def get_random_block_from_data(data, batch_size):
start_index = np.random.randint(0, len(data) - batch_size)
return data[start_index:(start_index + batch_size)]
X_train, X_test = min_max_scale(mnist.train.images, mnist.test.images)
n_samples = int(mnist.train.num_examples)
training_epochs = 20
batch_size = 128
display_step = 1
autoencoder = VariationalAutoencoder(
n_input=784,
n_hidden=200,
optimizer=tf.train.AdamOptimizer(learning_rate=0.001))
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(n_samples / batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs = get_random_block_from_data(X_train, batch_size)
# Fit training using batch data
cost = autoencoder.partial_fit(batch_xs)
# Compute average loss
avg_cost += cost / n_samples * batch_size
# Display logs per epoch step
def get_random_block_from_data(data, batch_size):
start_index = np.random.randint(0, len(data) - batch_size)
return data[start_index:(start_index + batch_size)]
X_train, X_test = min_max_scale(mnist.train.images, mnist.test.images)
n_samples = int(mnist.train.num_examples)
training_epochs = 20
batch_size = 128
display_step = 1
autoencoder = VariationalAutoencoder(
n_input=784,
n_hidden=200,
optimizer=tf.train.AdamOptimizer(learning_rate = 0.001))
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(n_samples / batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs = get_random_block_from_data(X_train, batch_size)
# Fit training using batch data
cost = autoencoder.partial_fit(batch_xs)
# Compute average loss
avg_cost += cost / n_samples * batch_size
# Display logs per epoch step