TensorFlow.js. Co-creator of deeplearn.js. Google Brain, PAIR
Published
Edited
Apr 29, 2018
5 forks
13 stars
collageFn = {
return pixels => {
return tf.tidy(() => {
// Feed the input through the model.
let activation = models[layerId].predict(pixels.expandDims(0)).squeeze();
// Normalize within each channel if the checkbox is checked.
if (channelNormalize) {
// Global pool so we compute normalization params for each channel.
const strides = 1; // Doesn't matter
const pad = 0;
const maxpool = activation.maxPool([activation.shape[0], activation.shape[1]], strides, pad);
const minpool = activation.neg().maxPool(
[activation.shape[0], activation.shape[1]], strides, pad).neg()
// To avoid dividing by zero.
.add(tf.scalar(.0000001));
activation = activation.sub(minpool).div(maxpool.sub(minpool));
}
// Transpose the channels to the outer most dimension and then reshape the activation so we can show
// it in a rectangular collage.
const reshaped = activation.transpose([2, 0, 1]).reshape(
[collageDims[layerId][0], collageDims[layerId][1], activation.shape[0], activation.shape[1]]);
// Split channels between rows and columns. [16, width, height] becomes [4, width, 4, height]
const transposed = reshaped.transpose([0, 2, 1, 3]);
// Combine channels. [4, height, 4, height] becomes [4 * height, 4 * height]
let output = transposed.reshape(
[transposed.shape[0] * transposed.shape[1], transposed.shape[2] * transposed.shape[3]]);
// Normalize across the entire activation if the channelNormalize option isn't checked.
if (!channelNormalize) {
const min = output.min();
const max = output.max();
output = output.sub(min).div(max.sub(min));
}
return output.maximum(tf.scalar(0)).minimum(tf.scalar(1));
});
}
}
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