Python Deep Learning Cookbook

How to do it...

1. Let's start with importing the libraries as follows:

import numpy as npimport scipy.iofrom matplotlib import pyplot as pltfrom keras.utils import np_utilsfrom keras.models import Sequential, Input, Modelfrom keras.layers.core import Dense, Dropout, Activation, Reshape, Flatten, Lambdafrom keras.layers import Conv2D, MaxPooling2D, UpSampling2Dfrom keras.callbacks import EarlyStopping

2. Next, we load the dataset and extract the data we will use in this recipe:

mat = scipy.io.loadmat('Data/train_32x32.mat')mat = mat['X']b, h, d, n = mat.shape

3. Before feeding the data to our network, we pre-process the data:

#Convert all RGB-Images to greyscaleimg_gray = np.zeros(shape =(n, b, h, 1))def rgb2gray(rgb):  return np.dot(rgb[...,:3], ...