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...
- Let's start with importing all the libraries, as follows:
from keras.datasets import cifar10from keras.preprocessing.image import ImageDataGeneratorfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Activation, Flattenfrom keras.layers import Conv2D, MaxPooling2Dfrom keras.optimizers import Adamfrom keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint
- We will be using the cifar10 dataset for our experiments. We load and pre-process the data like this:
(X_train, y_train), (X_test, y_test) = cifar10.load_data()X_train = X_train.astype('float32')/255.X_test = X_test.astype('float32')/255.n_classes = 10y_train = keras.utils.to_categorical(y_train, n_classes)y_test = keras.utils.to_categorical(y_test, ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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