keras_custom_weight

from keras.models import Sequential
from keras.layers import Dense
from keras import optimizers
import numpy as np
model = Sequential()
model.add(Dense(4, input_shape=(2,)))
model.add(Dense(4, activation='softmax'))
model.compile(optimizer=optimizers.Adagrad(), loss='categorical_crossentropy')


x = np.array([[1,1], [1,1]])
y = np.array([[0,1,0,0], [0,0,0,1]])
weights_mask = np.array([1, 1])
class_weights = {
    0:0,
    1:0,
    2:0,
    3:1
}
# weights_mask = np.array([1])
# model.fit(x,y, epochs=1000, sample_weight=weights_mask, class_weight=class_weights, validation_data=((x,y)))
model.fit(x,y, epochs=1000, class_weight=class_weights, validation_data=((x,y)))

ret = model.predict(x)