keras-js | Run Keras models in the browser, with GPU support using WebGL | Machine Learning library

Well, I knew nothing about this JS library but I tried to reproduce the problem and I really get that error you mentioned. HOWEVER, a careful programmer would notice that an error previous to that mentioned error had shown up. And it was the following:

Access to XMLHttpRequest at 'file:///< your_local_path_to_keras-js >/keras-js-master/example.bin' from origin 'null' has been blocked by CORS policy: Cross origin requests are only supported for protocol schemes: http, data, chrome, chrome-extension, https.

You can read more about this issue in this question. Basically, a webapp is not allowed to access your local files due to security measures. Then, you need to serve these files, which can be easily done with the following python command:

x_pred needs to be a tensor, the simplest way to create a tensor with custom values is tf.buffer, which can be initialized with a TypedArray or can be modified using .set() which would be better for you, because most of your values are 0 and buffer are filled with zeros by default. And to create a tensor out of a buffer just use .toTensor();

So it would something like this:

Ok, so I figured out why this was happening. There were two problems. First, the data array needs to be flattened, so i wrote a quick function to take the 2D input and "flatten" it to be a 1D array of length 784. Then, because I used a Sequential model, the key name of the data should not have been 'input_1', but rather just 'input'. This got rid of all the errors.

Now, to get the output information, we simply can store it in an array like this: var out = outputData['output']. Because I used the MNIST data set, out was a 1D array of length 10 that contained probabilities of each digit being the user-written digit. From there, you can simply find the number with the highest probability and use that as a prediciton for the model.