ops.py

import tensorflow as tf
from tensorflow.contrib.layers.python.layers import batch_norm, variance_scaling_initializer

#the implements of leakyRelu
def lrelu(x , alpha = 0.2 , name="LeakyReLU"):
    return tf.maximum(x , alpha*x)

def conv2d(input_, output_dim,
           k_h=3, k_w=3, d_h=2, d_w=2,
           name="conv2d"):
    with tf.variable_scope(name):

        w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
                            initializer= variance_scaling_initializer())
        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, w

def de_conv(input_, output_shape,
             k_h=3, k_w=3, d_h=2, d_w=2, stddev=0.02, name="deconv2d", 
             with_w=False, initializer = variance_scaling_initializer()):

    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 = initializer)
        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 fully_connect(input_, output_size, scope=None, with_w=False, 
                  initializer = variance_scaling_initializer()):

  shape = input_.get_shape().as_list()

  with tf.variable_scope(scope or "Linear"):

    matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
                 initializer = initializer)
    bias = tf.get_variable("bias", [output_size], initializer=tf.constant_initializer(0.0))
    if with_w:
      return tf.matmul(input_, matrix) + bias, matrix, bias
    else:
      return tf.matmul(input_, matrix) + bias

def conv_cond_concat(x, y):
    """Concatenate conditioning vector on feature map axis."""
    x_shapes = x.get_shape()
    y_shapes = y.get_shape()

    return tf.concat([x , y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2] , y_shapes[3]])], 3)

def batch_normal(input , scope="scope" , reuse=False):
    return batch_norm(input , epsilon=1e-5, decay=0.9 , scale=True, scope=scope , reuse = reuse , updates_collections=None)