z_mean, z_log_var = args

batch = K.shape(z_mean)[0]

dim = K.int_shape(z_mean)[1]

eps = K.random_normal(shape=(batch, dim))

# ----The generator for the train set----#

while True:

# Create empty arrays to contain batch of features and labels #

batch_data = np.zeros((batch_size, dataset.shape[1], dataset.shape[2], 1))

batch_target = np.zeros((batch_size, dataset.shape[1], dataset.shape[2], 1))

for i in range(batch_size):

# choose random index in features

index = random.randrange(input_data.shape[0])

batch_data[i] = input_data[index]

batch_target[i] = target[index]

def __init__(self, filepath, base_model, monitor='val_loss', verbose=0,

save_best_only=False, save_weights_only=False,

mode='auto', period=1):

ModelCheckpoint.__init__(self, filepath, monitor=monitor, verbose=verbose, save_best_only=save_best_only, save_weights_only=save_weights_only, mode=mode, period=period)

self.base_model = base_model

def on_epoch_end(self, epoch, logs=None):

logs = logs or {}

self.epochs_since_last_save += 1

if self.epochs_since_last_save >= self.period:

self.epochs_since_last_save = 0

filepath = self.filepath.format(epoch=epoch + 1, **logs)

if self.save_best_only:

current = logs.get(self.monitor)

if self.monitor_op(current, self.best):

if self.verbose > 0:

print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'

' saving model to %s'

% (epoch + 1, self.monitor, self.best,

current, filepath))

self.best = current

if self.save_weights_only:

self.base_model.save_weights(filepath, overwrite=True)

else:

self.base_model.save(filepath, overwrite=True)

else:

if self.verbose > 0:

print('\nEpoch %05d: %s did not improve from %0.5f' %

(epoch + 1, self.monitor, self.best))

else:

if self.verbose > 0:

print('\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))

if self.save_weights_only:

self.base_model.save_weights(filepath, overwrite=True)

else:

if epoch_index == 100 or epoch_index == 200:

return 0.9*lr