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Author SHA1 Message Date
hexahigh
f9460828bb Github Action: Updated torrent file 2023-09-11 08:56:12 +00:00
Boof
7783cb4e31
test ai model 2023-09-11 10:54:47 +02:00
10 changed files with 74 additions and 2 deletions
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15
other/tensor/test1/index.html Normal file
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<!DOCTYPE html>
<html>
<head>
<title>Audio Classification</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.8.4/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/speech-commands@0.4.0/dist/speech-commands.min.js"></script>
<script src="script.js" defer></script>
</head>
<body>
<div>Audio Model</div>
<button type="button" onclick="init()">Start</button>
<div id="label-container"></div>
</body>
</html>

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{"format": "layers-model", "generatedBy": "keras v2.13.1", "convertedBy": "TensorFlow.js Converter v4.10.0", "modelTopology": {"keras_version": "2.13.1", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 128, 87, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "batch_input_shape": [null, 128, 87, 1], "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 2, "activation": "softmax", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}}, "training_config": {"loss": "sparse_categorical_crossentropy", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Custom>Adam", "config": {"name": "Adam", "weight_decay": null, "clipnorm": null, "global_clipnorm": null, "clipvalue": null, "use_ema": false, "ema_momentum": 0.99, "ema_overwrite_frequency": null, "jit_compile": false, "is_legacy_optimizer": false, "learning_rate": 0.0010000000474974513, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07, "amsgrad": false}}}}, "weightsManifest": [{"paths": ["group1-shard1of6.bin", "group1-shard2of6.bin", "group1-shard3of6.bin", "group1-shard4of6.bin", "group1-shard5of6.bin", "group1-shard6of6.bin"], "weights": [{"name": "conv2d/kernel", "shape": [3, 3, 1, 32], "dtype": "float32"}, {"name": "conv2d/bias", "shape": [32], "dtype": "float32"}, {"name": "dense/kernel", "shape": [84672, 64], "dtype": "float32"}, {"name": "dense/bias", "shape": [64], "dtype": "float32"}, {"name": "dense_1/kernel", "shape": [64, 2], "dtype": "float32"}, {"name": "dense_1/bias", "shape": [2], "dtype": "float32"}]}]}

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// more documentation available at
// https://github.com/tensorflow/tfjs-models/tree/master/speech-commands
// the link to your model provided by Teachable Machine export panel
const URL = "./model/";
async function createModel() {
const checkpointURL = URL + "model.json"; // model topology
const metadataURL = URL + "metadata.json"; // model metadata
const recognizer = speechCommands.create(
"BROWSER_FFT", // fourier transform type, not useful to change
undefined, // speech commands vocabulary feature, not useful for your models
checkpointURL,
metadataURL);
// check that model and metadata are loaded via HTTPS requests.
await recognizer.ensureModelLoaded();
return recognizer;
}
async function init() {
const recognizer = await createModel();
const classLabels = recognizer.wordLabels(); // get class labels
const labelContainer = document.getElementById("label-container");
for (let i = 0; i < classLabels.length; i++) {
labelContainer.appendChild(document.createElement("div"));
}
// listen() takes two arguments:
// 1. A callback function that is invoked anytime a word is recognized.
// 2. A configuration object with adjustable fields
recognizer.listen(result => {
const scores = result.scores; // probability of prediction for each class
// render the probability scores per class
for (let i = 0; i < classLabels.length; i++) {
const classPrediction = classLabels[i] + ": " + result.scores[i].toFixed(2);
labelContainer.childNodes[i].innerHTML = classPrediction;
}
}, {
includeSpectrogram: true, // in case listen should return result.spectrogram
probabilityThreshold: 0.75,
invokeCallbackOnNoiseAndUnknown: true,
overlapFactor: 0.50 // probably want between 0.5 and 0.75. More info in README
});
// Stop the recognition in 5 seconds.
// setTimeout(() => recognizer.stopListening(), 5000);
}

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