b80fb3bdcfeac64d91b7365be1196392e013dcb8,autokeras/generator.py,RandomConvClassifierGenerator,generate,#RandomConvClassifierGenerator#,80

Before Change

        pool = self._get_pool_layer_func()
        conv = get_conv_layer_func(len(filter_shape))

        model = Sequential()
        for i in range(conv_num):
            kernel_num = randint(10, 30)
            if i == 0:
                model.add(conv(kernel_num,
                               input_shape=self.input_shape,
                               kernel_size=filter_shape,
                               activation="relu",
                               padding="same"))
            else:
                model.add(conv(kernel_num,
                               kernel_size=filter_shape,
                               activation="relu",
                               padding="same"))
            if random() > 0.5:
                model.add(pool(pool_size=pool_shape, padding="same"))
            if random() > 0.5:
                model.add(Dropout(dropout_rate))
        model.add(Flatten())
        for i in range(dense_num):
            node_num = randint(128, 1024)
            model.add(Dense(node_num, activation="relu"))
            if random() > 0.5:
                model.add(Dropout(dropout_rate))
        model.add(Dense(self.n_classes, activation="softmax"))
        model.compile(loss="categorical_crossentropy",
                      optimizer=Adam(),
                      metrics=["accuracy"])
        return model

After Change

        pool = self._get_pool_layer_func()
        conv = get_conv_layer_func(len(filter_shape))

        input_tensor = Input(shape=self.input_shape)
        output_tensor = input_tensor
        for i in range(conv_num):
            kernel_num = randint(10, 30)
            output_tensor = conv(kernel_num, filter_shape,
                                 padding="same")(output_tensor)
            output_tensor = BatchNormalization()(output_tensor)
            output_tensor = Activation("relu")(output_tensor)
            if random() > 0.5:
                output_tensor = pool(pool_size=pool_shape, padding="same")(output_tensor)
            if random() > 0.5:
                output_tensor = Dropout(dropout_rate)(output_tensor)
        output_tensor = Flatten()(output_tensor)
        for i in range(dense_num):
            node_num = randint(128, 1024)
            output_tensor = Dense(node_num, activation="relu")(output_tensor)
            if random() > 0.5:
                output_tensor = Dropout(dropout_rate)(output_tensor)
        output_tensor = Dense(self.n_classes, activation="softmax")(output_tensor)
        model = Model(input_tensor, output_tensor)
        model.compile(loss="categorical_crossentropy",
                      optimizer=Adam(),
                      metrics=["accuracy"])
        return model

Instances