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Add TensorFlow recurrent neural networks notebook.
This commit is contained in:
Donne Martin
parent
b424fe0ab2
commit
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@ -101,6 +101,7 @@ IPython Notebook(s) demonstrating deep learning functionality.
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| [tsf-alex](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/alexnet.ipynb) | Implement AlexNet in TensorFlow. |
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| [tsf-cnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/convolutional_network.ipynb) | Implement convolutional neural networks in TensorFlow. |
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| [tsf-mlp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/multilayer_perceptron.ipynb) | Implement multilayer perceptrons in TensorFlow. |
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| [tsf-rnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/recurrent_network.ipynb) | Implement recurrent neural networks in TensorFlow. |
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### tensor-flow-exercises
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@ -0,0 +1,294 @@
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{
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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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"# Recurrent Neural Network in TensorFlow\n",
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"\n",
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"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\n",
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"\n",
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"## Setup\n",
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"\n",
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"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)"
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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": false
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},
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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 /tmp/data/train-images-idx3-ubyte.gz\n",
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"Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
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"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
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"Extracting /tmp/data/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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"# Import MINST data\n",
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"import input_data\n",
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"mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow.models.rnn import rnn, rnn_cell\n",
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"import numpy as np"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"'''\n",
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"To classify images using a reccurent neural network, we consider every image row as a sequence of pixels.\n",
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"Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample.\n",
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"'''\n",
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"\n",
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"# Parameters\n",
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"learning_rate = 0.001\n",
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"training_iters = 100000\n",
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"batch_size = 128\n",
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"display_step = 10\n",
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"\n",
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"# Network Parameters\n",
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"n_input = 28 # MNIST data input (img shape: 28*28)\n",
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"n_steps = 28 # timesteps\n",
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"n_hidden = 128 # hidden layer num of features\n",
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"n_classes = 10 # MNIST total classes (0-9 digits)"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# tf Graph input\n",
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"x = tf.placeholder(\"float\", [None, n_steps, n_input])\n",
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"istate = tf.placeholder(\"float\", [None, 2*n_hidden]) #state & cell => 2x n_hidden\n",
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"y = tf.placeholder(\"float\", [None, n_classes])\n",
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"\n",
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"# Define weights\n",
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"weights = {\n",
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" 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights\n",
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" 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n",
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"}\n",
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"biases = {\n",
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" 'hidden': tf.Variable(tf.random_normal([n_hidden])),\n",
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" 'out': tf.Variable(tf.random_normal([n_classes]))\n",
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"}"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def RNN(_X, _istate, _weights, _biases):\n",
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"\n",
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" # input shape: (batch_size, n_steps, n_input)\n",
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" _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size\n",
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" # Reshape to prepare input to hidden activation\n",
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" _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input)\n",
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" # Linear activation\n",
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" _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']\n",
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"\n",
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" # Define a lstm cell with tensorflow\n",
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" lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n",
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" # Split data because rnn cell needs a list of inputs for the RNN inner loop\n",
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" _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)\n",
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"\n",
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" # Get lstm cell output\n",
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" outputs, states = rnn.rnn(lstm_cell, _X, initial_state=_istate)\n",
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"\n",
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" # Linear activation\n",
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" # Get inner loop last output\n",
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" return tf.matmul(outputs[-1], _weights['out']) + _biases['out']"
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"pred = RNN(x, istate, weights, biases)\n",
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"\n",
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"# Define loss and optimizer\n",
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"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss\n",
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"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer\n",
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"\n",
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"# Evaluate model\n",
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"correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
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"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))"
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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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"collapsed": false
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},
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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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"Iter 1280, Minibatch Loss= 1.888242, Training Accuracy= 0.39844\n",
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"Iter 2560, Minibatch Loss= 1.519879, Training Accuracy= 0.47656\n",
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"Iter 3840, Minibatch Loss= 1.238005, Training Accuracy= 0.63281\n",
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"Iter 5120, Minibatch Loss= 0.933760, Training Accuracy= 0.71875\n",
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"Iter 6400, Minibatch Loss= 0.832130, Training Accuracy= 0.73438\n",
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"Iter 7680, Minibatch Loss= 0.979760, Training Accuracy= 0.70312\n",
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"Iter 8960, Minibatch Loss= 0.821921, Training Accuracy= 0.71875\n",
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"Iter 10240, Minibatch Loss= 0.710566, Training Accuracy= 0.79688\n",
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"Iter 11520, Minibatch Loss= 0.578501, Training Accuracy= 0.82812\n",
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"Iter 12800, Minibatch Loss= 0.765049, Training Accuracy= 0.75000\n",
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"Iter 14080, Minibatch Loss= 0.582995, Training Accuracy= 0.78125\n",
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"Iter 15360, Minibatch Loss= 0.575092, Training Accuracy= 0.79688\n",
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"Iter 16640, Minibatch Loss= 0.701214, Training Accuracy= 0.75781\n",
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"Iter 17920, Minibatch Loss= 0.561972, Training Accuracy= 0.78125\n",
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"Iter 19200, Minibatch Loss= 0.394480, Training Accuracy= 0.85938\n",
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"Iter 20480, Minibatch Loss= 0.356244, Training Accuracy= 0.91406\n",
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"Iter 21760, Minibatch Loss= 0.632163, Training Accuracy= 0.78125\n",
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"Iter 23040, Minibatch Loss= 0.269334, Training Accuracy= 0.90625\n",
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"Iter 24320, Minibatch Loss= 0.485007, Training Accuracy= 0.86719\n",
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"Iter 25600, Minibatch Loss= 0.569704, Training Accuracy= 0.78906\n",
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"Iter 26880, Minibatch Loss= 0.267697, Training Accuracy= 0.92188\n",
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"Iter 28160, Minibatch Loss= 0.381177, Training Accuracy= 0.90625\n",
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"Iter 29440, Minibatch Loss= 0.350800, Training Accuracy= 0.87500\n",
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"Iter 30720, Minibatch Loss= 0.356782, Training Accuracy= 0.90625\n",
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"Iter 32000, Minibatch Loss= 0.322511, Training Accuracy= 0.89062\n",
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"Iter 33280, Minibatch Loss= 0.309195, Training Accuracy= 0.90625\n",
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"Iter 34560, Minibatch Loss= 0.535408, Training Accuracy= 0.83594\n",
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"Iter 35840, Minibatch Loss= 0.281643, Training Accuracy= 0.92969\n",
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"Iter 37120, Minibatch Loss= 0.290962, Training Accuracy= 0.89844\n",
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"Iter 38400, Minibatch Loss= 0.204718, Training Accuracy= 0.93750\n",
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"Iter 39680, Minibatch Loss= 0.205882, Training Accuracy= 0.92969\n",
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"Iter 40960, Minibatch Loss= 0.481441, Training Accuracy= 0.84375\n",
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"Iter 42240, Minibatch Loss= 0.348245, Training Accuracy= 0.89844\n",
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"Iter 43520, Minibatch Loss= 0.274692, Training Accuracy= 0.90625\n",
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"Iter 44800, Minibatch Loss= 0.171815, Training Accuracy= 0.94531\n",
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"Iter 46080, Minibatch Loss= 0.171035, Training Accuracy= 0.93750\n",
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"Iter 47360, Minibatch Loss= 0.235800, Training Accuracy= 0.89844\n",
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"Iter 48640, Minibatch Loss= 0.235974, Training Accuracy= 0.93750\n",
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"Iter 49920, Minibatch Loss= 0.207323, Training Accuracy= 0.92188\n",
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"Iter 51200, Minibatch Loss= 0.212989, Training Accuracy= 0.91406\n",
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"Iter 52480, Minibatch Loss= 0.151774, Training Accuracy= 0.95312\n",
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"Iter 53760, Minibatch Loss= 0.090070, Training Accuracy= 0.96875\n",
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"Iter 55040, Minibatch Loss= 0.264714, Training Accuracy= 0.92969\n",
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"Iter 56320, Minibatch Loss= 0.235086, Training Accuracy= 0.92969\n",
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"Iter 57600, Minibatch Loss= 0.160302, Training Accuracy= 0.95312\n",
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"Iter 58880, Minibatch Loss= 0.106515, Training Accuracy= 0.96875\n",
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"Iter 60160, Minibatch Loss= 0.236039, Training Accuracy= 0.94531\n",
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"Iter 61440, Minibatch Loss= 0.279540, Training Accuracy= 0.90625\n",
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"Iter 62720, Minibatch Loss= 0.173585, Training Accuracy= 0.93750\n",
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"Iter 64000, Minibatch Loss= 0.191009, Training Accuracy= 0.92188\n",
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"Iter 65280, Minibatch Loss= 0.210331, Training Accuracy= 0.89844\n",
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"Iter 66560, Minibatch Loss= 0.223444, Training Accuracy= 0.94531\n",
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"Iter 67840, Minibatch Loss= 0.278210, Training Accuracy= 0.91406\n",
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"Iter 69120, Minibatch Loss= 0.174290, Training Accuracy= 0.95312\n",
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"Iter 70400, Minibatch Loss= 0.188701, Training Accuracy= 0.94531\n",
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"Iter 71680, Minibatch Loss= 0.210277, Training Accuracy= 0.94531\n",
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"Iter 72960, Minibatch Loss= 0.249951, Training Accuracy= 0.95312\n",
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"Iter 74240, Minibatch Loss= 0.209853, Training Accuracy= 0.92188\n",
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"Iter 75520, Minibatch Loss= 0.049742, Training Accuracy= 0.99219\n",
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"Iter 76800, Minibatch Loss= 0.250095, Training Accuracy= 0.92969\n",
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"Iter 78080, Minibatch Loss= 0.133853, Training Accuracy= 0.95312\n",
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"Iter 79360, Minibatch Loss= 0.110206, Training Accuracy= 0.97656\n",
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"Iter 80640, Minibatch Loss= 0.141906, Training Accuracy= 0.93750\n",
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"Iter 81920, Minibatch Loss= 0.126872, Training Accuracy= 0.94531\n",
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"Iter 83200, Minibatch Loss= 0.138925, Training Accuracy= 0.95312\n",
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"Iter 84480, Minibatch Loss= 0.128652, Training Accuracy= 0.96094\n",
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"Iter 85760, Minibatch Loss= 0.099837, Training Accuracy= 0.96094\n",
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"Iter 87040, Minibatch Loss= 0.119000, Training Accuracy= 0.95312\n",
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"Iter 88320, Minibatch Loss= 0.179807, Training Accuracy= 0.95312\n",
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"Iter 89600, Minibatch Loss= 0.141792, Training Accuracy= 0.96094\n",
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"Iter 90880, Minibatch Loss= 0.142424, Training Accuracy= 0.96094\n",
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"Iter 92160, Minibatch Loss= 0.159564, Training Accuracy= 0.96094\n",
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"Iter 93440, Minibatch Loss= 0.111984, Training Accuracy= 0.95312\n",
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"Iter 94720, Minibatch Loss= 0.238978, Training Accuracy= 0.92969\n",
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"Iter 96000, Minibatch Loss= 0.068002, Training Accuracy= 0.97656\n",
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"Iter 97280, Minibatch Loss= 0.191819, Training Accuracy= 0.94531\n",
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"Iter 98560, Minibatch Loss= 0.081197, Training Accuracy= 0.99219\n",
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"Iter 99840, Minibatch Loss= 0.206797, Training Accuracy= 0.95312\n",
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"Optimization Finished!\n",
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"Testing Accuracy: 0.941406\n"
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]
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}
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],
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"source": [
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"# Initializing the variables\n",
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"init = tf.initialize_all_variables()\n",
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"\n",
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"# Launch the graph\n",
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"with tf.Session() as sess:\n",
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" sess.run(init)\n",
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" step = 1\n",
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" # Keep training until reach max iterations\n",
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" while step * batch_size < training_iters:\n",
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" batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
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" # Reshape data to get 28 seq of 28 elements\n",
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" batch_xs = batch_xs.reshape((batch_size, n_steps, n_input))\n",
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" # Fit training using batch data\n",
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" sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys,\n",
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" istate: np.zeros((batch_size, 2*n_hidden))})\n",
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" if step % display_step == 0:\n",
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" # Calculate batch accuracy\n",
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" acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys,\n",
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" istate: np.zeros((batch_size, 2*n_hidden))})\n",
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" # Calculate batch loss\n",
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" loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys,\n",
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" istate: np.zeros((batch_size, 2*n_hidden))})\n",
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" print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \"{:.6f}\".format(loss) + \\\n",
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" \", Training Accuracy= \" + \"{:.5f}\".format(acc)\n",
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" step += 1\n",
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" print \"Optimization Finished!\"\n",
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" # Calculate accuracy for 256 mnist test images\n",
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" test_len = 256\n",
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" test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n",
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" test_label = mnist.test.labels[:test_len]\n",
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" print \"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: test_data, y: test_label,\n",
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" istate: np.zeros((test_len, 2*n_hidden))})"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.4.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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