我遇到了这个错误：

ValueError: 检查输入时出错：期望序列有3个维度，但得到形状为(500, 400)的数组

这些是我使用的代码：

print(X1_Train.shape)print(X2_Train.shape)print(y_train.shape)

输出（这里每个都有500行）：

(500, 400)(500, 1500)(500,)400 => 时间步长（如下）1500 => n（如下）

代码：

timesteps = 50 * 8n = 50 * 30def createClassifier():    sequence = Input(shape=(timesteps, 1), name='Sequence')    features = Input(shape=(n,), name='Features')    conv = Sequential()    conv.add(Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))    conv.add(Conv1D(10, 5, activation='relu'))    conv.add(MaxPool1D(2))    conv.add(Dropout(0.5))    conv.add(Conv1D(5, 6, activation='relu'))    conv.add(Conv1D(5, 6, activation='relu'))    conv.add(MaxPool1D(2))    conv.add(Dropout(0.5))    conv.add(Flatten())    part1 = conv(sequence)    merged = concatenate([part1, features])    final = Dense(512, activation='relu')(merged)    final = Dropout(0.5)(final)    final = Dense(num_class, activation='softmax')(final)    model = Model(inputs=[sequence, features], outputs=[final])    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])    return modelmodel = createClassifier()# print(model.summary())history = model.fit([X1_Train, X2_Train], y_train, epochs =5)

有什么见解吗？

回答：

两点 –

Conv1D层期望输入的形状为(batch_size, x, filters)，在你的案例中为(500,400,1)。
你需要重塑你的输入层，添加一个大小为1的轴。（这不会改变你的数据）。

你试图使用多个输入，Sequential API不是最佳选择。我建议使用函数式API

编辑：关于你的评论，不确定你哪里做错了，但这是你的代码（使用假数据）的工作版本，包含了重塑：

import kerasimport numpy as npX1_Train = np.ones((500,400))X2_Train = np.ones((500,1500))y_train = np.ones((500))print(X1_Train.shape)print(X2_Train.shape)print(y_train.shape)num_class = 1timesteps = 50 * 8n = 50 * 30def createClassifier():    sequence = keras.layers.Input(shape=(timesteps, 1), name='Sequence')    features = keras.layers.Input(shape=(n,), name='Features')    conv = keras.Sequential()    conv.add(keras.layers.Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))    conv.add(keras.layers.Conv1D(10, 5, activation='relu'))    conv.add(keras.layers.MaxPool1D(2))    conv.add(keras.layers.Dropout(0.5))    conv.add(keras.layers.Conv1D(5, 6, activation='relu'))    conv.add(keras.layers.Conv1D(5, 6, activation='relu'))    conv.add(keras.layers.MaxPool1D(2))    conv.add(keras.layers.Dropout(0.5))    conv.add(keras.layers.Flatten())    part1 = conv(sequence)    merged = keras.layers.concatenate([part1, features])    final = keras.layers.Dense(512, activation='relu')(merged)    final = keras.layers.Dropout(0.5)(final)    final = keras.layers.Dense(num_class, activation='softmax')(final)    model = keras.Model(inputs=[sequence, features], outputs=[final])    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])    return modelmodel = createClassifier()# print(model.summary())X1_Train = X1_Train.reshape((500,400,1))history = model.fit([X1_Train, X2_Train], y_train, epochs =5)

输出为：

Using TensorFlow backend.(500, 400)(500, 1500)(500,)Epoch 1/5500/500 [==============================] - 1s 3ms/step - loss: 1.1921e-07 - acc: 1.0000Epoch 2/5500/500 [==============================] - 0s 160us/step - loss: 1.1921e-07 - acc: 1.0000Epoch 3/5500/500 [==============================] - 0s 166us/step - loss: 1.1921e-07 - acc: 1.0000Epoch 4/5500/500 [==============================] - 0s 154us/step - loss: 1.1921e-07 - acc: 1.0000Epoch 5/5500/500 [==============================] - 0s 157us/step - loss: 1.1921e-07 - acc: 1.0000