test_neural_networks.py 文件源码

def test_classifier_no_name(self):
        np.random.seed(1988)

        input_dim = 5
        num_hidden = 12
        num_classes = 6
        input_length = 3

        model = Sequential()
        model.add(LSTM(num_hidden, input_dim=input_dim, input_length=input_length, return_sequences=False))
        model.add(Dense(num_classes, activation='softmax'))

        model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])

        input_names = ['input']
        output_names = ['zzzz']
        class_labels = ['a', 'b', 'c', 'd', 'e', 'f']
        predicted_feature_name = 'pf'
        coremlmodel = keras_converter.convert(model, input_names, output_names, class_labels=class_labels, predicted_feature_name=predicted_feature_name)

        inputs = np.random.rand(input_dim)
        outputs = coremlmodel.predict({'input': inputs})
        # this checks that the dictionary got the right name and type
        self.assertEquals(type(outputs[output_names[0]]), type({'a': 0.5}))