November 2017
Intermediate to advanced
274 pages
6h 16m
English
Content preview from Neural Network Programming with TensorFlow
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Pooling
Pooling layers help with overfitting and improve performance by reducing the size of the input tensor. Typically, they are used to scale down the input, keeping important information. Pooling is a much faster mechanism for input size reduction compared with tf.nn.conv2d.
The following pooling mechanisms are supported by TensorFlow:
- Average
- Max
- Max with argmax
Each pooling operation uses rectangular windows of size ksize separated by offset strides. If strides are all ones (1, 1, 1, 1), every window is used; if strides are all twos (1, 2, 2, 1), every other window is used in each dimension; and so on.
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