model.py 文件源码

def discriminator_stego_nn(self, img, batch_size, name):
        eve_input = self.image_processing_layer(img)
        eve_conv1 = convolution2d(eve_input, 64, kernel_size = [5, 5], stride = [2,2],
        activation_fn= tf.nn.relu, normalizer_fn = BatchNorm, scope = 'eve/' + name + '/conv1')

        eve_conv2 = convolution2d(eve_conv1, 64 * 2, kernel_size = [5, 5], stride = [2,2],
        activation_fn= tf.nn.relu, normalizer_fn = BatchNorm, scope = 'eve/' + name + '/conv2')

        eve_conv3 = convolution2d(eve_conv2, 64 * 4,kernel_size = [5, 5], stride = [2,2],
        activation_fn= tf.nn.relu, normalizer_fn = BatchNorm, scope = 'eve/' + name + '/conv3')

        eve_conv4 = convolution2d(eve_conv3, 64* 8, kernel_size = [5, 5], stride = [2,2],
        activation_fn= tf.nn.relu, normalizer_fn = BatchNorm, scope = 'eve/' + name + '/conv4')

        eve_conv4 = tf.reshape(eve_conv4, [batch_size, -1])

        #eve_fc = fully_connected(eve_conv4, 1, activation_fn = tf.nn.sigmoid, normalizer_fn = BatchNorm,
        #weights_initializer=tf.random_normal_initializer(stddev=1.0))
        eve_fc = fully_connected(eve_conv4, 1, normalizer_fn = BatchNorm, 
        weights_initializer=tf.random_normal_initializer(stddev=1.0), scope = 'eve' + name + '/final_fc')
        return eve_fc