我实现了以下代码。在之前版本的Keras中它可以正常工作：
max_sequence = 56input_dim = 26 print("构建模型..1")first_input = Input(shape=(max_sequence,input_dim))first_lstm = LSTM(5, return_sequences=True)(first_input)first_bn = BatchNormalization()(first_lstm)first_activation = Activation('tanh')(first_bn)first_flat = Flatten()(first_activation)print("构建模型..2")second_input = Input(shape=(max_sequence,input_dim))second_lstm = LSTM(5, return_sequences=True)(second_input)second_bn = BatchNormalization()(second_lstm)second_activation = Activation('tanh')(second_bn)second_flat = Flatten()(second_activation)merge=concatenate([first_flat, second_flat])merge_dense=Dense(3)(merge)merge_bn = BatchNormalization()(merge_dense)merge_activation = Activation('tanh')(merge_bn)merge_dense2=Dense(1)(merge_activation)merge_activation2 = Activation('tanh')(merge_dense2)train_x_1 = np.reshape(np.array(train_x_1), [2999, 56, 26])train_x_2 = np.reshape(np.array(train_x_2), [2999, 56, 26])model=Model(inputs=[train_x_1,train_x_2], outputs=train_y_class)optimizer = RMSprop(lr=0.5)model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])history = model.fit([train_x_1, train_x_2], train_y_class, nb_epoch=300, batch_size=128, validation_data=([val_x_1, val_x_2], val_y_class))运行时：
history = model.fit([train_x_1, train_x_2], train_y_class, nb_epoch=300, batch_size=128, validation_data=([val_x_1, val_x_2], val_y_class))出现了以下错误：
TypeError: unhashable type: 'numpy.ndarray' 出现。因此，我检查了 train_x_1, train_x_2, train_y_class。它们的类型是 <class 'numpy.ndarray'>。我搜索了解决方案，尝试将其类型更改为元组，但没有成功。
如果 numpy.ndarray 是不可哈希的，那么 model.fit 接收什么类型的输入？
训练数据的形状如下：
train_x_1.shape(2999, 56, 26)train_x_2.shape(2999, 56, 26)train_y_class.shape(2999, 1)train_x_1 的一个样本如下所示：
array([[[ 1.62601626e-02, 2.26890756e-01, 1.17764920e-02, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [ 1.62601626e-02, 2.26890756e-01, 1.17764920e-02, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [ 1.62601626e-02, 2.26890756e-01, 1.17764920e-02, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], ..., [ 1.62601626e-02, 2.26890756e-01, 1.17764920e-02, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [ 1.62601626e-02, 2.26890756e-01, 1.17764920e-02, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [ 1.62601626e-02, 2.26890756e-01, 1.17764920e-02, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],回答：
问题在于你在构建模型时直接将输入和输出数组（而不是输入和输出张量）传递给了 Model 类：
model = Model(inputs=[train_x_1,train_x_2], outputs=train_y_class)相反，你需要传递相应的输入和输出张量，如下所示：
model = Model(inputs=[first_input,second_input], outputs=merge_activation2)