RNN w/ LSTM cell example in TensorFlow and Python





Welcome to part eleven of the Deep Learning with Neural Networks and TensorFlow tutorials. In this tutorial, we're going to cover how to code a Recurrent Neural Network model with an LSTM in TensorFlow.

To begin, we're going to start with the exact same code as we used with the basic multilayer-perceptron model:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 10
batch_size = 100

x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')

def neural_network_model(data):
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([784, n_nodes_hl1])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                    'biases':tf.Variable(tf.random_normal([n_classes])),}


    l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']), hidden_1_layer['biases'])
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']), hidden_2_layer['biases'])
    l2 = tf.nn.relu(l2)

    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']), hidden_3_layer['biases'])
    l3 = tf.nn.relu(l3)

    output = tf.matmul(l3,output_layer['weights']) + output_layer['biases']

    return output

def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
    optimizer = tf.train.AdamOptimizer().minimize(cost)
    
    hm_epochs = 10
    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                epoch_loss += c

            print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:',accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))

train_neural_network(x)

From here, we're going to simply modify the model function, along with a couple variables.

To begin:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.ops import rnn, rnn_cell
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

hm_epochs = 3
n_classes = 10
batch_size = 128
chunk_size = 28
n_chunks = 28
rnn_size = 128


x = tf.placeholder('float', [None, n_chunks,chunk_size])
y = tf.placeholder('float')

Here, we're importing TensorFlow, mnist, and the rnn model/cell code from TensorFlow. We're also defining the chunk size, number of chunks, and rnn size as new variables. Also, the shape of the x variable is changed, to include the chunks. In the basic neural network, you are sending in the entire image of pixel data all at once. With the Recurrent Neural Network, we're treating inputs now as sequential inputs of chunks instead.

Now our new model function:

def recurrent_neural_network(x):
    layer = {'weights':tf.Variable(tf.random_normal([rnn_size,n_classes])),
             'biases':tf.Variable(tf.random_normal([n_classes]))}

    x = tf.transpose(x, [1,0,2])
    x = tf.reshape(x, [-1, chunk_size])
    x = tf.split(0, n_chunks, x)

    lstm_cell = rnn_cell.BasicLSTMCell(rnn_size,state_is_tuple=True)
    outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)

    output = tf.matmul(outputs[-1],layer['weights']) + layer['biases']

    return output

We have a weights/biases dictionary like before, but then we get to some modifications to our input data, x. We're doing this is purely to satisfy the structure that TensorFlow wants of us to fit their rnn_cell model. The one confusing thing here is possibly our transpose operation. Any time there's an operation like this with TensorFlow, you can either play with the value in the interactive session, or you can just use Numpy for a quick example. For example, we can use the following Numpy code:

import numpy as np

x = np.ones((1,2,3))

print(x)
print(np.transpose(x,(1,0,2)))

The output:

[[
  [ 1.  1.  1.],
  [ 1.  1.  1.]
]]

[
 [[ 1.  1.  1.]],
 [[ 1.  1.  1.]]
]

There, you can see the change from the first to the 2nd. Now, we just make a few minor changes in the training function:

def train_neural_network(x):
    prediction = recurrent_neural_network(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
    optimizer = tf.train.AdamOptimizer().minimize(cost)
    
    
    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                epoch_x = epoch_x.reshape((batch_size,n_chunks,chunk_size))

                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                epoch_loss += c

            print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:',accuracy.eval({x:mnist.test.images.reshape((-1, n_chunks, chunk_size)), y:mnist.test.labels}))

train_neural_network(x)

The changes here are in epoch_x, where we reshape that so it is no longer just an input of 784 values, it's now organized by chunks of whatever our chunk_size is. The other change we need to make is when we calcualte accuracy, where each example here is reshaped, again, to be the n_chunks by chunk_size, only the first dimension is just -1, rather than the batch_size, since we're just checking the accuracy of a single image, rather than training a whole batch of images.

After this, with a mere 3 epochs:

Epoch 0 completed out of 3 loss: 192.525049236
Epoch 1 completed out of 3 loss: 54.7218228597
Epoch 2 completed out of 3 loss: 36.7738111783
Accuracy: 0.9748

With 10:

Epoch 0 completed out of 10 loss: 183.533833001
Epoch 1 completed out of 10 loss: 53.2128913924
Epoch 2 completed out of 10 loss: 36.641087316
Epoch 3 completed out of 10 loss: 28.2334972355
Epoch 4 completed out of 10 loss: 23.5787885857
Epoch 5 completed out of 10 loss: 20.3254865455
Epoch 6 completed out of 10 loss: 17.0910299073
Epoch 7 completed out of 10 loss: 15.3585778594
Epoch 8 completed out of 10 loss: 12.5780420878
Epoch 9 completed out of 10 loss: 12.060161829
Accuracy: 0.9827

As we can see, even on image data, a Recurrent Neural Network with an LSTM cell has a lot of potential. In the next tutorial, we're going to jump into the basics of the Convolutional Neural Network.

The next tutorial:






  • Practical Machine Learning Tutorial with Python Introduction
  • Regression - Intro and Data
  • Regression - Features and Labels
  • Regression - Training and Testing
  • Regression - Forecasting and Predicting
  • Pickling and Scaling
  • Regression - Theory and how it works
  • Regression - How to program the Best Fit Slope
  • Regression - How to program the Best Fit Line
  • Regression - R Squared and Coefficient of Determination Theory
  • Regression - How to Program R Squared
  • Creating Sample Data for Testing
  • Classification Intro with K Nearest Neighbors
  • Applying K Nearest Neighbors to Data
  • Euclidean Distance theory
  • Creating a K Nearest Neighbors Classifer from scratch
  • Creating a K Nearest Neighbors Classifer from scratch part 2
  • Testing our K Nearest Neighbors classifier
  • Final thoughts on K Nearest Neighbors
  • Support Vector Machine introduction
  • Vector Basics
  • Support Vector Assertions
  • Support Vector Machine Fundamentals
  • Constraint Optimization with Support Vector Machine
  • Beginning SVM from Scratch in Python
  • Support Vector Machine Optimization in Python
  • Support Vector Machine Optimization in Python part 2
  • Visualization and Predicting with our Custom SVM
  • Kernels Introduction
  • Why Kernels
  • Soft Margin Support Vector Machine
  • Kernels, Soft Margin SVM, and Quadratic Programming with Python and CVXOPT
  • Support Vector Machine Parameters
  • Machine Learning - Clustering Introduction
  • Handling Non-Numerical Data for Machine Learning
  • K-Means with Titanic Dataset
  • K-Means from Scratch in Python
  • Finishing K-Means from Scratch in Python
  • Hierarchical Clustering with Mean Shift Introduction
  • Mean Shift applied to Titanic Dataset
  • Mean Shift algorithm from scratch in Python
  • Dynamically Weighted Bandwidth for Mean Shift
  • Introduction to Neural Networks
  • Installing TensorFlow for Deep Learning - OPTIONAL
  • Introduction to Deep Learning with TensorFlow
  • Deep Learning with TensorFlow - Creating the Neural Network Model
  • Deep Learning with TensorFlow - How the Network will run
  • Deep Learning with our own Data
  • Simple Preprocessing Language Data for Deep Learning
  • Training and Testing on our Data for Deep Learning
  • 10K samples compared to 1.6 million samples with Deep Learning
  • How to use CUDA and the GPU Version of Tensorflow for Deep Learning
  • Recurrent Neural Network (RNN) basics and the Long Short Term Memory (LSTM) cell
  • RNN w/ LSTM cell example in TensorFlow and Python
  • Convolutional Neural Network (CNN) basics
  • Convolutional Neural Network CNN with TensorFlow tutorial
  • TFLearn - High Level Abstraction Layer for TensorFlow Tutorial
  • Using a 3D Convolutional Neural Network on medical imaging data (CT Scans) for Kaggle
  • Classifying Cats vs Dogs with a Convolutional Neural Network on Kaggle
  • Using a neural network to solve OpenAI's CartPole balancing environment