tensorflow_tutorials_libs.layers

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

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