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...
- First, we start with importing all the libraries needed, as follows:
import numpy as npfrom keras.models import Modelfrom keras.applications import Xceptionfrom keras.layers import Dense, GlobalAveragePooling2Dfrom keras.optimizers import Adam from keras.applications import imagenet_utilsfrom keras.utils import np_utilsfrom keras.callbacks import EarlyStopping
- Next, we load the dataset:
from keras.datasets import cifar10 (X_train, y_train), (X_test, y_test) = cifar10.load_data()
- We need to preprocess the data before feeding it to our model:
n_classes = len(np.unique(y_train))y_train = np_utils.to_categorical(y_train, n_classes)y_test = np_utils.to_categorical(y_test, n_classes) X_train = X_train.astype('float32')/255. ...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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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.