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