test_losses.py 文件源码

def test_GWD(self):
        # Compute categorical crossentropy
        indices = self.mock_y > 0
        selected_log = -np.log(self.mock_x_softmax[indices])
        self.loss = 0#np.sum(selected_log) / np.sum(self.mock_y)
        # Create keras model with this activation and compile it
        model = Sequential()
        activation_layer = Lambda(lambda x: x,
                                  input_shape=self.data_shape[1:],
                                  output_shape=self.data_shape[1:]
                                  )
        model.add(activation_layer)
        model.compile('sgd', loss=gwd)

        # Predict data from the model
        loss = model.evaluate(self.mock_y, self.mock_y, batch_size=1, verbose=0)
        # Assertions
        print('Expected loss: {}'.format(self.loss))
        print('Actual loss: {}'.format(loss))
        self.assertTrue(np.allclose(loss, self.loss),
                        msg='Categorical cross-entropy loss 3D does not produce the expected results')