def predict(self, X):
mulGate = MultiplyGate()
addGate = AddGate()
layer = Tanh()
softmaxOutput = Softmax()
input = X
for i in range(len(self.W)):
mul = mulGate.forward(self.W[i], input)
add = addGate.forward(mul, self.b[i])
input = layer.forward(add)
probs = softmaxOutput.predict(input)
return np.argmax(probs, axis=1)
def predict(self, x):
output = Softmax()
layers = self.forward_propagation(x)
return [np.argmax(output.predict(layer.mulv)) for layer in layers]
def predict(self, x):
"""Calculate the predictions given data x."""
output = Softmax()
layers = self.forward_propagation(x)
return [np.argmax(output.predict(layer.mulv)) for layer in layers]