wehub-resource-sync
576 lines
20 KiB
Plaintext
576 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
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"- Author: Sebastian Raschka\n",
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"- GitHub Repository: https://github.com/rasbt/deeplearning-models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sebastian Raschka \n",
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"\n",
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"CPython 3.6.1\n",
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"IPython 6.0.0\n",
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"\n",
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"tensorflow 1.2.0\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -a 'Sebastian Raschka' -v -p tensorflow"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"from tensorflow.examples.tutorials.mnist import input_data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Model Zoo -- Multilayer Perceptron with Backpropagation from Scratch"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook contains three different approaches for training a simple 1-hidden layer multilayer perceptron using TensorFlow:\n",
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" \n",
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"- Gradient descent via the \"high-level\" `tf.train.GradientDescentOptimizer`\n",
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"- A lower-level implementation to perform backpropagation via `tf.gradients`\n",
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"- An implementation of backpropagation and gradient descent learning based on basic linear algebra operations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Gradient Descent Using `tf.train.GradientDescentOptimizer`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Extracting ./train-images-idx3-ubyte.gz\n",
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"Extracting ./train-labels-idx1-ubyte.gz\n",
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"Extracting ./t10k-images-idx3-ubyte.gz\n",
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"Extracting ./t10k-labels-idx1-ubyte.gz\n"
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]
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}
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],
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"source": [
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"# Dataset\n",
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"np.random.seed(123) # set seed for mnist shuffling\n",
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"mnist = input_data.read_data_sets(\"./\", one_hot=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch: 001 | AvgCost: 0.785 | Train/Valid ACC: 0.885/0.891\n",
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"Epoch: 002 | AvgCost: 0.370 | Train/Valid ACC: 0.906/0.915\n",
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"Epoch: 003 | AvgCost: 0.317 | Train/Valid ACC: 0.914/0.921\n",
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"Epoch: 004 | AvgCost: 0.289 | Train/Valid ACC: 0.922/0.925\n",
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"Epoch: 005 | AvgCost: 0.268 | Train/Valid ACC: 0.926/0.929\n",
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"Epoch: 006 | AvgCost: 0.250 | Train/Valid ACC: 0.931/0.933\n",
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"Epoch: 007 | AvgCost: 0.235 | Train/Valid ACC: 0.936/0.937\n",
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"Epoch: 008 | AvgCost: 0.221 | Train/Valid ACC: 0.939/0.941\n",
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"Epoch: 009 | AvgCost: 0.209 | Train/Valid ACC: 0.943/0.943\n",
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"Epoch: 010 | AvgCost: 0.198 | Train/Valid ACC: 0.947/0.948\n",
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"Test ACC: 0.945\n"
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]
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}
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],
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"source": [
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"##########################\n",
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"### SETTINGS\n",
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"##########################\n",
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"\n",
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"# Hyperparameters\n",
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"learning_rate = 0.1\n",
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"training_epochs = 10\n",
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"batch_size = 64\n",
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"\n",
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"# Architecture\n",
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"n_hidden_1 = 128\n",
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"n_input = 784\n",
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"n_classes = 10\n",
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"\n",
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"\n",
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"##########################\n",
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"### GRAPH DEFINITION\n",
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"##########################\n",
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"\n",
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"g = tf.Graph()\n",
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"with g.as_default():\n",
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" \n",
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" tf.set_random_seed(123)\n",
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"\n",
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" # Input data\n",
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" tf_x = tf.placeholder(tf.float32, [None, n_input], name='features')\n",
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" tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')\n",
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"\n",
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" # Model parameters\n",
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" weights = {\n",
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" 'h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1], stddev=0.1)),\n",
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" 'out': tf.Variable(tf.truncated_normal([n_hidden_1, n_classes], stddev=0.1))\n",
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" }\n",
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" biases = {\n",
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" 'b1': tf.Variable(tf.zeros([n_hidden_1])),\n",
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" 'out': tf.Variable(tf.zeros([n_classes]))\n",
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" }\n",
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"\n",
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" # Forward Propagation\n",
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" h1_z = tf.add(tf.matmul(tf_x, weights['h1']), biases['b1'])\n",
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" h1_act = tf.nn.sigmoid(h1_z)\n",
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" out_z = tf.matmul(h1_act, weights['out']) + biases['out']\n",
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" out_act = tf.nn.softmax(out_z, name='predicted_probabilities')\n",
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" out_labels = tf.argmax(out_z, axis=1, name='predicted_labels')\n",
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" \n",
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" ######################\n",
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" # Forward Propagation\n",
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" ######################\n",
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"\n",
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" loss = tf.nn.softmax_cross_entropy_with_logits(logits=out_z, labels=tf_y)\n",
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" cost = tf.reduce_mean(loss, name='cost')\n",
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" \n",
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" ##################\n",
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" # Backpropagation\n",
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" ##################\n",
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"\n",
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" optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
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" train = optimizer.minimize(cost, name='train')\n",
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"\n",
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" ##############\n",
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" # Prediction\n",
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" ##############\n",
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"\n",
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" correct_prediction = tf.equal(tf.argmax(tf_y, 1), out_labels)\n",
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" accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')\n",
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"\n",
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" \n",
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"##########################\n",
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"### TRAINING & EVALUATION\n",
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"##########################\n",
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"\n",
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"with tf.Session(graph=g) as sess:\n",
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" sess.run(tf.global_variables_initializer())\n",
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"\n",
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" for epoch in range(training_epochs):\n",
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" avg_cost = 0.\n",
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" total_batch = mnist.train.num_examples // batch_size\n",
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"\n",
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" for i in range(total_batch):\n",
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" batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
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" _, c = sess.run(['train', 'cost:0'], feed_dict={'features:0': batch_x,\n",
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" 'targets:0': batch_y})\n",
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" avg_cost += c\n",
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" \n",
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" train_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.train.images,\n",
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" 'targets:0': mnist.train.labels})\n",
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" valid_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.validation.images,\n",
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" 'targets:0': mnist.validation.labels}) \n",
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" \n",
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" print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch + 1, avg_cost / (i + 1)), end=\"\")\n",
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" print(\" | Train/Valid ACC: %.3f/%.3f\" % (train_acc, valid_acc))\n",
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" \n",
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" test_acc = sess.run(accuracy, feed_dict={'features:0': mnist.test.images,\n",
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" 'targets:0': mnist.test.labels})\n",
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" print('Test ACC: %.3f' % test_acc)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Gradient Descent Using `tf.gradients` (low level)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Extracting ./train-images-idx3-ubyte.gz\n",
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"Extracting ./train-labels-idx1-ubyte.gz\n",
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"Extracting ./t10k-images-idx3-ubyte.gz\n",
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"Extracting ./t10k-labels-idx1-ubyte.gz\n"
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]
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}
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],
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"source": [
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"# Dataset\n",
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"np.random.seed(123) # set seed for mnist shuffling\n",
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"mnist = input_data.read_data_sets(\"./\", one_hot=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch: 001 | AvgCost: 0.785 | Train/Valid ACC: 0.890/0.894\n",
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"Epoch: 002 | AvgCost: 0.370 | Train/Valid ACC: 0.906/0.912\n",
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"Epoch: 003 | AvgCost: 0.317 | Train/Valid ACC: 0.915/0.918\n",
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"Epoch: 004 | AvgCost: 0.289 | Train/Valid ACC: 0.922/0.926\n",
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"Epoch: 005 | AvgCost: 0.268 | Train/Valid ACC: 0.927/0.930\n",
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"Epoch: 006 | AvgCost: 0.250 | Train/Valid ACC: 0.932/0.934\n",
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"Epoch: 007 | AvgCost: 0.235 | Train/Valid ACC: 0.936/0.938\n",
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"Epoch: 008 | AvgCost: 0.221 | Train/Valid ACC: 0.940/0.941\n",
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"Epoch: 009 | AvgCost: 0.210 | Train/Valid ACC: 0.942/0.944\n",
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"Epoch: 010 | AvgCost: 0.198 | Train/Valid ACC: 0.946/0.947\n",
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"Test ACC: 0.945\n"
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]
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}
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],
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"source": [
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"##########################\n",
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"### SETTINGS\n",
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"##########################\n",
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"\n",
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"# Hyperparameters\n",
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"learning_rate = 0.1\n",
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"training_epochs = 10\n",
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"batch_size = 64\n",
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"\n",
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"# Architecture\n",
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"n_hidden_1 = 128\n",
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"n_input = 784\n",
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"n_classes = 10\n",
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"\n",
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"\n",
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"##########################\n",
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"### GRAPH DEFINITION\n",
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"##########################\n",
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"\n",
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"g = tf.Graph()\n",
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"with g.as_default():\n",
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" \n",
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" tf.set_random_seed(123)\n",
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"\n",
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" # Input data\n",
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" tf_x = tf.placeholder(tf.float32, [None, n_input], name='features')\n",
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" tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')\n",
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"\n",
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" # Model parameters\n",
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" weights = {\n",
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" 'h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1], stddev=0.1)),\n",
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" 'out': tf.Variable(tf.truncated_normal([n_hidden_1, n_classes], stddev=0.1))\n",
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" }\n",
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" biases = {\n",
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" 'b1': tf.Variable(tf.zeros([n_hidden_1])),\n",
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" 'out': tf.Variable(tf.zeros([n_classes]))\n",
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" }\n",
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"\n",
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" ######################\n",
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" # Forward Propagation\n",
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" ######################\n",
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"\n",
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" h1_z = tf.add(tf.matmul(tf_x, weights['h1']), biases['b1'])\n",
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" h1_act = tf.nn.sigmoid(h1_z)\n",
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" out_z = tf.matmul(h1_act, weights['out']) + biases['out']\n",
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" out_act = tf.nn.softmax(out_z, name='predicted_probabilities')\n",
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" out_labels = tf.argmax(out_z, axis=1, name='predicted_labels')\n",
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" \n",
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" # Loss\n",
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" loss = tf.nn.softmax_cross_entropy_with_logits(logits=out_z, labels=tf_y)\n",
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" cost = tf.reduce_mean(loss, name='cost')\n",
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" \n",
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" ##################\n",
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" # Backpropagation\n",
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" ##################\n",
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"\n",
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" # Get Gradients\n",
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" dc_dw_out, dc_db_out = tf.gradients(cost, [weights['out'], biases['out']])\n",
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" dc_dw_1, dc_db_1 = tf.gradients(cost, [weights['h1'], biases['b1']])\n",
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" \n",
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" # Update Weights\n",
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" upd_w_1 = tf.assign(weights['h1'], weights['h1'] - learning_rate * dc_dw_1)\n",
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" upd_b_1 = tf.assign(biases['b1'], biases['b1'] - learning_rate * dc_db_1)\n",
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" upd_w_out = tf.assign(weights['out'], weights['out'] - learning_rate * dc_dw_out)\n",
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" upd_b_out = tf.assign(biases['out'], biases['out'] - learning_rate * dc_db_out)\n",
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" \n",
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" train = tf.group(upd_w_1, upd_b_1, upd_w_out, upd_b_out, name='train')\n",
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"\n",
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" ##############\n",
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" # Prediction\n",
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" ##############\n",
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"\n",
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" correct_prediction = tf.equal(tf.argmax(tf_y, 1), out_labels)\n",
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" accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')\n",
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"\n",
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" \n",
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"##########################\n",
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"### TRAINING & EVALUATION\n",
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"##########################\n",
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"\n",
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"with tf.Session(graph=g) as sess:\n",
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" sess.run(tf.global_variables_initializer())\n",
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"\n",
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" for epoch in range(training_epochs):\n",
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" avg_cost = 0.\n",
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" total_batch = mnist.train.num_examples // batch_size\n",
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"\n",
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" for i in range(total_batch):\n",
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" batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
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" _, c = sess.run(['train', 'cost:0'], feed_dict={'features:0': batch_x,\n",
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" 'targets:0': batch_y})\n",
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" avg_cost += c\n",
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" \n",
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" train_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.train.images,\n",
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" 'targets:0': mnist.train.labels})\n",
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" valid_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.validation.images,\n",
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" 'targets:0': mnist.validation.labels}) \n",
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" \n",
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" print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch + 1, avg_cost / (i + 1)), end=\"\")\n",
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" print(\" | Train/Valid ACC: %.3f/%.3f\" % (train_acc, valid_acc))\n",
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" \n",
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" test_acc = sess.run(accuracy, feed_dict={'features:0': mnist.test.images,\n",
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" 'targets:0': mnist.test.labels})\n",
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" print('Test ACC: %.3f' % test_acc)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Gradient Descent from scratch (very low level)"
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]
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},
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|
{
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"cell_type": "code",
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|
"execution_count": 7,
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|
"metadata": {},
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|
"outputs": [
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|
{
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|
"name": "stdout",
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|
"output_type": "stream",
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"text": [
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|
"Extracting ./train-images-idx3-ubyte.gz\n",
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"Extracting ./train-labels-idx1-ubyte.gz\n",
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"Extracting ./t10k-images-idx3-ubyte.gz\n",
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"Extracting ./t10k-labels-idx1-ubyte.gz\n"
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]
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|
}
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|
],
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"source": [
|
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"# Dataset\n",
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"np.random.seed(123) # set seed for mnist shuffling\n",
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"mnist = input_data.read_data_sets(\"./\", one_hot=True)"
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
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|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch: 001 | AvgCost: 0.785 | Train/Valid ACC: 0.884/0.892\n",
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"Epoch: 002 | AvgCost: 0.370 | Train/Valid ACC: 0.905/0.909\n",
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"Epoch: 003 | AvgCost: 0.317 | Train/Valid ACC: 0.914/0.916\n",
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"Epoch: 004 | AvgCost: 0.288 | Train/Valid ACC: 0.921/0.926\n",
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"Epoch: 005 | AvgCost: 0.268 | Train/Valid ACC: 0.927/0.931\n",
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"Epoch: 006 | AvgCost: 0.251 | Train/Valid ACC: 0.931/0.934\n",
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"Epoch: 007 | AvgCost: 0.235 | Train/Valid ACC: 0.936/0.937\n",
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"Epoch: 008 | AvgCost: 0.222 | Train/Valid ACC: 0.940/0.942\n",
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"Epoch: 009 | AvgCost: 0.209 | Train/Valid ACC: 0.944/0.944\n",
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"Epoch: 010 | AvgCost: 0.199 | Train/Valid ACC: 0.946/0.948\n",
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"Test ACC: 0.945\n"
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]
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}
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],
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"source": [
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"##########################\n",
|
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"### SETTINGS\n",
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"##########################\n",
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"\n",
|
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"# Hyperparameters\n",
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"learning_rate = 0.1\n",
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"training_epochs = 10\n",
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"batch_size = 64\n",
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"\n",
|
|
"# Architecture\n",
|
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"n_hidden_1 = 128\n",
|
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"n_input = 784\n",
|
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"n_classes = 10\n",
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"\n",
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"\n",
|
|
"##########################\n",
|
|
"### GRAPH DEFINITION\n",
|
|
"##########################\n",
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"\n",
|
|
"g = tf.Graph()\n",
|
|
"with g.as_default():\n",
|
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" \n",
|
|
" tf.set_random_seed(123)\n",
|
|
"\n",
|
|
" # Input data\n",
|
|
" tf_x = tf.placeholder(tf.float32, [None, n_input], name='features')\n",
|
|
" tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')\n",
|
|
"\n",
|
|
" # Model parameters\n",
|
|
" weights = {\n",
|
|
" 'h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1], stddev=0.1)),\n",
|
|
" 'out': tf.Variable(tf.truncated_normal([n_hidden_1, n_classes], stddev=0.1))\n",
|
|
" }\n",
|
|
" biases = {\n",
|
|
" 'b1': tf.Variable(tf.zeros([n_hidden_1])),\n",
|
|
" 'out': tf.Variable(tf.zeros([n_classes]))\n",
|
|
" }\n",
|
|
"\n",
|
|
" ######################\n",
|
|
" # Forward Propagation\n",
|
|
" ######################\n",
|
|
" \n",
|
|
" h1_z = tf.add(tf.matmul(tf_x, weights['h1']), biases['b1'])\n",
|
|
" h1_act = tf.nn.sigmoid(h1_z)\n",
|
|
" out_z = tf.matmul(h1_act, weights['out']) + biases['out']\n",
|
|
" out_act = tf.nn.softmax(out_z, name='predicted_probabilities')\n",
|
|
" out_labels = tf.argmax(out_z, axis=1, name='predicted_labels')\n",
|
|
" \n",
|
|
" # Loss\n",
|
|
" loss = tf.nn.softmax_cross_entropy_with_logits(logits=out_z, labels=tf_y)\n",
|
|
" cost = tf.reduce_mean(loss, name='cost')\n",
|
|
" \n",
|
|
" ##################\n",
|
|
" # Backpropagation\n",
|
|
" ##################\n",
|
|
" \n",
|
|
" # Get Gradients\n",
|
|
" \n",
|
|
" # input/output dim: [n_samples, n_classlabels]\n",
|
|
" sigma_out = (out_act - tf_y) / batch_size\n",
|
|
" \n",
|
|
" # input/output dim: [n_samples, n_hidden_1]\n",
|
|
" softmax_derivative_h1 = h1_act * (1. - h1_act)\n",
|
|
" \n",
|
|
" # input dim: [n_samples, n_classlabels] dot [n_classlabels, n_hidden]\n",
|
|
" # output dim: [n_samples, n_hidden]\n",
|
|
" sigma_h = (tf.matmul(sigma_out, tf.transpose(weights['out'])) *\n",
|
|
" softmax_derivative_h1)\n",
|
|
" \n",
|
|
" # input dim: [n_features, n_samples] dot [n_samples, n_hidden]\n",
|
|
" # output dim: [n_features, n_hidden]\n",
|
|
" grad_w_h1 = tf.matmul(tf.transpose(tf_x), sigma_h)\n",
|
|
" grad_b_h1 = tf.reduce_sum(sigma_h, axis=0)\n",
|
|
"\n",
|
|
" # input dim: [n_hidden, n_samples] dot [n_samples, n_classlabels]\n",
|
|
" # output dim: [n_hidden, n_classlabels]\n",
|
|
" grad_w_out = tf.matmul(tf.transpose(h1_act), sigma_out)\n",
|
|
" grad_b_out = tf.reduce_sum(sigma_out, axis=0)\n",
|
|
" \n",
|
|
" # Update weights\n",
|
|
" upd_w_1 = tf.assign(weights['h1'], weights['h1'] - learning_rate * grad_w_h1)\n",
|
|
" upd_b_1 = tf.assign(biases['b1'], biases['b1'] - learning_rate * grad_b_h1)\n",
|
|
" upd_w_out = tf.assign(weights['out'], weights['out'] - learning_rate * grad_w_out)\n",
|
|
" upd_b_out = tf.assign(biases['out'], biases['out'] - learning_rate * grad_b_out)\n",
|
|
" \n",
|
|
" train = tf.group(upd_w_1, upd_b_1, upd_w_out, upd_b_out, name='train')\n",
|
|
" \n",
|
|
" ##############\n",
|
|
" # Prediction\n",
|
|
" ##############\n",
|
|
"\n",
|
|
" correct_prediction = tf.equal(tf.argmax(tf_y, 1), out_labels)\n",
|
|
" accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')\n",
|
|
"\n",
|
|
" \n",
|
|
"##########################\n",
|
|
"### TRAINING & EVALUATION\n",
|
|
"##########################\n",
|
|
"\n",
|
|
"with tf.Session(graph=g) as sess:\n",
|
|
" sess.run(tf.global_variables_initializer())\n",
|
|
"\n",
|
|
" for epoch in range(training_epochs):\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",
|
|
" _, c = sess.run(['train', 'cost:0'], feed_dict={'features:0': batch_x,\n",
|
|
" 'targets:0': batch_y})\n",
|
|
" avg_cost += c\n",
|
|
" \n",
|
|
" train_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.train.images,\n",
|
|
" 'targets:0': mnist.train.labels})\n",
|
|
" valid_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.validation.images,\n",
|
|
" 'targets:0': mnist.validation.labels}) \n",
|
|
" \n",
|
|
" print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch + 1, avg_cost / (i + 1)), end=\"\")\n",
|
|
" print(\" | Train/Valid ACC: %.3f/%.3f\" % (train_acc, valid_acc))\n",
|
|
" \n",
|
|
" test_acc = sess.run(accuracy, feed_dict={'features:0': mnist.test.images,\n",
|
|
" 'targets:0': mnist.test.labels})\n",
|
|
" print('Test ACC: %.3f' % test_acc)"
|
|
]
|
|
}
|
|
],
|
|
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|
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|
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