October 2017
Intermediate to advanced
330 pages
7h 7m
English
Content preview from Python Deep Learning Cookbook
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How to do it...
- Import libraries as follows:
import numpy as np import pandas as pdimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGDSEED = 2017
- Load the dataset:
data = pd.read_csv('Data/winequality-red.csv', sep=';')y = data['quality']X = data.drop(['quality'], axis=1)
- Split the dataset into training and testing:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
- Normalize the input data:
scaler = StandardScaler().fit(X_train)X_train = pd.DataFrame(scaler.transform(X_train))X_test = pd.DataFrame(scaler.transform(X_test)) ...
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