i’m find solution
from keras import backend as K
def binaryActivationFromTanh(x, threshold) :
# convert [-inf,+inf] to [-1, 1]
# you can skip this step if your threshold is actually within [-inf, +inf]
activated_x = K.tanh(x)
binary_activated_x = activated_x > threshold
# cast the boolean array to float or int as necessary
# you shall also cast it to Keras default
# binary_activated_x = K.cast_to_floatx(binary_activated_x)
return binary_activated_x
x = Input(shape=(1000,))
y = Dense(3, activation=binaryActivationFromTanh)(x)
for my example i’m have
y = [
[5,9,3],
[6,11,7],
[5,2,6]
…
]
how create
def binaryActivationFromTanh(x, threshold) :
binary_activated_x = ???
a0 = [6,5]
a1 = [9,11,2]
a2 = [3,7,6]
binary_activated_x [0][0] => some_func(a0)
binary_activated_x [0][1] => some_func(a1)
binary_activated_x [0][2] => some_func(a2)
return binary_activated_x
in my example
act =‘relu’
model = Sequential()
model.add(BatchNormalization())
model.add(Dense(34*2, activation=act, input_dim=34))
model.add(Dropout(0.2))
model.add(Dense(34*100, activation=act)) #160 000
model.add(Dropout(0.2))
model.add(Dense(51, activation=btcActAbg)) # ERROR NOW
model.compile( optimizer='adam',loss='mse',metrics=['acc'])
