4b83c1070cebd0d996ba2cc69779dcb66d5d0032,autokeras/generator.py,DefaultClassifierGenerator,generate,#DefaultClassifierGenerator#Any#Any#,51

Before Change

        for i in range(model_len):
            output_tensor = BatchNormalization()(output_tensor)
            output_tensor = Activation("relu")(output_tensor)
            output_tensor = conv(model_width, kernel_size=self._get_shape(3), padding="same")(output_tensor)
            output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
            if (i + 1) % pooling_len == 0 and i != model_len - 1:
                output_tensor = pool(padding="same")(output_tensor)

        output_tensor = ave()(output_tensor)

After Change

        model.inputs = [0]
        model.layers.append(StubInput())
        for i in range(model_len):
            model.layers += [StubBatchNormalization(),
                             StubActivation("relu"),
                             StubConv(model_width, kernel_size=3, func=conv),
                             StubDropout(constant.CONV_DROPOUT_RATE)]
            if (i + 1) % pooling_len == 0 and i != model_len - 1:
                model.layers.append(StubPooling(func=pool))

        model.layers.append(StubGlobalPooling(ave))
        model.layers.append(StubDense(self.n_classes, activation="softmax"))
        model.outputs = [len(model.layers)]
        for index, layer in enumerate(model.layers):
            layer.input = index
            layer.output = index + 1
        return Graph(model, False)


class RandomConvClassifierGenerator(ClassifierGenerator):

Instances