ops.py

import tensorflow as tf


# Standard convolution layer
def conv2d(x, inputFeatures, outputFeatures, name):
    with tf.variable_scope(name):
        w = tf.get_variable("w", [5, 5, inputFeatures, outputFeatures],
                            initializer=tf.truncated_normal_initializer(stddev=0.02))
        b = tf.get_variable("b", [outputFeatures], initializer=tf.constant_initializer(0.0))
        conv = tf.nn.conv2d(x, w, strides=[1, 2, 2, 1], padding="SAME") + b
        return conv


def conv_transpose(x, outputShape, name):
    with tf.variable_scope(name):
        w = tf.get_variable("w", [5, 5, outputShape[-1], x.get_shape()[-1]],
                            initializer=tf.truncated_normal_initializer(stddev=0.02))
        b = tf.get_variable("b", [outputShape[-1]], initializer=tf.constant_initializer(0.0))
        convt = tf.nn.conv2d_transpose(x, w, output_shape=outputShape, strides=[1, 2, 2, 1])
        return convt


# leaky reLu unit
def lrelu(x, leak=0.2, name="lrelu"):
    with tf.variable_scope(name):
        f1 = 0.5 * (1 + leak)
        f2 = 0.5 * (1 - leak)
        return f1 * x + f2 * abs(x)


# fully-conected layer
def dense(x, inputFeatures, outputFeatures, scope=None, with_w=False):
    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [inputFeatures, outputFeatures], tf.float32,
                                 tf.random_normal_initializer(stddev=0.02))
        bias = tf.get_variable("bias", [outputFeatures], initializer=tf.constant_initializer(0.0))
        if with_w:
            return tf.matmul(x, matrix) + bias, matrix, bias
        else:
            return tf.matmul(x, matrix) + bias