def make_graph(hparams_string='{}'):
"""Construct the model and return the graph.
Hyperparameters are given in the hparams flag as a string
representation of a Python dictionary.
For example: '{"batch_size":64,"rnn_layer_sizes":[100,100]}'
Args:
hparams_string: A string literal of a Python dictionary. Keys are
hyperparameter names, and values replace default values.
Returns:
tf.Graph instance which contains the TF ops.
"""
with tf.Graph().as_default() as graph:
with tf.device(lambda op: ""):
hparams = basic_rnn_ops.default_hparams()
hparams = hparams.parse(hparams_string)
logging.info('hparams = %s', hparams.values())
with tf.variable_scope('rnn_model'):
# Define the type of RNN cell to use.
cell = basic_rnn_ops.make_cell(hparams)
# Construct dynamic_rnn inference.
# Make a batch.
melody_sequence = tf.placeholder(
tf.float32,
[hparams.batch_size, None, hparams.one_hot_length])
lengths = tf.placeholder(tf.int32, [hparams.batch_size])
# Make inference graph. That is, inputs to logits.
(logits, initial_state,
final_state) = basic_rnn_ops.dynamic_rnn_inference(
melody_sequence,
lengths,
cell,
hparams,
zero_initial_state=False,
parallel_iterations=1,
swap_memory=True)
softmax = tf.nn.softmax(
tf.reshape(logits, [hparams.batch_size, -1]))
tf.add_to_collection('logits', logits)
tf.add_to_collection('softmax', softmax)
tf.add_to_collection('initial_state', initial_state)
tf.add_to_collection('final_state', final_state)
tf.add_to_collection('melody_sequence', melody_sequence)
tf.add_to_collection('lengths', lengths)
return graph
def make_graph(hparams_string='{}'):
"""Construct the model and return the graph.
Hyperparameters are given in the hparams flag as a string
representation of a Python dictionary.
For example: '{"batch_size":64,"rnn_layer_sizes":[100,100]}'
Args:
hparams_string: A string literal of a Python dictionary. Keys are
hyperparameter names, and values replace default values.
Returns:
tf.Graph instance which contains the TF ops.
"""
with tf.Graph().as_default() as graph:
with tf.device(lambda op: ""):
hparams = basic_rnn_ops.default_hparams()
hparams = hparams.parse(hparams_string)
logging.info('hparams = %s', hparams.values())
with tf.variable_scope('rnn_model'):
# Define the type of RNN cell to use.
cell = basic_rnn_ops.make_cell(hparams)
# Construct dynamic_rnn inference.
# Make a batch.
melody_sequence = tf.placeholder(tf.float32,
[hparams.batch_size, None,
hparams.one_hot_length])
lengths = tf.placeholder(tf.int32, [hparams.batch_size])
# Make inference graph. That is, inputs to logits.
(logits,
initial_state,
final_state) = basic_rnn_ops.dynamic_rnn_inference(
melody_sequence, lengths, cell, hparams,
zero_initial_state=False, parallel_iterations=1,
swap_memory=True)
softmax = tf.nn.softmax(tf.reshape(logits, [hparams.batch_size, -1]))
tf.add_to_collection('logits', logits)
tf.add_to_collection('softmax', softmax)
tf.add_to_collection('initial_state', initial_state)
tf.add_to_collection('final_state', final_state)
tf.add_to_collection('melody_sequence', melody_sequence)
tf.add_to_collection('lengths', lengths)
return graph
def make_graph(sequence_example_file='', hparams_string='{}', is_eval_mode=False):
"""Construct the model and return the graph.
Constructs the TensorFlow graph. Hyperparameters
are given in the hparams flag as a string representation of a Python
dictionary.
For example: '{"batch_size":64,"rnn_layer_sizes":[100,100]}'
Args:
sequence_example_file: String path to tfrecord file containing training
samples.
hparams_string: String literal of a Python dictionary, where keys are
hyperparameter names and values replace default values.
is_eval_mode: If True, training related ops are not build.
Returns:
tf.Graph instance which contains the TF ops.
Raises:
ValueError: If sequence_example_file does not match any files.
"""
file_list = [sequence_example_file]
logging.info('Dataset files: %s', file_list)
with tf.Graph().as_default() as graph:
hparams = basic_rnn_ops.default_hparams()
hparams = hparams.parse(hparams_string)
logging.info('hparams = %s', hparams.values())
with tf.variable_scope('rnn_model'):
# Define the type of RNN cell to use.
cell = basic_rnn_ops.make_cell(hparams)
# There are two ways to construct a variable length RNN in TensorFlow:
# dynamic_rnn, and state_saving_rnn. The code below demonstrates how to
# construct an end-to-end samples on disk to labels and logits pipeline
# for dynamic_rnn.
# Construct dynamic_rnn reader and inference.
# Get a batch queue.
(melody_sequence,
melody_labels,
lengths) = basic_rnn_ops.dynamic_rnn_batch(file_list, hparams)
# Make inference graph. That is, inputs to logits.
# Note: long sequences need a lot of memory on GPU because all forward
# pass activations are needed to compute backprop. Additionally
# multiple steps are computed simultaneously (the parts of each step
# which don't depend on other steps). The `parallel_iterations`
# and `swap_memory` arguments given here trade lower GPU memory
# footprint for speed decrease.
logits, _, _ = basic_rnn_ops.dynamic_rnn_inference(
melody_sequence, lengths, cell, hparams, zero_initial_state=True,
parallel_iterations=1, swap_memory=True)
# The first hparams.skip_first_n_losses steps of the logits tensor is
# removed. Those first steps are given to the model as a primer during
# generation. The model does not get penalized for incorrect
# predictions in those first steps so the loss does not include those
# logits.
truncated_logits = logits[:, hparams.skip_first_n_losses:, :]
# Reshape logits from [batch_size, sequence_length, one_hot_length] to
# [batch_size * sequence_length, one_hot_length].
flat_logits = tf.reshape(truncated_logits,
[-1, hparams.one_hot_length])
# Reshape labels from [batch_size, num_unroll] to
# [batch_size * sequence_length]. Also truncate first steps to match
# truncated_logits.
flat_labels = tf.reshape(
melody_labels[:, hparams.skip_first_n_losses:], [-1])
# Compute loss and gradients for training, and accuracy for evaluation.
cross_entropy, log_perplexity = basic_rnn_ops.log_perplexity_loss(
flat_logits, flat_labels)
accuracy = basic_rnn_ops.eval_accuracy(flat_logits, flat_labels)
global_step = tf.Variable(0, name='global_step', trainable=False)
tf.add_to_collection('logits', logits)
tf.add_to_collection('cross_entropy', cross_entropy)
tf.add_to_collection('log_perplexity', log_perplexity)
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('global_step', global_step)
# Compute weight updates, and updates to learning rate and global step.
if not is_eval_mode:
training_op, learning_rate = basic_rnn_ops.train_op(
cross_entropy, global_step, hparams)
tf.add_to_collection('training_op', training_op)
tf.add_to_collection('learning_rate', learning_rate)
return graph
def make_graph(sequence_example_file='',
hparams_string='{}',
is_eval_mode=False):
"""Construct the model and return the graph.
Constructs the TensorFlow graph. Hyperparameters
are given in the hparams flag as a string representation of a Python
dictionary.
For example: '{"batch_size":64,"rnn_layer_sizes":[100,100]}'
Args:
sequence_example_file: String path to tfrecord file containing training
samples.
hparams_string: String literal of a Python dictionary, where keys are
hyperparameter names and values replace default values.
is_eval_mode: If True, training related ops are not build.
Returns:
tf.Graph instance which contains the TF ops.
Raises:
ValueError: If sequence_example_file does not match any files.
"""
file_list = [sequence_example_file]
logging.info('Dataset files: %s', file_list)
with tf.Graph().as_default() as graph:
hparams = basic_rnn_ops.default_hparams()
hparams = hparams.parse(hparams_string)
logging.info('hparams = %s', hparams.values())
with tf.variable_scope('rnn_model'):
# Define the type of RNN cell to use.
cell = basic_rnn_ops.make_cell(hparams)
# There are two ways to construct a variable length RNN in TensorFlow:
# dynamic_rnn, and state_saving_rnn. The code below demonstrates how to
# construct an end-to-end samples on disk to labels and logits pipeline
# for dynamic_rnn.
# Construct dynamic_rnn reader and inference.
# Get a batch queue.
(melody_sequence, melody_labels,
lengths) = basic_rnn_ops.dynamic_rnn_batch(file_list, hparams)
# Make inference graph. That is, inputs to logits.
# Note: long sequences need a lot of memory on GPU because all forward
# pass activations are needed to compute backprop. Additionally
# multiple steps are computed simultaneously (the parts of each step
# which don't depend on other steps). The `parallel_iterations`
# and `swap_memory` arguments given here trade lower GPU memory
# footprint for speed decrease.
logits, _, _ = basic_rnn_ops.dynamic_rnn_inference(
melody_sequence,
lengths,
cell,
hparams,
zero_initial_state=True,
parallel_iterations=1,
swap_memory=True)
# The first hparams.skip_first_n_losses steps of the logits tensor is
# removed. Those first steps are given to the model as a primer during
# generation. The model does not get penalized for incorrect
# predictions in those first steps so the loss does not include those
# logits.
truncated_logits = logits[:, hparams.skip_first_n_losses:, :]
# Reshape logits from [batch_size, sequence_length, one_hot_length] to
# [batch_size * sequence_length, one_hot_length].
flat_logits = tf.reshape(truncated_logits,
[-1, hparams.one_hot_length])
# Reshape labels from [batch_size, num_unroll] to
# [batch_size * sequence_length]. Also truncate first steps to match
# truncated_logits.
flat_labels = tf.reshape(
melody_labels[:, hparams.skip_first_n_losses:], [-1])
# Compute loss and gradients for training, and accuracy for evaluation.
cross_entropy, log_perplexity = basic_rnn_ops.log_perplexity_loss(
flat_logits, flat_labels)
accuracy = basic_rnn_ops.eval_accuracy(flat_logits, flat_labels)
global_step = tf.Variable(0, name='global_step', trainable=False)
tf.add_to_collection('logits', logits)
tf.add_to_collection('cross_entropy', cross_entropy)
tf.add_to_collection('log_perplexity', log_perplexity)
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('global_step', global_step)
# Compute weight updates, and updates to learning rate and global step.
if not is_eval_mode:
training_op, learning_rate = basic_rnn_ops.train_op(
cross_entropy, global_step, hparams)
tf.add_to_collection('training_op', training_op)
tf.add_to_collection('learning_rate', learning_rate)
return graph