kandi X-RAY | keras-js Summary
kandi X-RAY | keras-js Summary
Run Keras models in the browser, with GPU support using WebGL
Top functions reviewed by kandi - BETA
- Create diagram data structure
- cropping the bounds
- Create indices for the given shape .
- Unroll a 2D T .
- get the top - set of top classes
- Creates data from a DDL3D array
- Create an image from an array
- Converts a touch event into a coordinate .
- Calculates the minimum and maximum of the given array .
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keras-js Key Features
keras-js Examples and Code Snippets
Trending Discussions on keras-js
On my local system I know where is the keras.json file present(.keras/keras.json) but when I switched to google colab, I don't know where to find it.
I did google search for this problem but have not got any answer. I went through this link but got nothing helpful.
Any reply and reference will be appreciable....
ANSWERAnswered 2019-Aug-07 at 10:34
I can find that file in colab ~/.keras/keras.json
Good post to dynamically switch backend https://stackoverflow.com/a/44446822/8660575
Im trying to load a simple example network created with keras in the browser using keras-js. After saving the model as .h5 file and converting it to a .bin file I get following error while loading it:...
ANSWERAnswered 2019-Jun-07 at 02:04
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:
First I tried using keras.js, however that only takes 1-dimensional
Float32Array vectors in it's prediction function so I am unable to use it since the lstm_text_generation example uses a multidimensional array of shape
(1, maxlen, len(chars)).
Next I tried using tensorflow.js, using this tutorial to port my keras model to a
model.json file. Everything seems to work fine, up to the point where I perform the actual prediction where it freezes and gives me the warning
Orthogonal initializer is being called on a matrix with more than 2000 (65536) elements: Slowness may result.
I noticed that in many of the tensorflow.js examples, people convert their arrays to
tensor2d, but I did this and it had no effect on the performance of my code.
ANSWERAnswered 2018-Jun-17 at 10:30
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
So it would something like this:
I have been trying to implement a basic Keras model generated in Python into a website using the Keras.js library. Now, I have the model trained and exported into the
model_metadata.json files. Now, I essentially copied and pasted test code from the github page to see if the model would load in browser, but unfortunately I am getting errors. Here is the test code. (EDIT: I fixed some errors, see below for remaining ones.)
ANSWERAnswered 2017-Aug-03 at 19:39
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.
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