def build_graph(mode, hparams_string, input_size, num_classes,
sequence_example_file=None):
"""Builds the TensorFlow graph.
Args:
mode: 'train', 'eval', or 'generate'. Only mode related ops are added to
the graph.
hparams_string: A string literal of a Python dictionary, where keys are
hyperparameter names and values replace default values. For example:
'{"batch_size":64,"rnn_layer_sizes":[128,128]}'
input_size: The size of the input vectors in the inputs batch. Each
inputs batch should have a shape [batch_size, num_steps, input_size].
num_classes: The number of classes the labels can be.
sequence_example_file: A string path to a TFRecord file containing
tf.train.SequenceExamples. Only needed for training and evaluation.
Returns:
A tf.Graph instance which contains the TF ops.
Raises:
ValueError: If mode is not 'train', 'eval', or 'generate', or if
sequence_example_file does not match a file when mode is 'train' or
'eval'.
"""
if mode not in ('train', 'eval', 'generate'):
raise ValueError('The mode parameter must be \'train\', \'eval\', '
'or \'generate\'. The mode parameter was: %s' % mode)
with tf.Graph().as_default() as graph:
hparams = default_hparams()
hparams = hparams.parse(hparams_string)
tf.logging.info('hparams = %s', hparams.values())
inputs, labels, lengths, = None, None, None
state_is_tuple = True
if mode == 'train' or mode == 'eval':
inputs, labels, lengths = sequence_example_lib.get_padded_batch(
[sequence_example_file], hparams.batch_size, input_size)
elif mode == 'generate':
inputs = tf.placeholder(tf.float32, [hparams.batch_size, None,
input_size])
# If state_is_tuple is True, the output RNN cell state will be a tuple
# instead of a tensor. During training and evaluation this improves
# performance. However, during generation, the RNN cell state is fed
# back into the graph with a feed dict. Feed dicts require passed in
# values to be tensors and not tuples, so state_is_tuple is set to False.
state_is_tuple = False
cells = []
for num_units in hparams.rnn_layer_sizes:
cell = tf.nn.rnn_cell.BasicLSTMCell(
num_units, state_is_tuple=state_is_tuple)
cell = tf.nn.rnn_cell.DropoutWrapper(
cell, output_keep_prob=hparams.dropout_keep_prob)
cells.append(cell)
cell = tf.nn.rnn_cell.MultiRNNCell(cells, state_is_tuple=state_is_tuple)
initial_state = cell.zero_state(hparams.batch_size, tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(
cell, inputs, lengths, initial_state, parallel_iterations=1,
swap_memory=True)
outputs_flat = tf.reshape(outputs, [-1, hparams.rnn_layer_sizes[-1]])
logits_flat = tf.contrib.layers.linear(outputs_flat, num_classes)
if mode == 'train' or mode == 'eval':
if hparams.skip_first_n_losses:
logits = tf.reshape(logits_flat, [hparams.batch_size, -1, num_classes])
logits = logits[:, hparams.skip_first_n_losses:, :]
logits_flat = tf.reshape(logits, [-1, num_classes])
labels = labels[:, hparams.skip_first_n_losses:]
labels_flat = tf.reshape(labels, [-1])
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits_flat, labels_flat))
perplexity = tf.exp(loss)
correct_predictions = tf.nn.in_top_k(logits_flat, labels_flat, 1)
accuracy = tf.reduce_mean(tf.to_float(correct_predictions)) * 100
global_step = tf.Variable(0, trainable=False, name='global_step')
tf.add_to_collection('loss', loss)
tf.add_to_collection('perplexity', perplexity)
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('global_step', global_step)
if mode == 'train':
learning_rate = tf.train.exponential_decay(
hparams.initial_learning_rate, global_step, hparams.decay_steps,
hparams.decay_rate, staircase=True, name='learning_rate')
opt = tf.train.AdamOptimizer(learning_rate)
params = tf.trainable_variables()
gradients = tf.gradients(loss, params)
clipped_gradients, _ = tf.clip_by_global_norm(gradients,
hparams.clip_norm)
train_op = opt.apply_gradients(zip(clipped_gradients, params),
global_step)
tf.add_to_collection('learning_rate', learning_rate)
tf.add_to_collection('train_op', train_op)
tf.scalar_summary('loss', loss)
tf.scalar_summary('perplexity', perplexity)
tf.scalar_summary('accuracy', accuracy)
tf.scalar_summary('learning_rate', learning_rate)
if mode == 'eval':
summary_op = tf.merge_summary([
tf.scalar_summary('loss', loss),
tf.scalar_summary('perplexity', perplexity),
tf.scalar_summary('accuracy', accuracy)])
tf.add_to_collection('summary_op', summary_op)
elif mode == 'generate':
if hparams.temperature != 1.0:
logits_flat /= hparams.temperature
softmax_flat = tf.nn.softmax(logits_flat)
softmax = tf.reshape(softmax_flat, [hparams.batch_size, -1, num_classes])
tf.add_to_collection('inputs', inputs)
tf.add_to_collection('initial_state', initial_state)
tf.add_to_collection('final_state', final_state)
tf.add_to_collection('softmax', softmax)
return graph
def build_graph(mode, hparams_string, input_size, num_classes,
sequence_example_file=None):
"""Builds the TensorFlow graph.
Args:
mode: 'train', 'eval', or 'generate'. Only mode related ops are added to
the graph.
hparams_string: A string literal of a Python dictionary, where keys are
hyperparameter names and values replace default values. For example:
'{"batch_size":64,"rnn_layer_sizes":[128,128]}'
input_size: The size of the input vectors in the inputs batch. Each
inputs batch should have a shape [batch_size, num_steps, input_size].
num_classes: The number of classes the labels can be.
sequence_example_file: A string path to a TFRecord file containing
tf.train.SequenceExamples. Only needed for training and evaluation.
Returns:
A tf.Graph instance which contains the TF ops.
Raises:
ValueError: If mode is not 'train', 'eval', or 'generate', or if
sequence_example_file does not match a file when mode is 'train' or
'eval'.
"""
if mode not in ('train', 'eval', 'generate'):
raise ValueError('The mode parameter must be \'train\', \'eval\', '
'or \'generate\'. The mode parameter was: %s' % mode)
with tf.Graph().as_default() as graph:
hparams = default_hparams()
hparams = hparams.parse(hparams_string)
tf.logging.info('hparams = %s', hparams.values())
inputs, labels, lengths, = None, None, None
state_is_tuple = True
if mode == 'train' or mode == 'eval':
inputs, labels, lengths = sequence_example_lib.get_padded_batch(
[sequence_example_file], hparams.batch_size, input_size)
elif mode == 'generate':
inputs = tf.placeholder(tf.float32, [hparams.batch_size, None,
input_size])
# If state_is_tuple is True, the output RNN cell state will be a tuple
# instead of a tensor. During training and evaluation this improves
# performance. However, during generation, the RNN cell state is fed
# back into the graph with a feed dict. Feed dicts require passed in
# values to be tensors and not tuples, so state_is_tuple is set to False.
state_is_tuple = False
cells = []
for num_units in hparams.rnn_layer_sizes:
cell = tf.nn.rnn_cell.BasicLSTMCell(
num_units, state_is_tuple=state_is_tuple)
cell = tf.nn.rnn_cell.DropoutWrapper(
cell, output_keep_prob=hparams.dropout_keep_prob)
cells.append(cell)
cell = tf.nn.rnn_cell.MultiRNNCell(cells, state_is_tuple=state_is_tuple)
initial_state = cell.zero_state(hparams.batch_size, tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(
cell, inputs, lengths, initial_state, parallel_iterations=1,
swap_memory=True)
outputs_flat = tf.reshape(outputs, [-1, hparams.rnn_layer_sizes[-1]])
logits_flat = tf.contrib.layers.linear(outputs_flat, num_classes)
if mode == 'train' or mode == 'eval':
if hparams.skip_first_n_losses:
logits = tf.reshape(logits_flat, [hparams.batch_size, -1, num_classes])
logits = logits[:, hparams.skip_first_n_losses:, :]
logits_flat = tf.reshape(logits, [-1, num_classes])
labels = labels[:, hparams.skip_first_n_losses:]
labels_flat = tf.reshape(labels, [-1])
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits_flat, labels_flat))
perplexity = tf.exp(loss)
correct_predictions = tf.nn.in_top_k(logits_flat, labels_flat, 1)
accuracy = tf.reduce_mean(tf.to_float(correct_predictions)) * 100
global_step = tf.Variable(0, trainable=False, name='global_step')
tf.add_to_collection('loss', loss)
tf.add_to_collection('perplexity', perplexity)
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('global_step', global_step)
if mode == 'train':
learning_rate = tf.train.exponential_decay(
hparams.initial_learning_rate, global_step, hparams.decay_steps,
hparams.decay_rate, staircase=True, name='learning_rate')
opt = tf.train.AdamOptimizer(learning_rate)
params = tf.trainable_variables()
gradients = tf.gradients(loss, params)
clipped_gradients, _ = tf.clip_by_global_norm(gradients,
hparams.clip_norm)
train_op = opt.apply_gradients(zip(clipped_gradients, params),
global_step)
tf.add_to_collection('learning_rate', learning_rate)
tf.add_to_collection('train_op', train_op)
tf.scalar_summary('loss', loss)
tf.scalar_summary('perplexity', perplexity)
tf.scalar_summary('accuracy', accuracy)
tf.scalar_summary('learning_rate', learning_rate)
if mode == 'eval':
summary_op = tf.merge_summary([
tf.scalar_summary('loss', loss),
tf.scalar_summary('perplexity', perplexity),
tf.scalar_summary('accuracy', accuracy)])
tf.add_to_collection('summary_op', summary_op)
elif mode == 'generate':
if hparams.temperature != 1.0:
logits_flat /= hparams.temperature
softmax_flat = tf.nn.softmax(logits_flat)
softmax = tf.reshape(softmax_flat, [hparams.batch_size, -1, num_classes])
tf.add_to_collection('inputs', inputs)
tf.add_to_collection('initial_state', initial_state)
tf.add_to_collection('final_state', final_state)
tf.add_to_collection('softmax', softmax)
return graph