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I trained a CNN model on some images following the example of https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html. My model code is identical, I just trained it on another image dataset: also for classification between two classes.
The results are what you'd expect on the training set: images are classified correctly either 0 or 1.
I saved the model in a tensorflowjs-friendly format following the "Alternative: Use the Python API to export directly to TF.js Layers format" section of https://js.tensorflow.org/tutorials/import-keras.html
However when I try to access the results in an html page with javascript I get 1 for pretty much every image (or close to it): even if the image gives 0 in Keras.
I have even saved an image as a tensor in JSON and I get 0 in Keras and 1 in TensorflowJS. Is this a bug or have I made a mistake somewhere ?
Here is my code in TensorflowJS to access the json:
<html>
<head>
<!-- Load TensorFlow.js -->
<meta http-equiv="content-type" content="text/html; charset=utf-8" />
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"></script>
<script>
// https://stackoverflow.com/a/18324384/2730032
function callAjax(url, callback){
var xmlhttp;
// compatible with IE7+, Firefox, Chrome, Opera, Safari
xmlhttp = new XMLHttpRequest();
xmlhttp.onreadystatechange = function(){
if (xmlhttp.readyState == 4 && xmlhttp.status == 200){
callback(xmlhttp.responseText);
}
}
xmlhttp.open("GET", url, true);
xmlhttp.send();
}
tf.loadModel('/model.json').then(model => {
callAjax('/tensor.json', res => {
arr = JSON.parse(res);
const example = tf.tensor(arr).reshape([1, 150, 150, 3]);
const prediction = model.predict(example);
prediction.data().then(res => {
console.log('PREDICTION JS', res[0]);
})
});
})
</script>
</head>
<body>
</body>
</html>
And here is my python code for the same:
import json
import numpy as np
import tensorflowjs as tfjs
model = tfjs.converters.load_keras_model('model.json')
with open('tensor.json', 'r') as f:
r = json.load(f)
arr = np.array([np.array([np.array(v) for v in l]) for l in r])
print('PREDICTION PYTHON', model.predict(arr[np.newaxis,...])[0][0])
I get PREDICTION JS 1 and PREDICTION PYTHON 0.0, for exactly the same data and the same model: does anybody see any issue in my code ?
EDIT1: I'm on Xubuntu 18.04.1 LTS and use the following software versions:
Python 3.6.6
Keras 2.2.4
tensorflow 1.11.0
tensorflowjs 0.6.2
numpy 1.15.2
EDIT2: I opened the following issue https://github.com/tensorflow/tfjs/issues/776 and it has since been fixed.
740
Upgrading to the latest version of tfjs (currently 0.13.3) solves the issue. More context to the question can be viewed here and there
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"></script>
Similar issue here on Mac 10.15, TF 2.3, Python 3.8, plain JavaScript TFJS 2.6.0, webserver: python3 -m http.server
Results of inference would always remain around 0.5 on all units on a large, deep, CNN + RNN Keras network.
Solution: Do not use tensorflowjs_converter for .h5 to TFJS conversion
tensorflowjs_wizard lets you switch off numerical compression, providing almost identical results as Python TF2.3 (in my case up to 6 digits on final layer).
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