ops.py

import numpy as np
import tensorflow as tf


def flatten(x):
    return tf.reshape(x, [-1, np.prod(x.get_shape().as_list()[1:])])


def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1),
           pad="SAME", dtype=tf.float32, collections=None):
    with tf.variable_scope(name):
        stride_shape = [1, stride[0], stride[1], 1]
        filter_shape = [filter_size[0], filter_size[1],
                        int(x.get_shape()[3]), num_filters]

        # there are "num input feature maps * filter height * filter width"
        # inputs to each hidden unit
        fan_in = np.prod(filter_shape[:3])
        # each unit in the lower layer receives a gradient from:
        # "num output feature maps * filter height * filter width" /
        #   pooling size
        fan_out = np.prod(filter_shape[:2]) * num_filters
        # initialize weights with random weights
        w_bound = np.sqrt(6. / (fan_in + fan_out))

        w = tf.get_variable("W", filter_shape, dtype,
                            tf.random_uniform_initializer(-w_bound, w_bound),
                            collections=collections)
        b = tf.get_variable("b", [1, 1, 1, num_filters],
                            initializer=tf.constant_initializer(0.0),
                            collections=collections)
        return tf.nn.conv2d(x, w, stride_shape, pad) + b


def linear(x, size, name, initializer=None, bias_init=0):
    w = tf.get_variable(name + "/w", [x.get_shape()[1], size], initializer=initializer)
    b = tf.get_variable(name + "/b", [size], initializer=tf.constant_initializer(bias_init))
    return tf.matmul(x, w) + b