Neural network Padding.py

# -*- coding:utf-8 -*-
"""
@author:Levy
@file:Neural network Padding.py
@time:2018/5/1120:48
"""
"""
First week practice for Python deep learning
"""
import numpy as np
import matplotlib.pyplot as plt
import h5py


# GRADED FUNCTION: zero_pad

def zero_pad(X, pad):
    """
    Pad with zeros all images of the dataset X. The padding is applied to the height and width of an image,
    as illustrated in Figure 1.

    Argument:
    X -- python numpy array of shape (m, n_H, n_W, n_C) representing a batch of m images
    pad -- integer, amount of padding around each image on vertical and horizontal dimensions

    Returns:
    X_pad -- padded image of shape (m, n_H + 2*pad, n_W + 2*pad, n_C)
    """

    ### START CODE HERE ### (≈ 1 line)                          # padding method     #padding pattern(front using 0, back using0)
    X_pad = np.pad(X, ((0, 0), (pad, pad), (pad, pad), (0, 0)), 'constant', constant_values=(0, 0))
    ### END CODE HERE ###

    return X_pad

plt.rcParams['figure.figsize']=(5.0,4.0)
plt.rcParams['image.interpolation']='nearest'
plt.rcParams['image.cmap']='gray'

np.random.seed(1)
x = np.random.randn(4, 3, 3, 2)
x_pad = zero_pad(x, 2)
print("x.shape =", x.shape)
print("x_pad.shape =", x_pad.shape)
print("x[1,1] =", x[1, 1])
print("x_pad[1,1] =", x_pad[1, 1])

fig, axarr = plt.subplots(1, 2)
axarr[0].set_title('x')
axarr[0].imshow(x[0, :, :, 0])
axarr[1].set_title('x_pad')
axarr[1].imshow(x_pad[0, :, :, 0])