def convolution_block_2(layer_input, fine_grained_features, num_convolutions, keep_prob, activation_fn, is_training):
x = tf.concat((layer_input, fine_grained_features), axis=-1)
n_channels = get_num_channels(layer_input)
if num_convolutions == 1:
with tf.variable_scope('conv_' + str(1)):
x = convolution(x, [5, 5, 5, n_channels * 2, n_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
# layer_input = tf.layers.batch_normalization(layer_input, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = x + layer_input
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
with tf.variable_scope('conv_' + str(1)):
x = convolution(x, [5, 5, 5, n_channels * 2, n_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
for i in range(1, num_convolutions):
with tf.variable_scope('conv_' + str(i+1)):
x = convolution(x, [5, 5, 5, n_channels, n_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
# layer_input = tf.layers.batch_normalization(layer_input, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
if i == num_convolutions - 1:
x = x + layer_input
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
def convolution_block_2(layer_input, fine_grained_features, num_convolutions,
keep_prob, activation_fn):
x = tf.concat((layer_input, fine_grained_features), axis=-1)
n_channels = get_num_channels(layer_input)
if num_convolutions == 1:
with tf.variable_scope('conv_' + str(1)):
x = convolution(x, [5, 5, 5, n_channels * 2, n_channels])
x = x + layer_input
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
with tf.variable_scope('conv_' + str(1)):
x = convolution(x, [5, 5, 5, n_channels * 2, n_channels])
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
for i in range(1, num_convolutions):
with tf.variable_scope('conv_' + str(i + 1)):
x = convolution(x, [5, 5, 5, n_channels, n_channels])
if i == num_convolutions - 1:
x = x + layer_input
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
def convolution_block(layer_input, num_convolutions, keep_prob, activation_fn):
x = layer_input
n_channels = get_num_channels(x)
for i in range(num_convolutions):
with tf.variable_scope('conv_' + str(i + 1)):
x = convolution(x, [5, 5, 5, n_channels, n_channels])
if i == num_convolutions - 1:
x = x + layer_input
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
def convolution_block(layer_input, num_convolutions, keep_prob, activation_fn, is_training):
x = layer_input
n_channels = get_num_channels(x)
for i in range(num_convolutions):
with tf.variable_scope('conv_' + str(i+1)):
x = convolution(x, [5, 5, 5, n_channels, n_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
# layer_input = tf.layers.batch_normalization(layer_input, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
if i == num_convolutions - 1:
x = x + layer_input
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
