{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n", "- Author: Sebastian Raschka\n", "- GitHub Repository: https://github.com/rasbt/deeplearning-models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sebastian Raschka \n", "\n", "CPython 3.6.8\n", "IPython 7.2.0\n", "\n", "tensorflow 1.12.0\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -a 'Sebastian Raschka' -v -p tensorflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Zoo -- Convolutional Neural Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Low-level Implementation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From :10: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n", "WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please write your own downloading logic.\n", "WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use tf.data to implement this functionality.\n", "Extracting ./train-images-idx3-ubyte.gz\n", "WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use tf.data to implement this functionality.\n", "Extracting ./train-labels-idx1-ubyte.gz\n", "WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use tf.one_hot on tensors.\n", "Extracting ./t10k-images-idx3-ubyte.gz\n", "Extracting ./t10k-labels-idx1-ubyte.gz\n", "WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n" ] } ], "source": [ "import tensorflow as tf\n", "from functools import reduce\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "\n", "\n", "##########################\n", "### DATASET\n", "##########################\n", "\n", "mnist = input_data.read_data_sets(\"./\", one_hot=True)\n", "\n", "\n", "##########################\n", "### SETTINGS\n", "##########################\n", "\n", "# Hyperparameters\n", "learning_rate = 0.1\n", "dropout_keep_proba = 0.5\n", "epochs = 3\n", "batch_size = 32\n", "\n", "# Architecture\n", "input_size = 784\n", "image_width, image_height = 28, 28\n", "n_classes = 10\n", "\n", "# Other\n", "print_interval = 500\n", "random_seed = 123\n", "\n", "\n", "##########################\n", "### WRAPPER FUNCTIONS\n", "##########################\n", "\n", "def conv2d(input_tensor, output_channels,\n", " kernel_size=(5, 5), strides=(1, 1, 1, 1),\n", " padding='SAME', activation=None, seed=None,\n", " name='conv2d'):\n", "\n", " with tf.name_scope(name):\n", " input_channels = input_tensor.get_shape().as_list()[-1]\n", " weights_shape = (kernel_size[0], kernel_size[1],\n", " input_channels, output_channels)\n", "\n", " weights = tf.Variable(tf.truncated_normal(shape=weights_shape,\n", " mean=0.0,\n", " stddev=0.01,\n", " dtype=tf.float32,\n", " seed=seed),\n", " name='weights')\n", " biases = tf.Variable(tf.zeros(shape=(output_channels,)), name='biases')\n", " conv = tf.nn.conv2d(input=input_tensor,\n", " filter=weights,\n", " strides=strides,\n", " padding=padding)\n", "\n", " act = conv + biases\n", " if activation is not None:\n", " act = activation(conv + biases)\n", " return act\n", "\n", "\n", "def fully_connected(input_tensor, output_nodes,\n", " activation=None, seed=None,\n", " name='fully_connected'):\n", "\n", " with tf.name_scope(name):\n", " input_nodes = input_tensor.get_shape().as_list()[1]\n", " weights = tf.Variable(tf.truncated_normal(shape=(input_nodes,\n", " output_nodes),\n", " mean=0.0,\n", " stddev=0.01,\n", " dtype=tf.float32,\n", " seed=seed),\n", " name='weights')\n", " biases = tf.Variable(tf.zeros(shape=[output_nodes]), name='biases')\n", "\n", " act = tf.matmul(input_tensor, weights) + biases\n", " if activation is not None:\n", " act = activation(act)\n", " return act\n", "\n", " \n", "##########################\n", "### GRAPH DEFINITION\n", "##########################\n", "\n", "g = tf.Graph()\n", "with g.as_default():\n", " \n", " tf.set_random_seed(random_seed)\n", "\n", " # Input data\n", " tf_x = tf.placeholder(tf.float32, [None, input_size, 1], name='inputs')\n", " tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')\n", " \n", " keep_proba = tf.placeholder(tf.float32, shape=None, name='keep_proba')\n", "\n", " # Convolutional Neural Network:\n", " # 2 convolutional layers with maxpool and ReLU activation\n", " input_layer = tf.reshape(tf_x, shape=[-1, image_width, image_height, 1])\n", " \n", " conv1 = conv2d(input_tensor=input_layer,\n", " output_channels=8,\n", " kernel_size=(3, 3),\n", " strides=(1, 1, 1, 1),\n", " activation=tf.nn.relu,\n", " name='conv1')\n", " \n", " pool1 = tf.nn.max_pool(conv1,\n", " ksize=(1, 2, 2, 1), \n", " strides=(1, 2, 2, 1),\n", " padding='SAME',\n", " name='maxpool1')\n", " \n", " conv2 = conv2d(input_tensor=pool1,\n", " output_channels=16,\n", " kernel_size=(3, 3),\n", " strides=(1, 1, 1, 1),\n", " activation=tf.nn.relu,\n", " name='conv2')\n", " \n", " pool2 = tf.nn.max_pool(conv2,\n", " ksize=(1, 2, 2, 1), \n", " strides=(1, 2, 2, 1),\n", " padding='SAME',\n", " name='maxpool2')\n", " \n", " dims = pool2.get_shape().as_list()[1:]\n", " dims = reduce(lambda x, y: x * y, dims, 1)\n", " flat = tf.reshape(pool2, shape=(-1, dims))\n", " \n", " out_layer = fully_connected(flat, n_classes, activation=None, \n", " name='logits')\n", "\n", " # Loss and optimizer\n", " loss = tf.nn.softmax_cross_entropy_with_logits_v2(logits=out_layer, labels=tf_y)\n", " cost = tf.reduce_mean(loss, name='cost')\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", " train = optimizer.minimize(cost, name='train')\n", "\n", " # Prediction\n", " correct_prediction = tf.equal(tf.argmax(tf_y, 1), \n", " tf.argmax(out_layer, 1), \n", " name='correct_prediction')\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \n", " tf.float32), \n", " name='accuracy')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minibatch: 001 | Cost: 2.303\n", "Minibatch: 501 | Cost: 0.225\n", "Minibatch: 1001 | Cost: 0.106\n", "Minibatch: 1501 | Cost: 0.039\n", "Epoch: 001 | AvgCost: 0.530 | Train/Valid ACC: 0.966/0.964\n", "Minibatch: 001 | Cost: 0.051\n", "Minibatch: 501 | Cost: 0.035\n", "Minibatch: 1001 | Cost: 0.043\n", "Minibatch: 1501 | Cost: 0.058\n", "Epoch: 002 | AvgCost: 0.102 | Train/Valid ACC: 0.967/0.968\n", "Minibatch: 001 | Cost: 0.019\n", "Minibatch: 501 | Cost: 0.132\n", "Minibatch: 1001 | Cost: 0.064\n", "Minibatch: 1501 | Cost: 0.011\n", "Epoch: 003 | AvgCost: 0.076 | Train/Valid ACC: 0.978/0.978\n", "Test ACC: 0.980\n" ] } ], "source": [ "import numpy as np\n", "\n", "##########################\n", "### TRAINING & EVALUATION\n", "##########################\n", "\n", "with tf.Session(graph=g) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " np.random.seed(random_seed) # random seed for mnist iterator\n", " for epoch in range(1, epochs + 1):\n", " avg_cost = 0.\n", " total_batch = mnist.train.num_examples // batch_size\n", "\n", " for i in range(total_batch):\n", " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", " batch_x = batch_x[:, :, None] # add \"missing\" color channel\n", " \n", " _, c = sess.run(['train', 'cost:0'], \n", " feed_dict={'inputs:0': batch_x,\n", " 'targets:0': batch_y,\n", " 'keep_proba:0': dropout_keep_proba})\n", " avg_cost += c\n", " if not i % print_interval:\n", " print(\"Minibatch: %03d | Cost: %.3f\" % (i + 1, c))\n", " \n", " train_acc = sess.run('accuracy:0', \n", " feed_dict={'inputs:0': mnist.train.images[:, :, None],\n", " 'targets:0': mnist.train.labels,\n", " 'keep_proba:0': 1.0})\n", " valid_acc = sess.run('accuracy:0', \n", " feed_dict={'inputs:0': mnist.validation.images[:, :, None],\n", " 'targets:0': mnist.validation.labels,\n", " 'keep_proba:0': 1.0})\n", " \n", " print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch, avg_cost / (i + 1)), end=\"\")\n", " print(\" | Train/Valid ACC: %.3f/%.3f\" % (train_acc, valid_acc))\n", " \n", " test_acc = sess.run('accuracy:0', \n", " feed_dict={'inputs:0': mnist.test.images[:, :, None],\n", " 'targets:0': mnist.test.labels,\n", " 'keep_proba:0': 1.0})\n", " \n", " print('Test ACC: %.3f' % test_acc)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }