I am trying to use weightnorm for mnist. I get the following error.

[vishyML]

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

[vishyML]

the cifar10.cnnpy was edited for minst as follows

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from future import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

input image dimensions

img_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.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

convert class vectors to binary class matrices

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

from weightnorm import SGDWithWeightnorm
sgd_wn = SGDWithWeightnorm(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=keras.losses.categorical_crossentropy,optimizer=sgd_wn,
metrics=['accuracy'])

model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

[mirfan899]

Same issue for me too. Code is two years old (2016) and Keras current version is 2.2.4

[vishyML]

I am currently using keras 2.2.2. Did it work for you when you upgraded to 2.2.4 ?

[mirfan899]

Use this repository it has latest implementation https://github.com/krasserm/weightnorm

[vishyML]

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 — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#17 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AeyM4lPBeMuDnN-o6AfwqcEkeMuC1iMtks5u27KsgaJpZM4Y8jWl> .

[exowanderer]

PR #13 fixes this issue.

And TRAVIS says that there are no conflicts with the repo. Is @openai trying to keep this compatible with older versions of Keras? If so, why?