Python Deep Learning Cookbook

How to do it...

1. We start by loading the libraries as follows:

import numpy as npimport pandas as pdfrom matplotlib import pyplot as pltfrom sklearn.model_selection import train_test_splitfrom keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.wrappers.scikit_learn import KerasRegressorfrom keras.callbacks import EarlyStopping, ModelCheckpointfrom keras.optimizers import SGD, Adadelta, Adam, RMSprop, Adagrad, Nadam, Adamax

2. For this test, we will create training, validation, and test sets and pre-process all the sets as follows:

(X_train, y_train), (X_test, y_test) = cifar10.load_data()validation_split = 0.1X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=validation_split, random_state=SEED) ...