April 2017
Intermediate to advanced
318 pages
7h 40m
English
Content preview from Deep Learning with Keras
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Improving the CIFAR-10 performance with deeper a network
One way to improve the performance is to define a deeper network with multiple convolutional operations. In this example, we have a sequence of modules:
conv+conv+maxpool+dropout+conv+conv+maxpool
Followed by a standard dense+dropout+dense. All the activation functions are ReLU.
Let us see the code for the new network:
model = Sequential()model.add(Conv2D(32, (3, 3), padding='same',input_shape=(IMG_ROWS, IMG_COLS, IMG_CHANNELS)))model.add(Activation('relu'))model.add(Conv2D(32, (3, 3), padding='same'))model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(64, (3, 3), padding='same'))model.add(Activation('relu'))model.add(Conv2D(64, ...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.