Tensorflow.js and Tensorflow(keras_model.h5) give different results.

[hermes7308]

I am trying to use for image filtering.

Tensorflow.js gives the correct result but Tensorflow(keras_model.h5) gives the incorrect result.

I export the same training project, why this gives me different results.

I just change webcam to file using Tensorflow.js

Tensorflow.js

    // run the webcam image through the image model
    async function predict() {
        // predict can take in an image, video or canvas html element

        const image = document.getElementById("face-image");
        if (image == null) {
            alert("Please select image.");
            return;
        }

        labelContainer.innerHTML = "";

        const prediction = await model.predict(image, false);
        for (let i = 0; i < maxPredictions; i++) {
           const classPrediction =
                prediction[i].className + ": " + prediction[i].probability.toFixed(2);
            labelContainer.childNodes[i].innerHTML = classPrediction;
        }
    }

return: [[1.0 0]] (correct)

Tensorflow(keras_model.h5)

    # Create the array of the right shape to feed into the keras model
    # The 'length' or number of images you can put into the array is
    # determined by the first position in the shape tuple, in this case 1.
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

    # Replace this with the path to your image
    image = Image.open(image_file_path)

    # resize the image to a 224x224 with the same strategy as in TM2:
    # resizing the image to be at least 224x224 and then cropping from the center
    size = (224, 224)
    image = ImageOps.fit(image, size, Image.ANTIALIAS)

    # turn the image into a numpy array
    image_array = np.asarray(image)

    # display the resized image
    image.show()

    # Normalize the image
    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1

    # Load the image into the array
    data[0] = normalized_image_array

    # run the inference
    prediction = model.predict(data)

return: [[0.32318377 0.6768162]]