def conditioning_network_28(x,
masks,
nr_filters,
is_training=True,
nonlinearity=None,
bn=True,
kernel_initializer=None,
kernel_regularizer=None,
counters={}):
name = get_name("conditioning_network_28", counters)
x = x * broadcast_masks_tf(masks, num_channels=3)
x = tf.concat([x, broadcast_masks_tf(masks, num_channels=1)], axis=-1)
xs = int_shape(x)
x = tf.concat([x, tf.ones(xs[:-1] + [1])], 3)
with tf.variable_scope(name):
with arg_scope([conv2d, residual_block, dense],
nonlinearity=nonlinearity,
bn=bn,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
is_training=is_training,
counters=counters):
outputs = conv2d(x, nr_filters, 4, 1, "SAME")
for l in range(4):
outputs = conv2d(outputs, nr_filters, 4, 1, "SAME")
outputs = conv2d(outputs,
nr_filters,
1,
1,
"SAME",
nonlinearity=None,
bn=False)
return outputs
def conv_encoder_32(inputs,
z_dim,
nonlinearity=None,
bn=True,
kernel_initializer=None,
kernel_regularizer=None,
is_training=False,
counters={}):
name = get_name("conv_encoder_32", counters)
print("construct", name, "...")
with tf.variable_scope(name):
with arg_scope([conv2d, dense],
nonlinearity=nonlinearity,
bn=bn,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
is_training=is_training,
counters=counters):
outputs = inputs
outputs = conv2d(outputs, 64, 4, 2, "SAME")
outputs = conv2d(outputs, 128, 4, 2, "SAME")
outputs = conv2d(outputs, 128, 4, 2, "SAME")
outputs = conv2d(outputs, 256, 4, 1, "VALID")
outputs = tf.reshape(outputs, [-1, 256])
z_mu = dense(outputs, z_dim, nonlinearity=None, bn=False)
z_log_sigma_sq = dense(outputs, z_dim, nonlinearity=None, bn=False)
return z_mu, z_log_sigma_sq
def omniglot_conv_encoder(inputs,
r_dim,
is_training,
nonlinearity=None,
bn=True,
kernel_initializer=None,
kernel_regularizer=None,
counters={}):
name = get_name("omniglot_conv_encoder", counters)
print("construct", name, "...")
with tf.variable_scope(name):
with arg_scope([conv2d, dense],
nonlinearity=nonlinearity,
bn=bn,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
is_training=is_training):
outputs = inputs
outputs = conv2d(outputs, 64, 3, 1, "SAME")
outputs = conv2d(outputs, 64, 3, 2, "SAME")
outputs = conv2d(outputs, 128, 3, 1, "SAME")
outputs = conv2d(outputs, 128, 3, 2, "SAME")
outputs = conv2d(outputs, 256, 4, 1, "VALID")
outputs = conv2d(outputs, 256, 4, 1, "VALID")
outputs = tf.reshape(outputs, [-1, 256])
r = tf.dense(outputs, r_dim, nonlinearity=None, bn=False)
return r
