Question

我正在尝试连接2个CNN。这是代码：

        input1 = keras.layers.Input(shape=(64,64,1), name="camera")

        input2 = keras.layers.Input(shape=(64,64,1), name="lidar")
        

        #  First branch

        input1_features = keras.layers.Lambda(lambda x: x / 255.0)(input1)

        input1_features = keras.layers.Conv2D(16, (3, 3), strides=(4, 4), padding = 'same', activation='relu')(input1_features)

        input1_features = keras.layers.Conv2D(32, (2, 2), strides=(2, 2), padding = 'same' ,activation='relu')(input1_features)

        input1_features = keras.layers.Flatten()(input1_features)



        # Second branch

        input2_features = keras.layers.Lambda(lambda x: x / 255.0)(input2)

        input2_features = keras.layers.Conv2D(16, (3, 3), strides=(4, 4), padding = 'same', activation='relu')(input2_features)

        input2_features = keras.layers.Conv2D(32, (2, 2), strides=(2, 2), padding = 'same' ,activation='relu')(input2_features)

        input2_features = keras.layers.Flatten()(input2_features)




        merged = keras.layers.Concatenate(axis=1)([input1_features, input2_features])

        hidden1 = keras.layers.Dense(128, activation='relu')(merged)

        hidden2 = keras.layers.Dense(256, activation='relu')(hidden1)

        output = keras.layers.Dense(action_space, activation='softmax', name="action")(hidden2)

        nn = keras.models.Model([input1, input2], output)
        nn.compile(loss={"camera" : "mse",
                         "lidar" : "mse"},
                          optimizer=Adam(lr=LEARNING_RATE))

ValueError：密集层4的输入0与该层不兼容：预期输入形状的轴-1的值为4096，但接收到形状为[32，512]的输入

这是预测代码：

np.argmax(dqn.predict([camera,lidar]))

我已经仔细检查了两个图像的形状，它们分别是（64,64,1）。

我正在尝试编写一个nn，它将2张图像作为输入并输出9个动作之一。

我已经尝试使用Dense中的所有参数，但一无所获。这是该模型的摘要：

summary model

ValueError：层density_8的输入0与该层不兼容：使用tf.keras 2个CNN连接

0 个答案: