我创建了一个用于对灰度视频进行分类的convLSTM，这意味着它们只有一个通道。即使我将通道数定义为1，我仍然会得到以下错误：

ValueError: Error when checking input: expected conv_lst_m2d_1_inputto have 5 dimensions, but got array with shape (128, 176, 256, 256)

128是训练数据集的大小，176*256是每帧的分辨率，256是每个视频中的帧数。

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, shuffle=True, random_state=0) model = Sequential()model.add(ConvLSTM2D(filters = 64, kernel_size = (3, 3), return_sequences = False, data_format = "channels_last", input_shape = (seq_len, img_height, img_width, 1)))model.add(Dropout(0.2))model.add(Flatten())model.add(Dense(256, activation="relu"))model.add(Dropout(0.3))model.add(Dense(6, activation = "softmax")) model.summary() opt = keras.optimizers.SGD(lr=0.001)model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=["accuracy"]) earlystop = EarlyStopping(patience=7)callbacks = [earlystop]history = model.fit(x = X_train, y = y_train, epochs=40, batch_size = 8 , shuffle=True, validation_split=0.2, callbacks=callbacks)

回答：

你只需要扩展数据的最后一个维度即可

batch_dim, seq_len, img_height, img_width = 3, 17, 25, 25X = np.random.uniform(0,1, (batch_dim, seq_len, img_height, img_width))y = np.random.randint(0,6, batch_dim)print(X.shape)# expand input dimensionX = X[...,np.newaxis]print(X.shape)model = Sequential()model.add(ConvLSTM2D(filters = 64, kernel_size = (3, 3), return_sequences = False,                      data_format = "channels_last",                      input_shape = (seq_len, img_height, img_width, 1)))model.add(Dropout(0.2))model.add(Flatten())model.add(Dense(256, activation="relu"))model.add(Dropout(0.3))model.add(Dense(6, activation = "softmax"))model.summary()model.predict(X).shape