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 all libraries as usual:
from 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, ModelCheckpointfrom keras.datasets import cifar10
- We load and pre-process the training and validation data as follows:
(X_train, y_train), (X_val, y_val) = cifar10.load_data()X_train = X_train.astype('float32')/255.X_val = X_val.astype('float32')/255.n_classes = 10y_train = keras.utils.to_categorical(y_train, n_classes)y_val = keras.utils.to_categorical(y_val, n_classes)
- Next, we define our model ...
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