def bottleneck_layer(self, x, scope):
with tf.variable_scope(scope):
# x = layers.batch_normalization(x, training=self.training, name=scope + '_batch1')
x = layers.selu(x)
x = layers.conv2d(x,
filters=4 * self.filters,
kernel_size=[1, 1],
strides=[1, 1],
kernel_regularizer=layers.l2_regularizer(0.0005),
padding='same',
activation=None,
name=scope + '_conv1')
x = layers.drop_out(x, rate=self.dropout, training=self.training)
# x = layers.batch_normalization(x, training=self.training, name=scope + '_batch2')
x = layers.selu(x)
x = layers.conv2d(x,
filters=self.filters,
kernel_size=[3, 3],
strides=[1, 1],
kernel_regularizer=layers.l2_regularizer(0.0005),
padding='same',
activation=None,
name=scope + '_conv2')
x = layers.drop_out(x, rate=self.dropout, training=self.training)
return x
def Dense_net(self, input_x):
x = layers.conv2d(input_x,
filters=2 * self.filters,
kernel_size=[5, 5],
strides=[2, 2],
kernel_regularizer=layers.l2_regularizer(0.0005),
padding='valid',
activation=None,
name='conv0')
x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')
x = self.transition_layer(x, scope='trans_1')
x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')
x = self.transition_layer(x, scope='trans_2')
# x = self.dense_block(input_x=x, nb_layers=48, layer_name='dense_3')
# x = self.transition_layer(x, scope='trans_3')
# x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final')
x = self.dense_block(input_x=x, nb_layers=24, layer_name='dense_final')
# 100 Layer
# x = layers.batch_normalization(x, training=self.training, name='linear_batch')
x = layers.selu(x)
# x = layers.global_ave_pool2d(x)
# x = flatten(x)
# x = layers.fully_connected(x, self.class_num, use_bias=False, activation_fn=None, trainable=self.training,
# name='full_connecting')
# x = tf.reshape(x, [-1, 10])
return x
def transition_layer(self, x, scope):
with tf.variable_scope(scope):
x = layers.batch_normalization(x, training=self.training, name=scope + '_batch1')
x = layers.selu(x)
x = layers.conv2d(x, filters=self.filters, kernel_size=[1, 1], strides=[1, 1],
kernel_regularizer=layers.l2_regularizer(0.0005),
padding='same', activation=None, name=scope + '_conv1')
x = layers.drop_out(x, rate=self.dropout, training=self.training)
x = layers.ave_pool2d(x, pool_size=[2, 2], strides=[2, 2])
return x