def test_softmax(self):
a = tensor([1, 3], 'a')
feed = {'a': np.array([[1, 2, 3]])}
answer_list = np.exp([1, 2, 3])
answer_list /= np.sum(answer_list)
assert_array_almost_equal(
operator.softmax(a).forward(feed)[0], answer_list)
def construct_model(self, x_train, y_train):
# get number of features
input_dim = x_train.shape[-1]
# get number of classes
output_dim = len(np.unique(y_train))
layer_num = len(self.hidden_layer_sizes)
hidden_layer_num = self.hidden_layer_sizes
batch_size = self.batch_size
_lambda = self._lambda
if batch_size == 'auto':
# use all data
batch_size = x_train.shape[0]
self.input = lfdnn.tensor([batch_size, input_dim], 'input')
self.label = lfdnn.tensor([batch_size, output_dim], 'label')
h = self.input
# put your construction code here, feel free to modify the assignment of `w`
# Hint: you should put all weight and bias variables into self.weight
w = lfdnn.tensor([input_dim, output_dim], 'output_weight')
# end of your construction code
self.weight['output_weight'] = w
b = lfdnn.tensor([1, output_dim], 'output_bias')
self.weight['output_bias'] = b
h = operator.add(operator.matmul(h, w), b)
self.output = operator.softmax(h)
self.loss = operator.CE_with_logit(h, self.label)
if _lambda > 0:
for k, v in self.weight.items():
if k.find('bias') > 0:
continue
regularization_term = operator.scale(operator.mean_square_sum(v), _lambda)
self.loss = operator.add(self.loss, regularization_term)
self.accuracy = operator.accuracy(self.output, self.label)
def test_log_softmax(self):
a = tensor([1, 3], 'a')
feed = {'a': np.array([[1, 2, 3]])}
assert_array_almost_equal(
operator.log_softmax(a).forward(feed),
operator.log(operator.softmax(a)).forward(feed))