ops.py

import tensorflow as tf
from tensorflow.contrib.layers.python.layers import initializers

def conv2d(x,
           output_dim,
           kernel_size,
           stride,
           initializer=tf.contrib.layers.xavier_initializer(),
           activation_fn=tf.nn.relu,
           data_format='NHWC',
           padding='VALID',
           name='conv2d'):
  with tf.variable_scope(name):
    if data_format == 'NCHW':
      stride = [1, 1, stride[0], stride[1]]
      kernel_shape = [kernel_size[0], kernel_size[1], x.get_shape()[1], output_dim]
    elif data_format == 'NHWC':
      stride = [1, stride[0], stride[1], 1]
      kernel_shape = [kernel_size[0], kernel_size[1], x.get_shape()[-1], output_dim]

    w = tf.get_variable('w', kernel_shape, tf.float32, initializer=initializer)
    conv = tf.nn.conv2d(x, w, stride, padding, data_format=data_format)

    b = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
    out = tf.nn.bias_add(conv, b, data_format)

  if activation_fn != None:
    out = activation_fn(out)

  return out, w, b

def linear(input_, output_size, stddev=0.02, bias_start=0.0, activation_fn=None, name='linear'):
  shape = input_.get_shape().as_list()

  with tf.variable_scope(name):
    w = tf.get_variable('Matrix', [shape[1], output_size], tf.float32,
        tf.random_normal_initializer(stddev=stddev))
    b = tf.get_variable('bias', [output_size],
        initializer=tf.constant_initializer(bias_start))

    out = tf.nn.bias_add(tf.matmul(input_, w), b)

    if activation_fn != None:
      return activation_fn(out), w, b
    else:
      return out, w, b

def flatten(input_):
    in_list = [x for x in input_ if x is not None]
    if type(in_list[0]) is list:
      in_list = [flatten(elem) for elem in in_list ]
    
    return tf.concat(0, [ tf.reshape(elem, [-1]) for elem in in_list])