ops.py

import tensorflow as tf

from tensorflow.python.framework import ops


# h1 = lrelu(
#     tf.contrib.layers.batch_norm(
#         conv2d(h0, self.df_dim*2, name='d_h1_conv'),
#         decay=0.9, updates_collections=None, epsilon=0.00001, scale=True, scope="d_h1_conv"))


def batch_norm(x, train=True, name="batch_norm"):
    if train is False:
        print('---------- Running in inference mode ----------')
    # return tf.contrib.layers.batch_norm(x,
    #                                     decay=0.9,
    #                                     updates_collections=None,
    #                                     is_training=train,
    #                                     epsilon=1e-5,
    #                                     scale=True,
    #                                     scope=name)
    return tf.layers.batch_normalization(x,
                                         momentum=0.9,
                                         epsilon=1e-5,
                                         training=train,
                                         scale=True,
                                         name=name,
                                         renorm=True,
                                         renorm_clipping=None,
                                         renorm_momentum=0.9,
                                         fused=False)


def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="conv2d"):
    with tf.variable_scope(name):
        w = tf.get_variable('w',
                            [k_h, k_w, input_.get_shape()[-1], output_dim],
                            initializer=tf.truncated_normal_initializer(stddev=stddev))
        conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')

        biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
        conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())

        return conv


def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
             name="deconv2d", with_w=False):
    with tf.variable_scope(name):
        # filter : [height, width, output_channels, in_channels]
        w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
                            initializer=tf.random_normal_initializer(stddev=stddev))

        try:
            deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
                                            strides=[1, d_h, d_w, 1])

        # Support for verisons of TensorFlow before 0.7.0
        except AttributeError:
            deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
                                    strides=[1, d_h, d_w, 1])

        biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
        deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())

        if with_w:
            return deconv, w, biases
        else:
            return deconv


def lrelu(x, leak=0.2, name="lrelu"):
    return tf.maximum(x, leak * x)


def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
    shape = input_.get_shape().as_list()

    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
                                 tf.random_normal_initializer(stddev=stddev))
        bias = tf.get_variable("bias", [output_size],
                               initializer=tf.constant_initializer(bias_start))
        if with_w:
            return tf.matmul(input_, matrix) + bias, matrix, bias
        else:
            return tf.matmul(input_, matrix) + bias