p454_gan_model.py

import tensorflow.contrib.slim as slim
import tensorflow as tf

def discriminate(input_data, scope="discriminate", reuse=False, is_training=True):
    batch_norm_params = {
        "is_training": is_training,
        "decay": 0.9997,
        "epsilon": 0.001,
        "updates_collections": tf.GraphKeys.UPDATE_OPS,
        "variables_collections": {
            "beta": None,
            "gamma": None,
            "moving_mean": ['moving_vars'],
            'moving_variance': ['moving_vars'],
        }
    }

    with tf.variable_scope(scope, 'discriminate', [input_data]):
        with slim.arg_scope([slim.conv2d],
                            normalizer_fn=slim.batch_norm,
                            normalizer_params=batch_norm_params,
                            weights_initializer=tf.truncated_normal_initializer(0.0, 0.1),
                            weights_regularizer=slim.l1_l2_regularizer()):
            conv1 = slim.conv2d(input_data, 32, [3, 3], padding="SAME", scope="d_conv1", reuse=reuse)
            conv2 = slim.conv2d(conv1, 64, [5, 5], padding="SAME", scope="d_conv2", reuse=reuse)
            conv3 = slim.conv2d(conv2, 32, [3, 3], padding="SAME", scope="d_conv3", reuse=reuse)
            out = slim.conv2d(conv3, 1, [28, 28], padding="VALID", scope="d_conv4", reuse=reuse)
        return out


def generate(batch_size, trainable = True, scope="generate", reuse=False, is_training=True):
    batch_norm_params = {
        "is_training": is_training,
        "decay": 0.9997,
        "epsilon": 0.001,
        "updates_collections": tf.GraphKeys.UPDATE_OPS,
        "variables_collections": {
            "beta": None,
            "gamma": None,
            "moving_mean": ["moving_vars"],
            "moving_variance": ["moving_vars"],
        }
    }

    with tf.variable_scope(scope, "generate", [batch_size]):
        with slim.arg_scope([slim.conv2d],
                            normalizer_fn=slim.batch_norm,
                            normalizer_params=batch_norm_params,
                            weights_initializer=tf.truncated_normal_initializer(0.0, 0.1),
                            weights_regularizer=slim.l2_regularizer(0.00005)):
            img_x = tf.random_normal([batch_size, 28, 28, 1])
            conv1 = slim.conv2d(img_x, 32, [3, 3], padding="SAME", scope="g_conv1", trainable=trainable, reuse=reuse)
            conv2 = slim.conv2d(conv1, 64, [5, 5], padding="SAME", scope="g_conv2", trainable=trainable, reuse=reuse)
            conv3 = slim.conv2d(conv2, 32, [3, 3], padding="SAME", scope="g_conv3", trainable=trainable, reuse=reuse)
            conv4 = slim.conv2d(conv3, 1, [5, 5], padding="SAME", scope="g_conv4", trainable=trainable, reuse=reuse)
            out = tf.tanh(conv4, name="g_tanh")

    return out