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...
- We start by importing the libraries used in this recipe:
import sysimport numpy as npfrom hyperopt import fmin, tpe, hp, STATUS_OK, Trialsfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Activation, Flattenfrom keras.layers import Conv2D, MaxPooling2Dfrom keras.optimizers import Adamfrom sklearn.model_selection import train_test_splitfrom keras.utils import to_categoricalfrom keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpointfrom keras.datasets import cifar10
- Next, we load the cifar10 dataset and pre-process the data:
(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, ...
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