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I am trying to use weightnorm for mnist. I get the following error. #17
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the cifar10.cnnpy was edited for minst as follows '''Trains a simple convnet on the MNIST dataset. from future import print_function batch_size = 128 input image dimensionsimg_rows, img_cols = 28, 28 the data, split between train and test sets(x_train, y_train), (x_test, y_test) = mnist.load_data() if K.image_data_format() == 'channels_first': x_train = x_train.astype('float32') convert class vectors to binary class matricesy_train = keras.utils.to_categorical(y_train, num_classes) model = Sequential() from weightnorm import SGDWithWeightnorm model.fit(x_train, y_train, |
Same issue for me too. Code is two years old (2016) and |
I am currently using keras 2.2.2. Did it work for you when you upgraded to 2.2.4 ? |
Use this repository it has latest implementation https://github.com/krasserm/weightnorm |
thanks. It works
…On Sat, Dec 8, 2018 at 6:38 AM Muhammad Irfan ***@***.***> wrote:
Use this repository it has latest implementation
https://github.com/krasserm/weightnorm
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Traceback (most recent call last):
File "C:\Users\vkswa\Downloads\weightnorm\cifar10_cnn.py", line 67, in
validation_data=(x_test, y_test))
File "C:\Users\vkswa\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 1008, in fit
self._make_train_function()
File "C:\Users\vkswa\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 498, in _make_train_function
loss=self.total_loss)
TypeError: get_updates() missing 1 required positional argument: 'constraints'
Any suggestion will be helpful
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