Python Deep Learning Cookbook

How to do it...

1. We start by import the libraries as follows:

import numpy as np import pandas as pdfrom sklearn.model_selection import train_test_splitfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.callbacks import EarlyStopping, ModelCheckpointfrom keras.optimizers import Adamfrom sklearn.preprocessing import StandardScalerSEED = 2017

2. Load dataset:

data = pd.read_csv('Data/winequality-red.csv', sep=';')y = data['quality']X = data.drop(['quality'], axis=1)

3. Split data for training and testing:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)

4. Print average quality and first rows of training set:

print('Average quality training set: {:.4f}'.format(y_train.mean())) ...