tf_utils.py

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# %% Borrowed utils from here: https://github.com/pkmital/tensorflow_tutorials/
import tensorflow as tf
import numpy as np

def conv2d(x, n_filters,
           k_h=5, k_w=5,
           stride_h=2, stride_w=2,
           stddev=0.02,
           activation=lambda x: x,
           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 = conv + b
        return conv
    
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 weight_variable(shape):
    '''Helper function to create a weight variable initialized with
    a normal distribution
    Parameters
    ----------
    shape : list
        Size of weight variable
    '''
    #initial = tf.random_normal(shape, mean=0.0, stddev=0.01)
    initial = tf.zeros(shape)
    return tf.Variable(initial)

# %%
def bias_variable(shape):
    '''Helper function to create a bias variable initialized with
    a constant value.
    Parameters
    ----------
    shape : list
        Size of weight variable
    '''
    initial = tf.random_normal(shape, mean=0.0, stddev=0.01)
    return tf.Variable(initial)

# %% 
def dense_to_one_hot(labels, n_classes=2):
    """Convert class labels from scalars to one-hot vectors."""
    labels = np.array(labels)
    n_labels = labels.shape[0]
    index_offset = np.arange(n_labels) * n_classes
    labels_one_hot = np.zeros((n_labels, n_classes), dtype=np.float32)
    labels_one_hot.flat[index_offset + labels.ravel()] = 1
    return labels_one_hot