import numpy as np
import tensorflow as tf
from scipy import spatial
import operator


def weight_variable(shape, name=None):
    initial = tf.glorot_uniform_initializer()
    return tf.Variable(initial(shape), name=name)


def bias_variable(shape, name=''):
    initial = tf.zeros_initializer()
    return tf.Variable(initial(shape), name=name)


def prewitt_filter():
    v = np.array([[1, 0, -1]] * 3)
    h = v.swapaxes(0, 1)
    return tf.constant(np.dstack([v, h]).reshape((3, 3, 1, 2)), dtype=tf.
        float32, name='prewitt')


def mnist_samples(flatten_image=False, binalize_label=False):
    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

    def normalize(images):
        return images.astype(np.float32) / 255.0

    def flatten(images):
        d, w, h = images.shape
        return images.reshape(d, w * h)

    def binalize(labels):
        return list(map(lambda x: [1] if x == 1 else [0], labels))

    def one_hot_label(labels):
        return tf.keras.utils.to_categorical(labels, 10)
    X_train, X_test = normalize(X_train), normalize(X_test)
    if flatten_image:
        X_train, X_test = flatten(X_train), flatten(X_test)
    if binalize_label:
        y_train, y_test = binalize(y_train), binalize(y_test)
    else:
        y_train, y_test = one_hot_label(y_train), one_hot_label(y_test)
    return (X_train, y_train), (X_test, y_test)

57d5a9f7dfe5c2c72b59a0ce447c1c57d7023f5f
<mask token>


def mnist_samples(flatten_image=False, binalize_label=False):
    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

    def normalize(images):
        return images.astype(np.float32) / 255.0

    def flatten(images):
        d, w, h = images.shape
        return images.reshape(d, w * h)

    def binalize(labels):
        return list(map(lambda x: [1] if x == 1 else [0], labels))

    def one_hot_label(labels):
        return tf.keras.utils.to_categorical(labels, 10)
    X_train, X_test = normalize(X_train), normalize(X_test)
    if flatten_image:
        X_train, X_test = flatten(X_train), flatten(X_test)
    if binalize_label:
        y_train, y_test = binalize(y_train), binalize(y_test)
    else:
        y_train, y_test = one_hot_label(y_train), one_hot_label(y_test)
    return (X_train, y_train), (X_test, y_test)

<mask token>


def bias_variable(shape, name=''):
    initial = tf.zeros_initializer()
    return tf.Variable(initial(shape), name=name)


<mask token>


def mnist_samples(flatten_image=False, binalize_label=False):
    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

    def normalize(images):
        return images.astype(np.float32) / 255.0

    def flatten(images):
        d, w, h = images.shape
        return images.reshape(d, w * h)

    def binalize(labels):
        return list(map(lambda x: [1] if x == 1 else [0], labels))

    def one_hot_label(labels):
        return tf.keras.utils.to_categorical(labels, 10)
    X_train, X_test = normalize(X_train), normalize(X_test)
    if flatten_image:
        X_train, X_test = flatten(X_train), flatten(X_test)
    if binalize_label:
        y_train, y_test = binalize(y_train), binalize(y_test)
    else:
        y_train, y_test = one_hot_label(y_train), one_hot_label(y_test)
    return (X_train, y_train), (X_test, y_test)

<mask token>


def weight_variable(shape, name=None):
    initial = tf.glorot_uniform_initializer()
    return tf.Variable(initial(shape), name=name)


def bias_variable(shape, name=''):
    initial = tf.zeros_initializer()
    return tf.Variable(initial(shape), name=name)


def prewitt_filter():
    v = np.array([[1, 0, -1]] * 3)
    h = v.swapaxes(0, 1)
    return tf.constant(np.dstack([v, h]).reshape((3, 3, 1, 2)), dtype=tf.
        float32, name='prewitt')


def mnist_samples(flatten_image=False, binalize_label=False):
    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

    def normalize(images):
        return images.astype(np.float32) / 255.0

    def flatten(images):
        d, w, h = images.shape
        return images.reshape(d, w * h)

    def binalize(labels):
        return list(map(lambda x: [1] if x == 1 else [0], labels))

    def one_hot_label(labels):
        return tf.keras.utils.to_categorical(labels, 10)
    X_train, X_test = normalize(X_train), normalize(X_test)
    if flatten_image:
        X_train, X_test = flatten(X_train), flatten(X_test)
    if binalize_label:
        y_train, y_test = binalize(y_train), binalize(y_test)
    else:
        y_train, y_test = one_hot_label(y_train), one_hot_label(y_test)
    return (X_train, y_train), (X_test, y_test)

import numpy as np
import tensorflow as tf
from scipy import spatial
import operator


def weight_variable(shape, name=None):
    initial = tf.glorot_uniform_initializer()
    return tf.Variable(initial(shape), name=name)


def bias_variable(shape, name=''):
    initial = tf.zeros_initializer()
    return tf.Variable(initial(shape), name=name)


def prewitt_filter():
    v = np.array([[1, 0, -1]] * 3)
    h = v.swapaxes(0, 1)
    return tf.constant(np.dstack([v, h]).reshape((3, 3, 1, 2)), dtype=tf.
        float32, name='prewitt')


def mnist_samples(flatten_image=False, binalize_label=False):
    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

    def normalize(images):
        return images.astype(np.float32) / 255.0

    def flatten(images):
        d, w, h = images.shape
        return images.reshape(d, w * h)

    def binalize(labels):
        return list(map(lambda x: [1] if x == 1 else [0], labels))

    def one_hot_label(labels):
        return tf.keras.utils.to_categorical(labels, 10)
    X_train, X_test = normalize(X_train), normalize(X_test)
    if flatten_image:
        X_train, X_test = flatten(X_train), flatten(X_test)
    if binalize_label:
        y_train, y_test = binalize(y_train), binalize(y_test)
    else:
        y_train, y_test = one_hot_label(y_train), one_hot_label(y_test)
    return (X_train, y_train), (X_test, y_test)
