def test_conv_deconv_transpose(self):
def feval(X, y):
f = K.function(X, y)
shape = (np.random.randint(8, 18), ) + tuple(X.shape.as_list()[1:])
x = np.random.rand(*shape)
return f(x)
prog = Progbar(target=2 * 3 * 3 * 2 * 2, print_report=True)
for X in (K.placeholder(shape=(None, 13, 12, 25)),
K.placeholder(shape=(None, 13, 12, 8, 25))):
for strides in (1, 2, 3):
for filter_size in (3, 4, 5):
for num_filters in (8, 25):
for pad in ("same", "valid"):
for dilation in (1, ):
# ====== progress ====== #
prog['test'] = "#Dim:%d;Stride:%d;Filter:%d;Channel:%d;Pad:%s" % \
(X.shape.ndims, strides, filter_size, num_filters, pad)
prog.add(1)
# ====== test Conv ====== #
f = N.Conv(num_filters=num_filters,
filter_size=filter_size,
pad=pad,
strides=strides,
activation=tf.nn.relu,
dilation=dilation)
fT = f.T
y = f(X)
self.assertEqual(
feval(X, y).shape[1:],
tuple(y.shape.as_list()[1:]))
yT = fT(y)
self.assertEqual(
feval(X, yT).shape[1:],
tuple(yT.shape.as_list()[1:]))
self.assertEqual(X.shape.as_list(),
yT.shape.as_list())
# ====== test Transpose ====== #
f = N.TransposeConv(num_filters=num_filters,
filter_size=filter_size,
pad=pad,
strides=strides,
activation=K.relu,
dilation=dilation)
fT = f.T
y = f(X)
self.assertEqual(
feval(X, y).shape[1:],
tuple(y.shape.as_list()[1:]))
yT = fT(y)
self.assertEqual(
feval(X, yT).shape[1:],
tuple(yT.shape.as_list()[1:]))
self.assertEqual(X.shape.as_list(),
yT.shape.as_list())
N.BatchNorm(),
N.Conv(num_filters=64, filter_size=3, pad='valid'),
N.BatchNorm(activation=tf.nn.elu),
N.Pool(),
N.Flatten(outdim=2),
N.Dense(num_units=args.dim)
], debug=True, name='EncoderNetwork')
f_decoder = N.Sequence([
N.Dropout(level=LATENT_DROPOUT, noise_type='uniform'),
N.Noise(level=1.0, noise_type='gaussian'),
N.Dimshuffle((0, 'x', 'x', 1)),
N.TransposeConv(num_filters=64, filter_size=3, pad='valid'),
N.Upsample(size=2, axes=(1, 2)),
N.BatchNorm(),
N.TransposeConv(num_filters=64, filter_size=3, pad='same'),
N.BatchNorm(),
N.TransposeConv(num_filters=32, filter_size=3, pad='valid'),
N.Upsample(size=2, axes=(1, 2),
desire_shape=None if is_cifar10 else (None, 14, 14, None)),
N.BatchNorm(),
N.TransposeConv(num_filters=3 if is_cifar10 else 1,
filter_size=3, strides=2, pad='same'),
N.Dimshuffle((0, 3, 1, 2)) if is_cifar10 else N.Squeeze(axis=-1)
], debug=True, name='DecoderNetwork')
def convolutional_vae(X, saved_states, **kwargs):
""" convolutional_vae
Return
------
[y_encoder, y_decoder]
States
------
[f_inference (encoder), f_generative (decoder)]
"""
n = kwargs.get('n', 10)
batch_size = K.get_shape(X)[0]
if batch_size is None:
raise ValueError(
"You must specify batch_size dimension for the input placeholder.")
# ====== init ====== #
if saved_states is None:
# Encoder
f_inference = N.Sequence([
N.Reshape(shape=(-1, 28, 28, 1)),
N.Conv(num_filters=32,
filter_size=3,
strides=1,
pad='valid',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.Conv(num_filters=64,
filter_size=5,
strides=2,
pad='same',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.Dropout(level=0.1),
N.Flatten(outdim=2),
N.Dense(num_units=n * 2, b_init=None),
N.BatchNorm(axes=0)
],
debug=True,
name='Encoder')
# Decoder
f_generative = N.Sequence([
N.Dimshuffle(pattern=(0, 'x', 'x', 1)),
N.TransposeConv(num_filters=64,
filter_size=3,
strides=1,
pad='valid',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.TransposeConv(num_filters=32,
filter_size=5,
strides=2,
pad='same',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.TransposeConv(num_filters=1,
filter_size=13,
strides=3,
pad='valid',
b_init=None),
N.BatchNorm(activation=K.linear),
N.Flatten(outdim=3)
],
debug=True,
name="Decoder")
else:
f_inference, f_generative = saved_states
# ====== Perfrom ====== #
# Encoder
y_encoder = f_inference(K.cast(X, 'float32'))
mu = y_encoder[:, :n]
sigma = K.softplus(y_encoder[:, n:])
qz = Normal(mu=mu, sigma=sigma, name='Normal_qz')
# Decoder
z = Normal(mu=K.zeros(shape=(batch_size, n)),
sigma=K.ones(shape=(batch_size, n)),
name="Normal_pz")
logits = f_generative(z)
X_reconstruct = Bernoulli(logits=logits)
# inference
params = f_inference.parameters + f_generative.parameters
inference = ed.KLqp(latent_vars={z: qz}, data={X_reconstruct: X})
# ====== get cost for training ====== #
# Bind p(x, z) and q(z | x) to the same placeholder for x.
if K.is_training():
import tensorflow as tf
inference.initialize()
if True:
optimizer = tf.train.AdamOptimizer(0.01, epsilon=1.0)
updates = optimizer.apply_gradients(
optimizer.compute_gradients(inference.loss, var_list=params))
init = tf.global_variables_initializer()
init.run()
f_train = K.function(X, inference.loss, updates)
else:
optimizer = tf.train.AdamOptimizer(0.01, epsilon=1.0)
inference.initialize(optimizer=optimizer, var_list=params)
init = tf.global_variables_initializer()
init.run()
f_train = lambda x: inference.update(feed_dict={X: x})['loss']
samples = K.sigmoid(logits)
return (samples, z, qz), (f_inference, f_generative)