创建多渠道网络：“连接”对象没有属性“形状”

问题描述

我试图按照以下方法创建一个多输入模型，但是在定义以下内容时遇到了麻烦：

I'm trying to make a multiple input model as follow but am having trouble defining the following:

1. 每个单独输入的实际输入形状


2. 何时应该使用展平型


3. 将我的两个模型合并在一起

我想构建如下内容：

-First Dense Layer-      - First Dense layer -
         |                        |
         |                        |
Second Dense layer          Second Dense layer
                      |
                      |
            Final Dense layer (Single Output)

但是在运行模型时出现以下错误：

However I get the following error when running my model:

AttributeError: 'Concatenate' object has no attribute 'shape'

我的代码

def build_nn_model(x_input1_train, x_input2_train):

    """
    Creates the a multi-channel ANN, capable of accepting multiple inputs.

    :param: none
    :return: the model of the ANN with a single output given
    """

    x_input1= np.expand_dims(x_input1,1)

    # define two sets of inputs for models
    input1= Input(shape = (x_input1.shape[1], 1))
    input2= Input(shape = (x_input2.shape[1], 1))

    # The first branch operates on the first input
    x = Dense(units = 128, activation="relu")(input1)
    x = BatchNormalization()(x)

    x = Dense(units = 128, activation="relu")(x)
    x =Flatten()(x)
    x = BatchNormalization()(x)

    x = Model(inputs=input1, outputs=x)

    # The second branch operates on the second input
    y = Dense(units = 128, activation="relu")(input2)
    y = BatchNormalization()(y)

    y = Dense(units = 128, activation="relu")(y)
    y =Flatten()(y)
    y = BatchNormalization()(y)

    y = Model(inputs=inp_embeddings, outputs=y)

    # combine the output of the two branches
    combined = Concatenate([x.output, y.output])

    # Apply a FC layer and then a regression activation on the combined outputs
    #z = Dense(2, activation="relu")(combined)
    #z = Dense(1, activation="linear")(z)

    outputs = Dense(128, activation='relu')(combined)
    #out = Dropout(0.5)(out)
    outputs = Dense(1)(out)

    # The model will accept the inputs of the two branches and then output a single value
    model = Model(inputs = [x.input, y.input], outputs = out)

    #model = Model(inputs=[x.input, y.input], outputs=z)

    # Compile the NN
    model.compile(loss='mse', optimizer = Adam(lr = 0.001), metrics = ['mse'])

    # ANN Summary
    model.summary()

    return model

Input1 ：

array([55., 46., 46., ..., 60., 60., 45.])

S hape ：（ 2400年）

Shape: (2400,)

Input2 ：

array([[-2.00370455, -2.35689664, -1.96147382, ...,  2.11014128,
         2.59383321,  1.24209607],
       [-1.97130549, -2.19063663, -2.02996445, ...,  2.32125568,
         2.27316046,  1.48600614],
       [-2.01526666, -2.40440917, -1.94321752, ...,  2.15266657,
         2.68460488,  1.23534095],
       ...,
       [-2.1359458 , -2.52428007, -1.75701785, ...,  2.25480819,
         2.68114281,  1.75468981],
       [-1.95868206, -2.23297167, -1.96401751, ...,  2.07427239,
         2.60306072,  1.28556955],
       [-1.80507278, -2.62199521, -2.08697271, ...,  2.34080577,
         2.48254585,  1.52028871]])>

形状：（2400，3840）

Shape: (2400, 3840)

推荐答案

，您需要将方括号添加到 Concatenate 层。它是 Concatenate（）（[x.output，y.output]）

you need to add the brackets to the Concatenate layer. it's Concatenate()([x.output, y.output])

您也可以编写模型而无需使用flatten操作。您的数据是2D的，因此您无需进行任何奇怪的操作。您需要使用展平从3D（或更大尺寸）传递到2D，但在您的情况下，您可以从2D开始而不会出现问题

you can also write your model without the usage of flatten operation. your data are 2D so you don't need to do strange manipulations. you need to use the flatten to pass from 3D (or bigger dimension) to 2D but in your case, you can start from 2D without problems

此处是完整示例

n_sample = 2400
X1 = np.random.uniform(0,1, (n_sample,))  # (2400,)
X2 = np.random.uniform(0,1, (n_sample,3840))  # (2400,3840)
Y = np.random.uniform(0,1, (n_sample,))  # (2400,)

input1= Input(shape = (1, ))
input2= Input(shape = (3840, ))

# The first branch operates on the first input
x = Dense(units = 128, activation="relu")(input1)
x = BatchNormalization()(x)
x = Dense(units = 128, activation="relu")(x)
x = BatchNormalization()(x)
x = Model(inputs=input1, outputs=x)

# The second branch operates on the second input (Protein Embeddings)
y = Dense(units = 128, activation="relu")(input2)
y = BatchNormalization()(y)
y = Dense(units = 128, activation="relu")(y)
y = BatchNormalization()(y)
y = Model(inputs=input2, outputs=y)

# combine the output of the two branches
combined = Concatenate()([x.output, y.output])

out = Dense(128, activation='relu')(combined)
out = Dropout(0.5)(out)
out = Dense(1)(out)

# The model will accept the inputs of the two branches and then output a single value
model = Model(inputs = [x.input, y.input], outputs = out)
model.compile(loss='mse', optimizer = Adam(lr = 0.001), metrics = ['mse'])

model.fit([X1,X2], Y, epochs=3)

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