def pad_conv3d_lrelu(self, activations, n_filters, kernel_size, strides,
scope):
"""Pad, apply 3-D convolution and leaky relu."""
padding = [[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]]
# tf.nn.conv3d accepts a list of 5 values for strides
# with first and last value equal to 1
if isinstance(strides, numbers.Integral):
strides = [strides] * 3
strides = [1] + strides + [1]
# Filter_shape = [K, K, K, num_input, num_output]
filter_shape = ([kernel_size] * 3 + activations.shape[-1:].as_list() +
[n_filters])
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
conv_filter = tf.get_variable(
"conv_filter",
shape=filter_shape,
initializer=tf.truncated_normal_initializer(stddev=0.02))
if self.hparams.use_spectral_norm:
conv_filter, assign_op = common_layers.apply_spectral_norm(
conv_filter)
if self.is_training:
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, assign_op)
padded = tf.pad(activations, padding)
convolved = tf.nn.conv3d(padded,
conv_filter,
strides=strides,
padding="VALID")
rectified = tf.nn.leaky_relu(convolved, alpha=0.2)
return rectified
def testSpectralNorm(self):
# Test that after 20 calls to apply_spectral_norm, the spectral
# norm of the normalized matrix is close to 1.0
with tf.Graph().as_default():
weights = tf.get_variable("w", dtype=tf.float32, shape=[2, 3, 50, 100])
weights = tf.multiply(weights, 10.0)
normed_weight, assign_op = common_layers.apply_spectral_norm(weights)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(20):
sess.run(assign_op)
normed_weight, assign_op = common_layers.apply_spectral_norm(
weights)
normed_weight = sess.run(normed_weight).reshape(-1, 100)
_, s, _ = np.linalg.svd(normed_weight)
self.assertTrue(np.allclose(s[0], 1.0, rtol=0.1))
def testSpectralNorm(self):
# Test that after 20 calls to apply_spectral_norm, the spectral
# norm of the normalized matrix is close to 1.0
with tf.Graph().as_default():
weights = tf.get_variable("w", dtype=tf.float32, shape=[2, 3, 50, 100])
weights = tf.multiply(weights, 10.0)
normed_weight, assign_op = common_layers.apply_spectral_norm(weights)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(20):
sess.run(assign_op)
normed_weight, assign_op = common_layers.apply_spectral_norm(
weights)
normed_weight = sess.run(normed_weight).reshape(-1, 100)
_, s, _ = np.linalg.svd(normed_weight)
self.assertTrue(np.allclose(s[0], 1.0, rtol=0.1))
