A quick example on how to run in-training validation in batches

test_in_batches.py

'''
A Dynamic Recurrent Neural Network (LSTM) implementation example using
TensorFlow library. This example is using a toy dataset to classify linear
sequences. The generated sequences have variable length.

Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf

Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

import tensorflow as tf
import random


# ====================
#  TOY DATA GENERATOR
# ====================
class ToySequenceData(object):
    """ Generate sequence of data with dynamic length.
    This class generate samples for training:
    - Class 0: linear sequences (i.e. [0, 1, 2, 3,...])
    - Class 1: random sequences (i.e. [1, 3, 10, 7,...])

    NOTICE:
    We have to pad each sequence to reach 'max_seq_len' for TensorFlow
    consistency (we cannot feed a numpy array with inconsistent
    dimensions). The dynamic calculation will then be perform thanks to
    'seqlen' attribute that records every actual sequence length.
    """
    def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,
                 max_value=1000):
        self.data = []
        self.labels = []
        self.seqlen = []
        for i in range(n_samples):
            # Random sequence length
            len = random.randint(min_seq_len, max_seq_len)
            # Monitor sequence length for TensorFlow dynamic calculation
            self.seqlen.append(len)
            # Add a random or linear int sequence (50% prob)
            if random.random() < .5:
                # Generate a linear sequence
                rand_start = random.randint(0, max_value - len)
                s = [[float(i)/max_value] for i in
                     range(rand_start, rand_start + len)]
                # Pad sequence for dimension consistency
                s += [[0.] for i in range(max_seq_len - len)]
                self.data.append(s)
                self.labels.append([1., 0.])
            else:
                # Generate a random sequence
                s = [[float(random.randint(0, max_value))/max_value]
                     for i in range(len)]
                # Pad sequence for dimension consistency
                s += [[0.] for i in range(max_seq_len - len)]
                self.data.append(s)
                self.labels.append([0., 1.])
        self.batch_id = 0

    def next(self, batch_size):
        """ Return a batch of data. When dataset end is reached, start over.
        """
        if self.batch_id == len(self.data):
            self.batch_id = 0
        batch_data = (self.data[self.batch_id:min(self.batch_id +
                                                  batch_size, len(self.data))])
        batch_labels = (self.labels[self.batch_id:min(self.batch_id +
                                                  batch_size, len(self.data))])
        batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id +
                                                  batch_size, len(self.data))])
        self.batch_id = min(self.batch_id + batch_size, len(self.data))
        return batch_data, batch_labels, batch_seqlen


# ==========
#   MODEL
# ==========

# Parameters
learning_rate = 0.01
training_iters = 1000000
batch_size = 128
val_batch_size = 128
val_set_size = 5000
val_iters = 1
display_step = 10

# Network Parameters
seq_max_len = 20 # Sequence max length
n_hidden = 64 # hidden layer num of features
n_classes = 2 # linear sequence or not

trainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len)
testset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len)

# tf Graph input
x = tf.placeholder("float", [None, seq_max_len, 1])
y = tf.placeholder("float", [None, n_classes])
# A placeholder for indicating each sequence length
seqlen = tf.placeholder(tf.int32, [None])

# Define weights
weights = {
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([n_classes]))
}


def dynamicRNN(x, seqlen, weights, biases):

    # Prepare data shape to match `rnn` function requirements
    # Current data input shape: (batch_size, n_steps, n_input)
    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
    
    # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)
    x = tf.unstack(x, seq_max_len, 1)

    # Define a lstm cell with tensorflow
    lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden)

    # Get lstm cell output, providing 'sequence_length' will perform dynamic
    # calculation.
    outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32,
                                sequence_length=seqlen)

    # When performing dynamic calculation, we must retrieve the last
    # dynamically computed output, i.e., if a sequence length is 10, we need
    # to retrieve the 10th output.
    # However TensorFlow doesn't support advanced indexing yet, so we build
    # a custom op that for each sample in batch size, get its length and
    # get the corresponding relevant output.

    # 'outputs' is a list of output at every timestep, we pack them in a Tensor
    # and change back dimension to [batch_size, n_step, n_input]
    outputs = tf.stack(outputs)
    outputs = tf.transpose(outputs, [1, 0, 2])

    # Hack to build the indexing and retrieve the right output.
    batch_size = tf.shape(outputs)[0]
    # Start indices for each sample
    index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1)
    # Indexing
    outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index)

    # Linear activation, using outputs computed above
    return tf.matmul(outputs, weights['out']) + biases['out']

pred = dynamicRNN(x, seqlen, weights, biases)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.global_variables_initializer()

    
test_data = testset.data
test_label = testset.labels
test_seqlen = testset.seqlen
    
# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    step = 1

    # Keep training until reach max iterations
    while step * batch_size < training_iters:
        batch_x, batch_y, batch_seqlen = trainset.next(batch_size)
        # Run optimization op (backprop)
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,
                                       seqlen: batch_seqlen})
        if step % display_step == 0:
            # Calculate batch accuracy
            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y,
                                                seqlen: batch_seqlen})
            # Calculate batch loss
            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y,
                                             seqlen: batch_seqlen})
            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
            
        # Once in every 1000 *train* steps we evaluate the validation set (called test here)
        if step % 1000 == 0:
          val_step = 1
          val_acc = 0
          while val_step * val_batch_size < val_set_size:
            valid_x, valid_y, valid_seqlen = testset.next(val_batch_size)
            step_acc = sess.run(accuracy, feed_dict={x: valid_x, y: valid_y,seqlen: valid_seqlen})
            val_acc+=step_acc
            val_step +=1
          
          val_acc = val_acc / val_step
          print("Validation set accuracy: %s" % val_acc)
          
        step += 1
    print("Optimization Finished!")

    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={x: test_data, y: test_label,
                                      seqlen: test_seqlen}))