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 follows:
from functools import reducefrom operator import addimport randomfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.utils.np_utils import to_categoricalfrom keras.callbacks import EarlyStopping, ModelCheckpoint
- After importing the libraries, we set some of the hyperparameters:
n_classes = 10batch_size = 128n_epochs = 1000
- Next, we load and preprocess the training data:
(X_train, y_train), (X_val, y_val) = mnist.load_data()X_train = X_train.reshape(60000, 28, 28, 1)X_val = X_val.reshape(10000, 28, 28, 1)X_train = X_train.astype('float32')X_val = X_val.astype('float32')X_val /= 255X_val /= 255y_train = to_categorical(y_train, ...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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