def extract_all_reconstructed_spectrograms(
spectrograms_directory, checkpoint_directory=checkpoint_directory):
extract_reconstructed_spectrograms(
load_dataset.train_input_fn(BATCH_SIZE),
spectrograms_directory + "/train",
checkpoint_directory=checkpoint_directory)
extract_reconstructed_spectrograms(
load_dataset.train_input_fn(BATCH_SIZE),
spectrograms_directory + "/test",
checkpoint_directory=checkpoint_directory)
extract_reconstructed_spectrograms(
load_dataset.train_input_fn(BATCH_SIZE),
spectrograms_directory + "/devel",
checkpoint_directory=checkpoint_directory)
def extract_all_features(features_directory,
features_name,
checkpoint_directory=checkpoint_directory):
extract_features(load_dataset.train_input_fn(BATCH_SIZE),
features_directory + '/' + features_name + ".train.csv",
checkpoint_directory=checkpoint_directory)
extract_features(load_dataset.test_input_fn(BATCH_SIZE),
features_directory + '/' + features_name + ".test.csv",
checkpoint_directory=checkpoint_directory)
extract_features(load_dataset.val_input_fn(BATCH_SIZE),
features_directory + '/' + features_name + ".devel.csv",
checkpoint_directory=checkpoint_directory)
def train_network(epochs, batch_size, learning_rate, num_units,
checkpoint_directory):
global EPOCHS
global BATCH_SIZE
global LEARNING_RATE
global NUM_UNITS
global optimizer
EPOCHS = epochs
BATCH_SIZE = batch_size
LEARNING_RATE = learning_rate
NUM_UNITS = num_units
global_step = tf.train.create_global_step()
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
encoder=encoder,
decoder=decoder,
global_step=global_step)
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
with writer.as_default():
tf.contrib.summary.initialize()
begin_time = time.time()
for epoch in range(1, EPOCHS + 1):
dataset = load_dataset.train_input_fn(BATCH_SIZE)
epoch_time = now_time = time.time()
total_loss = 0
num_step = 0
for (batch, (input, filenames, labels)) in enumerate(dataset):
if input['image'].shape[0] < BATCH_SIZE:
break
encoder_hidden = encoder.initialize_hidden_state()
input['image'] = (input['image'] / 255 * 2) - 1
batch_loss = train_step_network(input['image'], encoder_hidden)
total_loss += batch_loss
num_step = batch + 1
global_step.assign_add(1)
with tf.contrib.summary.record_summaries_every_n_global_steps(
1):
tf.contrib.summary.scalar('loss_attention', batch_loss)
now_time = time.time()
print('Epoch {} Step {} Loss {:.4f} Elapsed time {}'.format(
epoch, num_step, batch_loss.numpy(),
time.strftime("%H:%M:%S",
time.gmtime(now_time - begin_time))))
print('Epoch {} Loss {:.4f} Duration {}'.format(
epoch, total_loss / num_step,
time.strftime("%H:%M:%S", time.gmtime(now_time - epoch_time))))
if epoch % 10 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
tf.enable_eager_execution()
EPOCHS = 50
BATCH_SIZE = 64
LEARNING_RATE = 0.001
NUM_UNITS = 256
logdir = "./tensorboard_logs/attention_bi_bi_hidden"
writer = tf.contrib.summary.create_file_writer(logdir)
with writer.as_default():
tf.contrib.summary.always_record_summaries()
optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
checkpoint_directory = './training_checkpoints/attention_bi_bi_hidden'
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
dataset = load_dataset.train_input_fn(BATCH_SIZE)
class Encoder(tf.keras.Model):
def __init__(self, num_units, batch_size):
super(Encoder, self).__init__()
self.batch_size = batch_size
self.num_units = num_units
self.gru_1_f = tf.keras.layers.GRU(
self.num_units,
return_sequences=True,
recurrent_initializer='glorot_uniform',
return_state=True)
self.gru_1_b = tf.keras.layers.GRU(
self.num_units,
return_sequences=True,