloicrejpMN/godTMU6xA1evF4Biuu2K7s2vWJHz4IAHh05ziAhNSZFL3lQxXsn2p2ROXJaq8g\np7EuG6ri2793trJMUUVvvrGQVSR5z8pQ9NpN3cz6LS7/zVM7MRwLi3bw2wC+3a2NybXIjSBEww8x\n4Lnivk9IiF70mgLEs0RrmTirJDN1MyZ62vty37xa3DC93Wdnr6MHeB5CzhUS3gtjsijvd2hHSbSn\nSn+hj5PF+gOyolak1mDHwRnsnagDkPqOGRQ9uUE6kBC9oaoHP0xu4NQIHJBq9FpYxxNoBpdIZZK6\nGRM9iSDJD4xTDlcdFIWiJ41/WVxwf8oRS1PbnS1MNypd0TNhqFp8vuM77zsb13zw3NkNsAUWcsqE\n4rRZQsDW7kx1P6urFgsHjLGPAfAB/GfG5+9ljG1kjG3cvbv9OnATRgYkoufHrpvVbNdNum3pt1e6\nhlqnbqqfNzSiJy+7UFBqe4X435CrpKIX0kkXK+RroLzUTVuvuVBhiV6HsG3/lGgWrr4/Lf6mWrc8\nnHXZdTjjb34OIHkYkNGJTAT2TzbTX0akTgVhiCYRvWaAGx/bjb0TdXEhm2rBjH30YqJHaaIDnpq6\nKTcNlx9cRAAPXzqgrFtWSU4/ajn++/fPxmufvwYAsGu8btyfdmC6PQVhKNTGbKJXXNF70dEr2moF\n0A4WMrmQOWwZymXbip5NYbIoGYyxdwN4PYB38owCKs75Vzjn6znn61ev7kxKsUz0phoBQh5lc+i3\np8SMRf2XQM+rLE6TGIGo78s1eqTiLbReer0QTKcVvfLHtFghH/qyaiXpVOiFtGKLzsKmbnYA+ycb\neOmnrwcAPHnZa5UUk637psTf9WYADFZS388CPdxMit6eCTMxGq658EMu6uom6j4u3nAnTjxsBBef\nc7SyPhnmGr2IAO0noldRXTezCNJ0I8CqJTVc96cvi5Y3KHqMMZyxboUgv88cmE6tp11kuW5SU/ks\ne3ybujn/kK+JviB6C5h0W/Q+GGMXAPhzAOdxzqdaLd9JLKmln1EDlfQ9kiZD6EoxpVY6LDtwTJp1\nqxOglIFx8dnrsHb5YPReGGIQvXGf7gR6gVTJZiz02qIcyEe+bNfNvO0n5j32XOknFIp+GGMXMMYe\nZYxtYoxdYvh8HWPsutgK+gbG2Nr4/Zczxu6R/pthjL0p/uwYxtgd8Tq/zRirdnbXuoeNT+8Xf09r\nPYH2TjbE30UUPQKXZtuEa6X0IJXXK2Ow4iEIuUjlnIz/fWL3RNs1etTwllJJKXWT+uiZHv5AtJ8r\nh6tC2ZPNW3QcuTxqev3MgRnjutrBP779tNR7QcDhhyE8J+00Shis9MZ8x0IugmYlK3rtpm72bPNz\nKAAAIABJREFUswOqRX+BMfYtALcBeC5jbBtj7D0AvgBgBMC18bPzy90aj6zoEQYrboqcuJIJSxZc\nh2WnbsYTmU2tpIFckv/oFceLZ9+WvVP45RN7Cu5B76MXSBXnKglfgJ43fYOwF1I3edpYT4fld/2J\nluEMY8wF8EUArwFwMoCLGGMna4t9FsAVsRX0pQAuAwDO+fWc89M456cBOB/AFAByE/s0gM9xzo8D\nsB/AezqwP6Wg7ifkjtocEOSH2IyvksA87B6vi5lOCozlXnN7DYpexWWoeg78kEv99ZLx0CyM6cFs\nSt1cNhRxb2psW1jRawaCFEbjIkUvvY01y6L0zk44X778xEPw+XeoZI/66OUF+lbRm38wlKzotcnc\nTOeqhcV8gHN+Eed8Dee8wjlfyzn/V875cZzzI+n5yTl/X7fGI5uxEAYqTirI09srmGb5HcZaKnpN\nX6/RSyY46b79+n++Bf/rq3e0sRe9Cbrz9YqiF/QAwbBQCVTZZix5qZudOkfCkGdmpVl0HkWimTMB\nbOKcb+acNwBcCeCN2jInA/hF/Pf1hs+ByB76Gs75FIuYxvkAroo/+waAN7U7+F5BQ7KDHq/7mZ/N\nNIsTvS37pkSLBApSZS62fypdo1fzXHgOgx+EQtGjRuoMSSuEoqmby+I00x0x0aPCeFONnoyZZqCo\nfbUcRa/iOvjSO0/HdzQnzNlCV8WC2IwlL3WvU0Rvw7vX46d//Buz/n6b2YV9hdJr9DxrxmJhUQSj\nA+nUzcGKm0P0otemGFBuwaOD3m9mpG5WXaftCZp+QQ8IenF7heR1LzRxX6zgUvJmWWmRSepmjqLX\noW39y41PYP3f/Bzb9nc1K33Roshd9AgAW6XX2+L3ZNwL4ML47zcDGGGMrdSWeQeAb8V/rwRwgHNO\nrMi0TgDz4yrWadQlMjdZz1H02kjd3Lp/KlH0DGYsJlQ9B67DEPLkohWKHoNoFl40dZOcMXccrKPm\nOUIJNLluypjxQ4Xo0Qxxlqr22uevwdGrhnP3rSj0Fg5+EaJnmMGeDc4/8VA897CRWX9/IaduOiXX\n6LWr0FkzFovFiiWG1M2BiqsEo4Ccuhm91j+nZbIIRCgUPY3oSTXV+jPjtif24uB0E0HIccJfXIOv\n3by5wB61xr7Jhshc6QZ6IXUz5FxN3bRErzTIh76sc4M2m7f9TpHQ6x/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MInshdTOq0Quj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\ni4hq3Pb2bdi4uD98XArdLP+cT155Au55Yj+WDHZj1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw(a[50:], c[50:])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Learning rate decay\n", "\n", "- замедлять скорость обучения по ходу обучения\n", "\n", "```\n", "max_learning_rate = 0.003\n", "min_learning_rate = 0.0001\n", "decay_speed = 2000.0\n", "learning_rate = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i/decay_speed)\n", "```" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def train_lr_decay(X, Y, Ylogits, Y_, mnist, epoches=1000, verbose=100):\n", " cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)\n", " cross_entropy = tf.reduce_mean(cross_entropy)*100\n", " \n", " is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))\n", " accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n", "\n", " max_learning_rate = 0.003\n", " min_learning_rate = 0.0001\n", " decay_speed = 2000.0\n", " learning_rate = tf.placeholder(tf.float32)\n", " \n", " optimizer = tf.train.AdamOptimizer(learning_rate)\n", " train_step = optimizer.minimize(cross_entropy)\n", "\n", " accuracy_vis, cross_entropy_vis = [], []\n", " \n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", " for i in range(epoches):\n", " batch_X, batch_Y = mnist.train.next_batch(100)\n", " \n", " lr = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i/decay_speed)\n", " train_data={X: batch_X, Y_: batch_Y, learning_rate: lr}\n", "\n", " # train\n", " sess.run(train_step, feed_dict=train_data)\n", "\n", " if i % 10 == 0:\n", " test_data={X: mnist.test.images, Y_: mnist.test.labels}\n", " a, c = sess.run([accuracy, cross_entropy], feed_dict=test_data)\n", " accuracy_vis.append(a)\n", " cross_entropy_vis.append(c)\n", " if i % verbose == 0:\n", " print(\"Test: {}, {}\".format(a, c))\n", " return (accuracy_vis, cross_entropy_vis)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.1859000027179718, 227.35543823242188\n", "Test: 0.9544000029563904, 14.740668296813965\n", "Test: 0.9699000120162964, 9.963876724243164\n", "Test: 0.9735999703407288, 9.058289527893066\n", "Test: 0.9779999852180481, 7.636318206787109\n", "Test: 0.9793999791145325, 7.169418811798096\n", "Test: 0.9789000153541565, 7.904794216156006\n", "Test: 0.9783999919891357, 7.831852912902832\n", "Test: 0.9796000123023987, 8.334189414978027\n", "Test: 0.9797999858856201, 8.294150352478027\n", "Test: 0.9807000160217285, 8.431187629699707\n", "Test: 0.9810000061988831, 8.371230125427246\n", "Test: 0.9807999730110168, 8.59482479095459\n", "Test: 0.9807000160217285, 8.825003623962402\n", "Test: 0.98089998960495, 9.139716148376465\n", "Test: 0.9807000160217285, 9.22861099243164\n", "Test: 0.9807999730110168, 9.49561882019043\n", "Test: 0.9811000227928162, 9.663046836853027\n", "Test: 0.9805999994277954, 9.864192962646484\n", "Test: 0.9810000061988831, 10.201659202575684\n" ] } ], "source": [ "a, c = train_lr_decay(X, Y, Ylogits, Y_, mnist, 10000)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Переобучение" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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3MtaSAUC/ruHXH9EzAyKCJb87A0UV9n+blx7fF59tOBBxv/9eewJ25JXh/jmb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw(a[50:], c[50:])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Drop Out\n", "\n", "![](images/dropout.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "```python\n", "pkeep = tf.placeholder(tf.float32)\n", "Y1d = tf.nn.dropout(Y1, pkeep)\n", "\n", "train_data={X: batch_X, Y_: batch_Y, learning_rate: lr, pkeep: 0.75}\n", "test_data={X: mnist.test.images, Y_: mnist.test.labels, pkeep: 1.0}\n", "```" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def train_lr_decay_dropout(X, Y, Ylogits, Y_, pkeep, mnist, epoches=1000):\n", " cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)\n", " cross_entropy = tf.reduce_mean(cross_entropy)*100\n", " \n", " is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))\n", " accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n", "\n", " max_learning_rate = 0.003\n", " min_learning_rate = 0.0001\n", " decay_speed = 2000.0\n", " learning_rate = tf.placeholder(tf.float32)\n", " \n", " optimizer = tf.train.AdamOptimizer(learning_rate)\n", " train_step = optimizer.minimize(cross_entropy)\n", "\n", " accuracy_vis, cross_entropy_vis = [], []\n", " \n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", " for i in range(epoches):\n", " batch_X, batch_Y = mnist.train.next_batch(100)\n", " \n", " lr = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i/decay_speed)\n", " train_data={X: batch_X, Y_: batch_Y, learning_rate: lr, pkeep: 0.75}\n", "\n", " # train\n", " sess.run(train_step, feed_dict=train_data)\n", "\n", " if i % 10 == 0:\n", " test_data={X: mnist.test.images, Y_: mnist.test.labels, pkeep: 1.0}\n", " a, c = sess.run([accuracy, cross_entropy], feed_dict=test_data)\n", " accuracy_vis.append(a)\n", " cross_entropy_vis.append(c)\n", " if i % 500 == 0:\n", " print(\"Test: {}, {}\".format(a, c))\n", " return (accuracy_vis, cross_entropy_vis)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "pkeep = tf.placeholder(tf.float32)\n", "\n", "X = tf.placeholder(tf.float32, [None, 784])\n", "\n", "Y1 = tf.nn.dropout(tf.nn.relu(tf.matmul(X, W1) + B1), pkeep)\n", "Y2 = tf.nn.dropout(tf.nn.relu(tf.matmul(Y1, W2) + B2), pkeep)\n", "Y3 = tf.nn.dropout(tf.nn.relu(tf.matmul(Y2, W3) + B3), pkeep)\n", "Y4 = tf.nn.dropout(tf.nn.relu(tf.matmul(Y3, W4) + B4), pkeep)\n", "\n", "Ylogits = tf.matmul(Y4, W5) + B5\n", "Y = tf.nn.softmax(Ylogits)\n", "Y_ = tf.placeholder(tf.float32, [None, 10])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.13210000097751617, 227.8106689453125\n", "Test: 0.9544000029563904, 16.029678344726562\n", "Test: 0.9645000100135803, 12.972384452819824\n", "Test: 0.9684000015258789, 11.261515617370605\n", "Test: 0.9739999771118164, 9.899155616760254\n", "Test: 0.9739999771118164, 9.44610595703125\n", "Test: 0.9740999937057495, 10.034586906433105\n", "Test: 0.9758999943733215, 9.311582565307617\n", "Test: 0.9785000085830688, 9.393570899963379\n", "Test: 0.9781000018119812, 9.309616088867188\n", "Test: 0.979200005531311, 8.980360984802246\n", "Test: 0.9786999821662903, 8.907164573669434\n", "Test: 0.9789000153541565, 8.919472694396973\n", "Test: 0.9796000123023987, 9.136421203613281\n", "Test: 0.9800999760627747, 8.896743774414062\n", "Test: 0.9789999723434448, 9.251812934875488\n", "Test: 0.980400025844574, 9.065969467163086\n", "Test: 0.9796000123023987, 9.546613693237305\n", "Test: 0.9800999760627747, 9.150278091430664\n", "Test: 0.9801999926567078, 9.352210998535156\n" ] } ], "source": [ "a, c = train_lr_decay_dropout(X, Y, Ylogits, Y_, pkeep, mnist, 10000)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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PZ1RYBICq+sDMYFWdF4/O24rzn16inWvTIazcXYy/f74dXp8fhXoxEL9fwR/0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw(a[50:], c[50:])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Резюме\n", "\n", "- 5 слоёв\n", "- relu & softmax\n", "- learning rate decay\n", "- dropout" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 0.98\n", "\n", "![](images/good_success.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Свёрточная нейросеть" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![](images/common_cnn_design.jpg)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/1.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![](images/2.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![](images/3.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](images/conv_layers.png)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True, reshape=False)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "X = tf.placeholder(tf.float32, [None, 28, 28, 1])\n", "Y_ = tf.placeholder(tf.float32, [None, 10])\n", "lr = tf.placeholder(tf.float32)\n", "\n", "K = 4 # first convolutional layer output depth\n", "L = 8 # second convolutional layer output depth\n", "M = 12 # third convolutional layer\n", "N = 200 # fully connected layer" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# сверточные слои\n", "W1 = tf.Variable(tf.truncated_normal([5, 5, 1, K], stddev=0.1))\n", "B1 = tf.Variable(tf.ones([K])/10)\n", "W2 = tf.Variable(tf.truncated_normal([5, 5, K, L], stddev=0.1))\n", "B2 = tf.Variable(tf.ones([L])/10)\n", "W3 = tf.Variable(tf.truncated_normal([4, 4, L, M], stddev=0.1))\n", "B3 = tf.Variable(tf.ones([M])/10)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# полносвязный слой\n", "W4 = tf.Variable(tf.truncated_normal([7 * 7 * M, N], stddev=0.1))\n", "B4 = tf.Variable(tf.ones([N])/10)\n", "W5 = tf.Variable(tf.truncated_normal([N, 10], stddev=0.1))\n", "B5 = tf.Variable(tf.ones([10])/10)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "stride = 1 # output is 28x28\n", "Y1 = tf.nn.relu(tf.nn.conv2d(X, W1, strides=[1, stride, stride, 1], padding='SAME') + B1)\n", "stride = 2 # output is 14x14\n", "Y2 = tf.nn.relu(tf.nn.conv2d(Y1, W2, strides=[1, stride, stride, 1], padding='SAME') + B2)\n", "stride = 2 # output is 7x7\n", "Y3 = tf.nn.relu(tf.nn.conv2d(Y2, W3, strides=[1, stride, stride, 1], padding='SAME') + B3)\n", "\n", "# меняем размерность выхода из последнего сверточного слоая в полносвязный слой\n", "YY = tf.reshape(Y3, shape=[-1, 7 * 7 * M])\n", "\n", "Y4 = tf.nn.relu(tf.matmul(YY, W4) + B4)\n", "Ylogits = tf.matmul(Y4, W5) + B5\n", "Y = tf.nn.softmax(Ylogits)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.10379999876022339, 228.32412719726562\n", "Test: 0.9330000281333923, 21.54973602294922\n", "Test: 0.9614999890327454, 13.37433910369873\n", "Test: 0.968500018119812, 10.562755584716797\n", "Test: 0.9718999862670898, 8.294029235839844\n", "Test: 0.9785000085830688, 6.942697048187256\n", "Test: 0.978600025177002, 6.892008304595947\n", "Test: 0.9764000177383423, 6.874610424041748\n", "Test: 0.9804999828338623, 6.037387847900391\n", "Test: 0.98089998960495, 6.129174709320068\n" ] } ], "source": [ "a, c = train_lr_decay(X, Y, Ylogits, Y_, mnist, epoches=1000, verbose=100)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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VhgtdCgAABcOS/wBQPIoutLX1JVUdjyoQsEKXAgBAwXBzbQAoHkUY2hLcWBsA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\np9eN3OB9IjJZp6xzJ9QscM5pT8eASsL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