Open linus87 opened 6 days ago
Hi, @linus87
Thank you for bringing this issue to our attention and I was trying to replicate the same issue from my end and I'm getting below out, for your reference I have added screenshot below so I'll ding into this issue and will update you soon.
Thank you for your cooperation and patience.
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System information
Describe the current behavior The argument numTokens of tf.layers.categoryEncoding will impact inputLayer's output shape. The input shape is changed to [..., numTokens].
Describe the expected behavior InputLayer's output shape should be [..., sampleLength]. Since samples are all integers, and they are between 0 and numTokens. CategoryEncoding should be able to create correct output while no other constraint on inputLayer.
Standalone code to reproduce the issue Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/CodePen/any notebook.
`// Tiny TFJS train / predict example. var numFeatures = 1;
// Define the model architecture var model = tf.sequential(); model.add(tf.layers.inputLayer({inputShape: [numFeatures]})); model.add(tf.layers.categoryEncoding({numTokens: 10, outputMode: "count"}));
model.summary(); tfvis.show.modelSummary({name: 'Model Summary'}, model);
// Generate some synthetic data for training // const numbers = tf.range(0, 10, 1); // Generate numbers from 0 to 99 var numbers = tf.rand([10], () => Math.floor(Math.random() * 10), 'int32'); // Generate numbers from 0 to 99 numbers.print();
var input = tf.reshape(numbers, [numFeatures, 10]); input.print();
model.predict(input).print();`
Other info / logs Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.