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...
- Load the libraries:
import numpy as npimport pandas as pdfrom sklearn.model_selection import train_test_splitfrom keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.callbacks import EarlyStopping, ModelCheckpointfrom keras.optimizers import SGD, Adadelta, Adam, RMSprop, Adagrad, Nadam, Adamax
- Import the dataset and extract the target variable:
data = pd.read_csv('../Data/winequality-red.csv', sep=';')y = data['quality']X = data.drop(['quality'], axis=1)
- Split the dataset for training, validation, and testing:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2017)X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=2017) ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.