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Python Deep Learning Cookbook by Indra den Bakker

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How to do it...

  1. 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
  1. 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, ...

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