python类calculate_gain()的实例源码

def test_calculate_gain_leaky_relu_only_accepts_numbers(self):
        for param in [True, [1], {'a': 'b'}]:
            with self.assertRaises(ValueError):
                init.calculate_gain('leaky_relu', param)

def test_calculate_gain_only_accepts_valid_nonlinearities(self):
        for n in [2, 5, 25]:
            # Generate random strings of lengths that definitely aren't supported
            random_string = ''.join([random.choice(string.ascii_lowercase) for i in range(n)])
            with self.assertRaises(ValueError):
                init.calculate_gain(random_string)

def reset_parameters(self):

        tanh_gain = weight_init.calculate_gain('tanh')
        linear_gain = weight_init.calculate_gain('linear')

        weight_init.xavier_uniform(self.W_s1.data, tanh_gain)
        weight_init.xavier_uniform(self.W_s2.data, linear_gain)

def reset_parameters(self):

        linear_gain = weight_init.calculate_gain('linear')

        weight_init.xavier_uniform(self.W_x.data, linear_gain)
        weight_init.xavier_uniform(self.W_y.data, linear_gain)
        weight_init.xavier_uniform(self.W_z.data, linear_gain)

def initWeight(self):
        for name, params in self.named_parameters():
            # weight?xavier????
            if 'weight' in name:
                init.xavier_uniform(params, init.calculate_gain('relu'))
            # bias?0????
            else:
                init.constant(params, 0)

def _initialize_weights(self):
        init.orthogonal(self.conv1.weight, init.calculate_gain('relu'))
        init.orthogonal(self.conv2.weight, init.calculate_gain('relu'))
        init.orthogonal(self.conv3.weight, init.calculate_gain('relu'))
        init.orthogonal(self.conv4.weight)

# Create the super-resolution model by using the above model definition.