def reconstruct_images(self,
save_file,
sess,
x,
block_shape,
show_original_images=True,
batch_size=20,
dec_output_2_img_func=None,
**kwargs):
if batch_size < 0:
x1 = self.reconstruct(sess, x, **kwargs)
else:
x1 = []
for batch_ids in iterate_data(len(x), batch_size, shuffle=False):
x1.append(self.reconstruct(sess, x[batch_ids], **kwargs))
x1 = np.concatenate(x1, axis=0)
if dec_output_2_img_func is not None:
x1 = dec_output_2_img_func(x1)
x = dec_output_2_img_func(x)
x1 = np.reshape(x1, to_list(block_shape) + self.x_shape)
x = np.reshape(x, to_list(block_shape) + self.x_shape)
if show_original_images:
save_img_blocks_col_by_col(save_file, [x, x1])
else:
save_img_block(save_file, x1)
def __init__(self, x_shape, z_shape):
super(BaseLatentModel, self).__init__()
self.x_shape = to_list(x_shape)
self.z_shape = to_list(z_shape)
self.x_ph = tf.placeholder(tf.float32, [None] + self.x_shape, name="x")
self.z_ph = tf.placeholder(tf.float32, [None] + self.z_shape, name="z")
def __init__(self, z_shape, stochastic=False, activation=tf.nn.relu):
self.z_shape = to_list(z_shape)
self.z_dim = prod(self.z_shape)
self.stochastic = stochastic
self.activation = activation
print("[{}] activation: {}".format(self.__class__.__name__,
self.activation))
def __init__(self, x_shape, y_shape,
input_perturber, main_classifier,
cons_mode='mse',
cons_4_unlabeled_only=False,
same_perturbed_inputs=False,
weight_decay=-1.0):
LiteBaseModel.__init__(self)
self.x_shape = to_list(x_shape)
self.y_shape = to_list(y_shape)
assert len(self.y_shape) == 1, "'y_shape' must be a scalar or an array of length 1!"
self.num_classes = self.y_shape[0]
self.x_ph = tf.placeholder(tf.float32, [None] + self.x_shape, name="x")
self.y_ph = tf.placeholder(tf.int32, [None], name="y")
self.label_flag_ph = tf.placeholder(tf.bool, [None], name="label_flag")
# Main classifier
self.main_classifier_name = "main_classifier"
self.main_classifier_fn = tf.make_template(
self.main_classifier_name, main_classifier, is_train=self.is_train)
# Input perturber
self.input_perturber_name = "input_perturber"
self.input_perturber_fn = tf.make_template(
self.input_perturber_name, input_perturber, is_train=self.is_train)
possible_cons_modes = ['mse', 'kld', 'rev_kld', '2rand']
assert cons_mode in possible_cons_modes, \
"'cons_mode' must be in {}. Found {}!".format(possible_cons_modes, cons_mode)
self.cons_mode = cons_mode
self.cons_4_unlabeled_only = cons_4_unlabeled_only
self.same_perturbed_inputs = same_perturbed_inputs
self.weight_decay = weight_decay
print("In class [{}]:".format(self.__class__.__name__))
print("cons_mode: {}".format(self.cons_mode))
print("cons_4_unlabeled_only: {}".format(self.cons_4_unlabeled_only))
print("same_perturbed_inputs: {}".format(self.same_perturbed_inputs))
print("weight_decay: {}".format(self.weight_decay))
def generate_images(self,
save_file,
sess,
z,
block_shape,
batch_size=20,
dec_output_2_img_func=None,
**kwargs):
if batch_size < 0:
x1_gen = self.decode(sess, z, **kwargs)
else:
x1_gen = []
for batch_ids in iterate_data(len(z), batch_size, shuffle=False):
x1_gen.append(self.decode(sess, z[batch_ids], **kwargs))
x1_gen = np.concatenate(x1_gen, axis=0)
if dec_output_2_img_func is not None:
x1_gen = dec_output_2_img_func(x1_gen)
x1_gen = np.reshape(x1_gen, to_list(block_shape) + self.x_shape)
save_img_block(save_file, x1_gen)