假设我有两个特征：x1和x2。这里，x1是一个词索引向量，x2是一个数值向量。x1和x2的长度都等于50。每组x1和x2都有6000行。我将这两个特征组合成一个，如下所示：
X = np.array([np.row_stack((x1[i], x2[i])) for i in range(x1.shape[0])])我的初始LSTM模型是这样的：
X_input = Input(shape = (50, 2), name = "X_seq")X_hidden1 = LSTM(units = 256, dropout = 0.25, return_sequences = True)(X_input)X_hidden2 = LSTM(units = 256, dropout = 0.25, return_sequences = True)(X_hidden1)X_hidden3 = LSTM(units = 128, dropout = 0.25)(X_hidden2)X_dense = Dense(units = 128, activation = 'relu')(X_hidden3)X_dense_dropout = Dropout(0.25)(X_dense)concat = tf.keras.layers.concatenate(inputs = [X_dense_dropout])output = Dense(units = num_category, activation = 'softmax', name = "output")(concat)model = tf.keras.Model(inputs = [X_input], outputs = [output])model.compile(optimizer = 'adam', loss = "sparse_categorical_crossentropy", metrics = ["accuracy"])然而，我知道我需要在输入层下方添加一个嵌入层来处理X[0,:]。因此，我修改了上面的代码如下：
X_input = Input(shape = (50, 2), name = "X_seq")x1_embedding = Embedding(input_dim = max_pages, output_dim = embedding_dim, input_length = max_length)(X_input[0,:])X_concat = tf.keras.layers.concatenate(inputs = [x1_embedding, X_input[1,:]])X_hidden1 = LSTM(units = 256, dropout = 0.25, return_sequences = True)(X_concat)X_hidden2 = LSTM(units = 256, dropout = 0.25, return_sequences = True)(X_hidden1)X_hidden3 = LSTM(units = 128, dropout = 0.25)(X_hidden2)X_dense = Dense(units = 128, activation = 'relu')(X_hidden3)X_dense_dropout = Dropout(0.25)(X_dense)concat = tf.keras.layers.concatenate(inputs = [X_dense_dropout])output = Dense(units = num_category, activation = 'softmax', name = "output")(concat)model = tf.keras.Model(inputs = [X_input], outputs = [output])model.compile(optimizer = 'adam', loss = "sparse_categorical_crossentropy", metrics = ["accuracy"])Python显示了一个错误：
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 2, 15), (None, 2)]有什么建议吗？非常感谢
回答：
问题在于concat层的输入具有不同的维度，因此我们无法将它们concat。为了解决这个问题，我们可以使用tf.keras.layers.Reshape来重塑concat层的输入，如下所示，其余部分保持不变。
reshaped_input = tf.keras.layers.Reshape((-1,1))(X_input[:, 1])X_concat = tf.keras.layers.concatenate(inputs = [x1_embedding, reshaped_input])