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

Before Change

        pooling_len = int(model_len / 4)
        output_tensor = input_tensor = Input(shape=self.input_shape)
        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)
        output_tensor = Dense(self.n_classes, activation="softmax")(output_tensor)
        return Model(inputs=input_tensor, outputs=output_tensor)


class RandomConvClassifierGenerator(ClassifierGenerator):

After Change

        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)

Instances