def main_unsupervised():
with tf.Graph().as_default() as g:
sess = tf.Session()
num_hidden = FLAGS.num_hidden_layers
ae_hidden_shapes = [getattr(FLAGS, "hidden{0}_units".format(j + 1))
for j in xrange(num_hidden)]
#ae_shape = [FLAGS.image_pixels] + ae_hidden_shapes + [FLAGS.num_classes]
data = read_data_sets_pretraining(FLAGS.data_dir)
num_train = data.train.num_examples # No. of training examples
ae_shape = [data.train.items.shape[1]] + ae_hidden_shapes
ae = AutoEncoder(ae_shape, sess)
learning_rates = {j: getattr(FLAGS,
"pre_layer{0}_learning_rate".format(j + 1))
for j in xrange(num_hidden)}
noise = {j: getattr(FLAGS, "noise_{0}".format(j + 1))
for j in xrange(num_hidden)}
# for i in xrange(len(ae_shape) - 2):
for i in xrange(len(ae_shape) - 1):
n = i + 1
with tf.variable_scope("pretrain_{0}".format(n)):
input_ = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size, ae_shape[0]),
name='ae_input_pl')
target_ = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size, ae_shape[0]),
name='ae_target_pl')
layer = ae.pretrain_net(input_, n)
with tf.name_scope("target"):
target_for_loss = ae.pretrain_net(target_, n, is_target=True) #signal that we want to reconstruct
loss = loss_x_entropy(layer, target_for_loss)
train_op, global_step = training(loss, learning_rates[i], i)
# summarizing training variables
summary_dir = pjoin(FLAGS.summary_dir, 'pretraining_{0}'.format(n))
summary_writer = tf.train.SummaryWriter(summary_dir,
graph_def=sess.graph_def,
flush_secs=FLAGS.flush_secs)
summary_vars = [ae["biases{0}".format(n)], ae["weights{0}".format(n)]]
hist_summarries = [tf.histogram_summary(v.op.name, v)
for v in summary_vars]
hist_summarries.append(loss_summaries[i])
summary_op = tf.merge_summary(hist_summarries)
# training variables initialization
vars_to_init = ae.get_variables_to_init(n)
vars_to_init.append(global_step)
sess.run(tf.initialize_variables(vars_to_init))
print("\n\n")
print("| Training Step | Cross Entropy | Layer | Epoch |")
print("|---------------|---------------|---------|----------|")
minibatches = int(math.ceil(num_train / FLAGS.batch_size))
# for step in xrange(FLAGS.pretraining_epochs * num_train):
for step in xrange(FLAGS.pretraining_epochs * minibatches):
# feed_dict of input (with injected noise) and target
feed_dict = fill_feed_dict_ae(data.train, input_, target_, noise[i])
loss_summary, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict) # unsupervised training and loss
if step % 100 == 0:
# if step % FLAGS.batch_size == 0:
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
# image_summary_op = \
# tf.image_summary("training_images",
# tf.reshape(input_,
# (FLAGS.batch_size,
# FLAGS.image_size,
# FLAGS.image_size, 1)),
# max_images=FLAGS.batch_size)
#
# summary_img_str = sess.run(image_summary_op,
# feed_dict=feed_dict)
# summary_writer.add_summary(summary_img_str)
output = "| {0:>13} | {1:13.4f} | Layer {2} | Epoch {3} |"\
.format(step, loss_value, n, step // num_train + 1)
print(output)
# if i == 0:
# filters = sess.run(tf.identity(ae["weights1"]))
# np.save(pjoin(FLAGS.chkpt_dir, "filters"), filters)
# filters = tile_raster_images(X=filters.T,
# img_shape=(FLAGS.image_size,
# FLAGS.image_size),
# tile_shape=(10, 10),
# output_pixel_vals=False)
# filters = np.expand_dims(np.expand_dims(filters, 0), 3)
# image_var = tf.Variable(filters)
# image_filter = tf.identity(image_var)
# sess.run(tf.initialize_variables([image_var]))
# img_filter_summary_op = tf.image_summary("first_layer_filters",
# image_filter)
# summary_writer.add_summary(sess.run(img_filter_summary_op))
# summary_writer.flush()
return ae
def main_unsupervised():
with tf.Graph().as_default() as g:
sess = tf.Session()
num_hidden = FLAGS.num_hidden_layers
ae_hidden_shapes = [
getattr(FLAGS, "hidden{0}_units".format(j + 1))
for j in range(num_hidden)
]
ae_shape = [FLAGS.image_pixels
] + ae_hidden_shapes + [FLAGS.num_classes]
ae = AutoEncoder(ae_shape, sess)
data = read_data_sets_pretraining(FLAGS.data_dir,
sub['tr'],
sub['te'],
sub['val'],
one_hot=True)
num_train = data.train.num_examples
learning_rates = {
j: getattr(FLAGS, "pre_layer{0}_learning_rate".format(j + 1))
for j in range(num_hidden)
}
noise = {
j: getattr(FLAGS, "noise_{0}".format(j + 1))
for j in range(num_hidden)
}
for i in range(len(ae_shape) - 2):
n = i + 1
with tf.variable_scope("pretrain_{0}".format(n)):
input_ = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size, ae_shape[0]),
name='ae_input_pl')
target_ = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size, ae_shape[0]),
name='ae_target_pl')
layer = ae.pretrain_net(input_, n)
with tf.name_scope("target"):
target_for_loss = ae.pretrain_net(target_,
n,
is_target=True)
loss = loss_x_entropy(layer, target_for_loss)
train_op, global_step = training(loss, learning_rates[i], i)
summary_dir = pjoin(FLAGS.summary_dir,
'pretraining_{0}'.format(n))
summary_writer = tf.summary.FileWriter(
summary_dir, graph=sess.graph, flush_secs=FLAGS.flush_secs)
summary_vars = [
ae["biases{0}".format(n)], ae["weights{0}".format(n)]
]
hist_summarries = [
tf.summary.histogram(v.op.name, v) for v in summary_vars
]
hist_summarries.append(loss_summaries[i])
summary_op = tf.summary.merge(hist_summarries)
vars_to_init = ae.get_variables_to_init(n)
vars_to_init.append(global_step)
sess.run(tf.variables_initializer(vars_to_init))
print("\n\n")
print("| Training Step | Cross Entropy | Layer | Epoch |")
print("|---------------|---------------|---------|----------|")
for step in range(FLAGS.pretraining_epochs * num_train):
feed_dict = fill_feed_dict_ae(data.train, input_, target_,
noise[i])
loss_summary, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict)
if step % 5000 == 0:
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
image_summary_op = \
tf.summary.image("training_images",
tf.reshape(input_,
(FLAGS.batch_size,
FLAGS.image_size,
FLAGS.image_size, 1)),
max_outputs=FLAGS.batch_size)
summary_img_str = sess.run(image_summary_op,
feed_dict=feed_dict)
summary_writer.add_summary(summary_img_str)
output = "| {0:>13} | {1:13.4f} | Layer {2} | Epoch {3} |"\
.format(step, loss_value, n, step // num_train + 1)
print(output)
filters = sess.run(tf.identity(ae["weights" + str(n)]))
np.save(pjoin(FLAGS.chkpt_dir, "filters" + str(n)), filters)
filters_biases = sess.run(tf.identity(ae["biases" + str(n)]))
np.save(pjoin(FLAGS.chkpt_dir, "biases" + str(n)), filters_biases)
if i == 0:
filters = tile_raster_images(X=filters.T,
img_shape=(FLAGS.image_size,
FLAGS.image_size),
tile_shape=(10, 10),
output_pixel_vals=False)
filters = np.expand_dims(np.expand_dims(filters, 0), 3)
image_var = tf.Variable(filters)
image_filter = tf.identity(image_var)
sess.run(tf.variables_initializer([image_var]))
img_filter_summary_op = tf.summary.image(
"first_layer_filters", image_filter)
summary_writer.add_summary(sess.run(img_filter_summary_op))
summary_writer.flush()
return ae
def main_unsupervised():
with tf.Graph().as_default() as g:
sess = tf.Session()
num_hidden = FLAGS.num_hidden_layers
ae_hidden_shapes = [
getattr(FLAGS, "hidden{0}_units".format(j + 1))
for j in xrange(num_hidden)
]
#ae_shape = [FLAGS.image_pixels] + ae_hidden_shapes + [FLAGS.num_classes]
data = read_data_sets_pretraining(FLAGS.data_dir)
num_train = data.train.num_examples # No. of training examples
ae_shape = [data.train.items.shape[1]] + ae_hidden_shapes
ae = AutoEncoder(ae_shape, sess)
learning_rates = {
j: getattr(FLAGS, "pre_layer{0}_learning_rate".format(j + 1))
for j in xrange(num_hidden)
}
noise = {
j: getattr(FLAGS, "noise_{0}".format(j + 1))
for j in xrange(num_hidden)
}
# for i in xrange(len(ae_shape) - 2):
for i in xrange(len(ae_shape) - 1):
n = i + 1
with tf.variable_scope("pretrain_{0}".format(n)):
input_ = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size, ae_shape[0]),
name='ae_input_pl')
target_ = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size, ae_shape[0]),
name='ae_target_pl')
layer = ae.pretrain_net(input_, n)
with tf.name_scope("target"):
target_for_loss = ae.pretrain_net(
target_, n,
is_target=True) #signal that we want to reconstruct
loss = loss_x_entropy(layer, target_for_loss)
train_op, global_step = training(loss, learning_rates[i], i)
# summarizing training variables
summary_dir = pjoin(FLAGS.summary_dir,
'pretraining_{0}'.format(n))
summary_writer = tf.train.SummaryWriter(
summary_dir,
graph_def=sess.graph_def,
flush_secs=FLAGS.flush_secs)
summary_vars = [
ae["biases{0}".format(n)], ae["weights{0}".format(n)]
]
hist_summarries = [
tf.histogram_summary(v.op.name, v) for v in summary_vars
]
hist_summarries.append(loss_summaries[i])
summary_op = tf.merge_summary(hist_summarries)
# training variables initialization
vars_to_init = ae.get_variables_to_init(n)
vars_to_init.append(global_step)
sess.run(tf.initialize_variables(vars_to_init))
print("\n\n")
print("| Training Step | Cross Entropy | Layer | Epoch |")
print("|---------------|---------------|---------|----------|")
minibatches = int(math.ceil(num_train / FLAGS.batch_size))
# for step in xrange(FLAGS.pretraining_epochs * num_train):
for step in xrange(FLAGS.pretraining_epochs * minibatches):
# feed_dict of input (with injected noise) and target
feed_dict = fill_feed_dict_ae(data.train, input_, target_,
noise[i])
loss_summary, loss_value = sess.run(
[train_op, loss],
feed_dict=feed_dict) # unsupervised training and loss
if step % 100 == 0:
# if step % FLAGS.batch_size == 0:
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
# image_summary_op = \
# tf.image_summary("training_images",
# tf.reshape(input_,
# (FLAGS.batch_size,
# FLAGS.image_size,
# FLAGS.image_size, 1)),
# max_images=FLAGS.batch_size)
#
# summary_img_str = sess.run(image_summary_op,
# feed_dict=feed_dict)
# summary_writer.add_summary(summary_img_str)
output = "| {0:>13} | {1:13.4f} | Layer {2} | Epoch {3} |"\
.format(step, loss_value, n, step // num_train + 1)
print(output)
# if i == 0:
# filters = sess.run(tf.identity(ae["weights1"]))
# np.save(pjoin(FLAGS.chkpt_dir, "filters"), filters)
# filters = tile_raster_images(X=filters.T,
# img_shape=(FLAGS.image_size,
# FLAGS.image_size),
# tile_shape=(10, 10),
# output_pixel_vals=False)
# filters = np.expand_dims(np.expand_dims(filters, 0), 3)
# image_var = tf.Variable(filters)
# image_filter = tf.identity(image_var)
# sess.run(tf.initialize_variables([image_var]))
# img_filter_summary_op = tf.image_summary("first_layer_filters",
# image_filter)
# summary_writer.add_summary(sess.run(img_filter_summary_op))
# summary_writer.flush()
return ae