def main(_=None, weight_init=None, activation_f=tf.nn.sigmoid, data_min=0, data_scale=1.0, epochs=3,learning_rate=None):
tf.reset_default_graph()
input_placeholder = tf.placeholder(tf.float32, [BATCH_SIZE, 2])
output_placeholder = tf.placeholder(tf.float32, [BATCH_SIZE, 28, 28, 1])
# Grab the data as numpy arrays.
train_input, train_output = data_utils.mnist(training=True)
test_input, test_output = data_utils.mnist(training=False)
train_set = ut.mnist_select_n_classes(train_input, train_output, NUM_CLASSES, min=data_min, scale=data_scale)
test_set = ut.mnist_select_n_classes(test_input, test_output, NUM_CLASSES, min=data_min, scale=data_scale)
train_input, train_output = train_set[1], train_set[0]
test_input, test_output = test_set[1], test_set[0]
ut.print_info('train (min, max): (%f, %f)' % (np.min(train_set[0]), np.max(train_set[0])))
visual_inputs, visual_output = train_set[1][0:BATCH_SIZE], train_set[0][0:BATCH_SIZE]
epoch_reconstruction = []
EPOCH_SIZE = len(train_input) // BATCH_SIZE
TEST_SIZE = len(test_input) // BATCH_SIZE
ut.print_info('train: %s' % str(train_input.shape))
ut.print_info('test: %s' % str(test_input.shape))
ut.print_info('output shape: %s' % str(train_output[0].shape))
assert visual_inputs.shape == input_placeholder.get_shape()
assert len(train_input.shape) == len(input_placeholder.get_shape())
assert len(test_input.shape) == len(input_placeholder.get_shape())
assert visual_output.shape == output_placeholder.get_shape()
assert len(train_output.shape) == len(output_placeholder.get_shape())
assert len(test_output.shape) == len(output_placeholder.get_shape())
with pt.defaults_scope(activation_fn=activation_f,
# batch_normalize=True,
# learned_moments_update_rate=0.0003,
# variance_epsilon=0.001,
# scale_after_normalization=True
):
with pt.defaults_scope(phase=pt.Phase.train):
with tf.variable_scope("model") as scope:
output_tensor = decoder(encoder(input_placeholder), weight_init=weight_init)
pretty_loss = loss(output_tensor, output_placeholder)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train = pt.apply_optimizer(optimizer, losses=[pretty_loss])
init = tf.initialize_all_variables()
runner = pt.train.Runner(save_path=FLAGS.save_path)
best_q = 100000
with tf.Session() as sess:
sess.run(init)
for epoch in xrange(epochs):
# Shuffle the training data.
if epoch % np.ceil(epochs / 40.0) == 0 or epoch + 1 == epochs:
reconstruct, loss_value = sess.run([output_tensor, pretty_loss], {input_placeholder: visual_inputs, output_placeholder: visual_output})
epoch_reconstruction.append(reconstruct)
ut.print_info('epoch:%d (min, max): (%f %f)' %(epoch, np.min(reconstruct), np.max(reconstruct)))
train_input, train_output = data_utils.permute_data(
(train_input, train_output))
runner.train_model(
train,
pretty_loss,
EPOCH_SIZE,
feed_vars=(input_placeholder, output_placeholder),
feed_data=pt.train.feed_numpy(BATCH_SIZE, train_input, train_output)
)
accuracy = runner.evaluate_model(
pretty_loss,
TEST_SIZE,
feed_vars=(input_placeholder, output_placeholder),
feed_data=pt.train.feed_numpy(BATCH_SIZE, test_input, test_output))
ut.print_time('Accuracy after %d epoch %g%%' % (
epoch + 1, accuracy * 100))
if best_q > accuracy * 10:
best_q = accuracy * 10
ut.reconstruct_images_epochs(np.asarray(epoch_reconstruction), visual_output,
save_params={'suf':'mn_trivs', 'act':activation_f, 'e':epochs, 'opt':optimizer,
'lr': learning_rate, 'init':weight_init, 'acu': int(best_q)})
def main(_=None, weight_init=tf.random_normal, activation_f=tf.nn.sigmoid, data_min=0, data_scale=1.0, epochs=50,
learning_rate=0.01, prefix=None):
tf.reset_default_graph()
input_placeholder = tf.placeholder(tf.float32, [BATCH_SIZE, 28, 28, 1])
output_placeholder = tf.placeholder(tf.float32, [BATCH_SIZE, 28, 28, 1])
# Grab the data as numpy arrays.
train_input, train_output = data_utils.mnist(training=True)
test_input, test_output = data_utils.mnist(training=False)
train_set = ut.mnist_select_n_classes(train_input, train_output, NUM_CLASSES, min=data_min, scale=data_scale)
test_set = ut.mnist_select_n_classes(test_input, test_output, NUM_CLASSES, min=data_min, scale=data_scale)
train_input, train_output = train_set[0], train_set[0]
test_input, test_output = test_set[0], test_set[0]
ut.print_info('train (min, max): (%f, %f)' % (np.min(train_set[0]), np.max(train_set[0])))
visual_inputs, visual_output = train_set[0][0:BATCH_SIZE], train_set[0][0:BATCH_SIZE]
epoch_reconstruction = []
EPOCH_SIZE = len(train_input) // BATCH_SIZE
TEST_SIZE = len(test_input) // BATCH_SIZE
assert_model(input_placeholder, output_placeholder, test_input, test_output, train_input, train_output, visual_inputs, visual_output)
with pt.defaults_scope(activation_fn=activation_f,
# batch_normalize=True,
# learned_moments_update_rate=0.0003,
# variance_epsilon=0.001,
# scale_after_normalization=True
):
with pt.defaults_scope(phase=pt.Phase.train):
with tf.variable_scope("model") as scope:
output_tensor = decoder(encoder(input_placeholder), weight_init=weight_init)
pretty_loss = loss(output_tensor, output_placeholder)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train = pt.apply_optimizer(optimizer, losses=[pretty_loss])
init = tf.initialize_all_variables()
runner = pt.train.Runner(save_path=FLAGS.save_path)
best_q = 100000
with tf.Session() as sess:
sess.run(init)
for epoch in xrange(epochs):
# Shuffle the training data.
additional_info = ''
if epoch % np.ceil(epochs / 40.0) == 0 or epoch + 1 == epochs:
reconstruct, loss_value = sess.run([output_tensor, pretty_loss], {input_placeholder: visual_inputs, output_placeholder: visual_output})
epoch_reconstruction.append(reconstruct)
additional_info += 'epoch:%d (min, max): (%f %f)' %(epoch, np.min(reconstruct), np.max(reconstruct))
train_input, train_output = data_utils.permute_data(
(train_input, train_output))
runner.train_model(
train,
pretty_loss,
EPOCH_SIZE,
feed_vars=(input_placeholder, output_placeholder),
feed_data=pt.train.feed_numpy(BATCH_SIZE, train_input, train_output),
print_every=None
)
accuracy = runner.evaluate_model(
pretty_loss,
TEST_SIZE,
feed_vars=(input_placeholder, output_placeholder),
feed_data=pt.train.feed_numpy(BATCH_SIZE, test_input, test_output))
ut.print_time('Accuracy after %2d/%d epoch %.2f; %s' % (epoch + 1, epochs, accuracy, additional_info))
if best_q > accuracy:
best_q = accuracy
save_params = {'suf': 'mn_basic', 'act': activation_f, 'e': epochs, 'opt': optimizer, 'lr': learning_rate,
'init': weight_init, 'acu': int(best_q), 'bs': BATCH_SIZE, 'h': HIDDEN_0_SIZE, 'i':prefix}
ut.reconstruct_images_epochs(np.asarray(epoch_reconstruction), visual_output, save_params=save_params)
ut.print_time('Best Quality: %f for %s' % (best_q, ut.to_file_name(save_params)))
ut.reset_start_time()
return best_q