From 3636d9b4816084ad2d2a856f0d5a51a2b345c260 Mon Sep 17 00:00:00 2001 From: Reuben Morais Date: Thu, 1 Aug 2019 14:53:46 +0200 Subject: [PATCH] Use dynamic batch size in train/val graph Avoid needing to use the same batch size for training and validation. --- DeepSpeech.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/DeepSpeech.py b/DeepSpeech.py index 19e16d3b..8a9cc779 100755 --- a/DeepSpeech.py +++ b/DeepSpeech.py @@ -141,7 +141,7 @@ def rnn_impl_static_rnn(x, seq_length, previous_state, reuse): return output, output_state -def create_model(batch_x, batch_size, seq_length, dropout, reuse=False, previous_state=None, overlap=True, rnn_impl=rnn_impl_lstmblockfusedcell): +def create_model(batch_x, seq_length, dropout, reuse=False, batch_size=None, previous_state=None, overlap=True, rnn_impl=rnn_impl_lstmblockfusedcell): layers = {} # Input shape: [batch_size, n_steps, n_input + 2*n_input*n_context] @@ -207,7 +207,7 @@ def create_model(batch_x, batch_size, seq_length, dropout, reuse=False, previous # Conveniently, this loss function is implemented in TensorFlow. # Thus, we can simply make use of this implementation to define our loss. -def calculate_mean_edit_distance_and_loss(iterator, dropout, batch_size, reuse): +def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse): r''' This routine beam search decodes a mini-batch and calculates the loss and mean edit distance. Next to total and average loss it returns the mean edit distance, @@ -222,7 +222,7 @@ def calculate_mean_edit_distance_and_loss(iterator, dropout, batch_size, reuse): rnn_impl = rnn_impl_lstmblockfusedcell # Calculate the logits of the batch - logits, _ = create_model(batch_x, batch_size, batch_seq_len, dropout, reuse=reuse, rnn_impl=rnn_impl) + logits, _ = create_model(batch_x, batch_seq_len, dropout, reuse=reuse, rnn_impl=rnn_impl) # Compute the CTC loss using TensorFlow's `ctc_loss` total_loss = tfv1.nn.ctc_loss(labels=batch_y, inputs=logits, sequence_length=batch_seq_len) @@ -267,7 +267,7 @@ def create_optimizer(): # on which all operations within the tower execute. # For example, all operations of 'tower 0' could execute on the first GPU `tf.device('/gpu:0')`. -def get_tower_results(iterator, optimizer, dropout_rates, batch_size): +def get_tower_results(iterator, optimizer, dropout_rates): r''' With this preliminary step out of the way, we can for each GPU introduce a tower for which's batch we calculate and return the optimization gradients @@ -289,7 +289,7 @@ def get_tower_results(iterator, optimizer, dropout_rates, batch_size): with tf.name_scope('tower_%d' % i): # Calculate the avg_loss and mean_edit_distance and retrieve the decoded # batch along with the original batch's labels (Y) of this tower - avg_loss = calculate_mean_edit_distance_and_loss(iterator, dropout_rates, batch_size, reuse=i > 0) + avg_loss = calculate_mean_edit_distance_and_loss(iterator, dropout_rates, reuse=i > 0) # Allow for variables to be re-used by the next tower tfv1.get_variable_scope().reuse_variables() @@ -436,7 +436,7 @@ def train(): # Building the graph optimizer = create_optimizer() - gradients, loss = get_tower_results(iterator, optimizer, dropout_rates, FLAGS.train_batch_size) + gradients, loss = get_tower_results(iterator, optimizer, dropout_rates) # Average tower gradients across GPUs avg_tower_gradients = average_gradients(gradients)