src/audio2vec_train.py
def create_train(total_loss, global_step,batch_size):
    """ 
    Create an optimizer and apply to all trainable variables.
    Add moving average for all trainable variables.

    Args: 
      total_loss: Total loss from loss().
      global_step: Integer Variable counting the number of training step
        processed.
    Returns:
      train_op: op for training.
    """

    # Variables that affect learning rate.
    num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN/batch_size
    decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)

    # Decay the learning rate exponentially based on the number of steps
    lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,global_step,
                                    decay_steps, LEARNING_RATE_DECAY_FACTOR,
                                    staircase=True)
    tf.scalar_summary('learning_rate', lr)
    # Generate moving averages of all losses and associated summaries

    return
 
    Create an optimizer and apply to all trainable variables.
    Add moving average for all trainable variables.

    Args: 
      total_loss: Total loss from loss().
      global_step: Integer Variable counting the number of training step
        processed.
    Returns:
      train_op: op for training.
    
Create an optimizer and apply to all trainable variables.
Add moving average for all trainable variables.
Create an optimizer and apply to all trainable variables . Add moving average for all trainable variables .
def create_train(total_loss, global_step,batch_size):
    num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN/batch_size
    decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
    lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,global_step,
                                    decay_steps, LEARNING_RATE_DECAY_FACTOR,
                                    staircase=True)
    tf.scalar_summary('learning_rate', lr)
    return
def create_train ( total_loss , global_step , batch_size ) : num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / batch_size decay_steps = int ( num_batches_per_epoch * NUM_EPOCHS_PER_DECAY ) lr = tf . train . exponential_decay ( INITIAL_LEARNING_RATE , global_step , decay_steps , LEARNING_RATE_DECAY_FACTOR , staircase = True ) tf . scalar_summary ( 'learning_rate' , lr ) return
Create an optimizer and apply to all trainable variables.
Create an optimizer and apply to all trainable variables .
""" 
    Create an optimizer and apply to all trainable variables.
    Add moving average for all trainable variables.

    Args: 
      total_loss: Total loss from loss().
      global_step: Integer Variable counting the number of training step
        processed.
    Returns:
      train_op: op for training.
    """ # Variables that affect learning rate. # Decay the learning rate exponentially based on the number of steps # Generate moving averages of all losses and associated summaries
op for training.
op for training .
train_op
total_loss
Total loss from loss().
Total loss from loss () .
global_step
Integer Variable counting the number of training step
processed.
Integer Variable counting the number of training step processed .
batch_size