activation.py 文件源码

def get_function(self, func_name):
        if func_name == 'tanh':
            return T.tanh
        elif func_name == 'hardtanh':
            L.warning('Current hardTanh implementation is slow!')
            return lambda x: ((abs(x) <= 1) * x) + ((1 < abs(x)) * T.sgn(x))
        elif func_name == 'xtanh':
            return lambda x: T.tanh(x) + 0.1 * x
        elif func_name == 'sigmoid':
            return T.nnet.sigmoid
        elif func_name == 'fastsigmoid':
            L.error('T.nnet.ultra_fast_sigmoid function has some problems')
        elif func_name == 'hardsigmoid':
            return T.nnet.hard_sigmoid
        elif func_name == 'xsigmoid':
            return lambda x: T.nnet.sigmoid(x) + 0.1 * x
        elif func_name == 'softplus':
            return T.nnet.softplus
        elif func_name == 'relu':
            #return lambda x: T.maximum(x, 0)
            return lambda x: x * (x > 0)
            #return T.nnet.relu # Update theano and then use this one instead
        elif func_name == 'leakyrelu':
            return lambda x: T.maximum(x, 0.01 * x)
        elif func_name == 'cappedrelu':
            return lambda x: T.minimum(x * (x > 0), 6)
        elif func_name == 'softmax':
            return T.nnet.softmax
        elif func_name == 'norm1':
            return lambda x: x / T.nlinalg.norm(x, 1)
        elif func_name == 'norm2':
            #return lambda x: x / T.nlinalg.norm(x, 2)
            return lambda x: x / T.dot(x, x)**0.5
        else:
            L.error('Invalid function name given: ' + func_name)