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...
- Import all necessary libraries:
import numpy as npfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Flattenfrom keras.callbacks import EarlyStopping, ModelCheckpointfrom keras.layers import Conv2D, MaxPooling2Dfrom keras.optimizers import Adamfrom keras.callbacks import EarlyStopping
- Load the mnist dataset:
from keras.datasets import mnist(X_train, y_train), (X_test, y_test) = mnist.load_data()
- Reshape the training data to represent grayscaled image input:
img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)input_shape ...
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