connections.py

"""APL 2.0 code from github.com/pkmital/tensorflow_tutorials w/ permission
from Parag K. Mital.
"""
import math
import tensorflow as tf


def batch_norm(x, phase_train, scope='bn', affine=True):
    """
    Batch normalization on convolutional maps.
    from: https://stackoverflow.com/questions/33949786/how-could-i-
    use-batch-normalization-in-tensorflow
    Only modified to infer shape from input tensor x.
    Parameters
    ----------
    x
        Tensor, 4D BHWD input maps
    phase_train
        boolean tf.Variable, true indicates training phase
    scope
        string, variable scope
    affine
        whether to affine-transform outputs
    Return
    ------
    normed
        batch-normalized maps
    """
    with tf.variable_scope(scope):
        og_shape = x.get_shape().as_list()
        if len(og_shape) == 2:
            x = tf.reshape(x, [-1, 1, 1, og_shape[1]])
        shape = x.get_shape().as_list()
        beta = tf.Variable(tf.constant(0.0, shape=[shape[-1]]),
                           name='beta', trainable=True)
        gamma = tf.Variable(tf.constant(1.0, shape=[shape[-1]]),
                            name='gamma', trainable=affine)

        batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
        ema = tf.train.ExponentialMovingAverage(decay=0.9)
        ema_apply_op = ema.apply([batch_mean, batch_var])
        ema_mean, ema_var = ema.average(batch_mean), ema.average(batch_var)

        def mean_var_with_update():
            """Summary
            Returns
            -------
            name : TYPE
                Description
            """
            with tf.control_dependencies([ema_apply_op]):
                return tf.identity(batch_mean), tf.identity(batch_var)
        mean, var = tf.cond(phase_train,
                                          mean_var_with_update,
                                          lambda: (ema_mean, ema_var))

        normed = tf.nn.batch_norm_with_global_normalization(
            x, mean, var, beta, gamma, 1e-3, affine)
        if len(og_shape) == 2:
            normed = tf.reshape(normed, [-1, og_shape[-1]])
    return normed


def lrelu(x, leak=0.2, name="lrelu"):
    """Leaky rectifier.
    Parameters
    ----------
    x : Tensor
        The tensor to apply the nonlinearity to.
    leak : float, optional
        Leakage parameter.
    name : str, optional
        Variable scope to use.
    Returns
    -------
    x : Tensor
        Output of the nonlinearity.
    """
    with tf.variable_scope(name):
        f1 = 0.5 * (1 + leak)
        f2 = 0.5 * (1 - leak)
        return f1 * x + f2 * abs(x)


def linear(x, n_units, scope=None, stddev=0.02,
           activation=lambda x: x):
    """Fully-connected network.
    Parameters
    ----------
    x : Tensor
        Input tensor to the network.
    n_units : int
        Number of units to connect to.
    scope : str, optional
        Variable scope to use.
    stddev : float, optional
        Initialization's standard deviation.
    activation : arguments, optional
        Function which applies a nonlinearity
    Returns
    -------
    x : Tensor
        Fully-connected output.
    """
    shape = x.get_shape().as_list()

    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [shape[1], n_units], tf.float32,
                                 tf.random_normal_initializer(stddev=stddev))
        return activation(tf.matmul(x, matrix))


def conv2d(x, n_filters,
           k_h=5, k_w=5,
           stride_h=2, stride_w=2,
           stddev=0.02,
           activation=None,
           bias=True,
           padding='SAME',
           name="Conv2D"):
    """2D Convolution with options for kernel size, stride, and init deviation.

    Parameters
    ----------
    x : Tensor
        Input tensor to convolve.
    n_filters : int
        Number of filters to apply.
    k_h : int, optional
        Kernel height.
    k_w : int, optional
        Kernel width.
    stride_h : int, optional
        Stride in rows.
    stride_w : int, optional
        Stride in cols.
    stddev : float, optional
        Initialization's standard deviation.
    activation : arguments, optional
        Function which applies a nonlinearity
    padding : str, optional
        'SAME' or 'VALID'
    name : str, optional
        Variable scope to use.

    Returns
    -------
    x : Tensor
        Convolved input.
    """
    with tf.variable_scope(name):
        w = tf.get_variable(
            'w', [k_h, k_w, x.get_shape()[-1], n_filters],
            initializer=tf.truncated_normal_initializer(stddev=stddev))
        conv = tf.nn.conv2d(
            x, w, strides=[1, stride_h, stride_w, 1], padding=padding)
        if bias:
            b = tf.get_variable(
                'b', [n_filters],
                initializer=tf.truncated_normal_initializer(stddev=stddev))
            conv = tf.nn.bias_add(conv, b)
        if activation:
            conv = activation(conv)
        return conv