Normalise the input data
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parent
00075b1823
commit
926a82bebf
2 changed files with 11 additions and 7 deletions
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@ -30,10 +30,14 @@ class DataPreprocessor {
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let filenames = await fs.promises.readdir(dirpath);
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let filenames = await fs.promises.readdir(dirpath);
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for(let filename of filenames) {
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for(let filename of filenames) {
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let imagetensor = tf.node.decodeImage(
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let imagebin = await fs.promises.readFile(path.join(dirpath, filename));
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await fs.promises.readFile(path.join(dirpath, filename)),
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let imagetensor = tf.tidy(() => {
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3 // channels
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// Normalise the data to be 0 - 1
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);
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return tf.node.decodeImage(
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imagebin,
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3 // channels
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).cast("float32").div(255);
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});
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if(imagetensor.shape[0] == 256
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if(imagetensor.shape[0] == 256
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&& imagetensor.shape[1] == 256
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&& imagetensor.shape[1] == 256
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&& imagetensor.shape[0] == 3)
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&& imagetensor.shape[0] == 3)
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@ -13,7 +13,7 @@ export default function(cats_count, image_size, use_crossentropy = false) {
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kernelSize: 5,
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kernelSize: 5,
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filters: 3,
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filters: 3,
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strides: 1,
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strides: 1,
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activation: "relu"
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activation: "tanh"
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}));
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}));
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if(image_size > 32) {
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if(image_size > 32) {
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model.add(tf.layers.conv2d({
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model.add(tf.layers.conv2d({
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@ -22,7 +22,7 @@ export default function(cats_count, image_size, use_crossentropy = false) {
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kernelSize: 5,
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kernelSize: 5,
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filters: 3,
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filters: 3,
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strides: 2,
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strides: 2,
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activation: "relu"
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activation: "tanh"
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}));
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}));
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model.add(tf.layers.conv2d({
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model.add(tf.layers.conv2d({
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name: "conv2d_3",
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name: "conv2d_3",
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@ -30,7 +30,7 @@ export default function(cats_count, image_size, use_crossentropy = false) {
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kernelSize: 5,
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kernelSize: 5,
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filters: 3,
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filters: 3,
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strides: 2,
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strides: 2,
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activation: "relu"
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activation: "tanh"
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}));
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}));
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}
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}
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