我遇到了以下错误： ValueError: Input 0 is incompatible with layer lstm_1: expected ndim=3, found ndim=2，这是我使用的代码：
def make_model(): model = Sequential() model.add(Conv2D(20,(5,5), input_shape = (24,48,30), activation = "relu", strides = 1, padding = "valid")) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(50, (5,5), use_bias = 50)) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Flatten()) model.add(Dense(20, activation = "relu")) model.add(LSTM(50, activation="relu", return_sequences=True)) return model我的输入是30个大小为24*48的矩阵。
回答：
问题在于最后一个Dense层（LSTM层之前）的输出形状为(?, 20)，而LSTM层期望的是一个3D张量，而不是2D张量。因此，您可以在输入到LSTM层之前扩展维度。
您可以使用tf.expand_dims来扩展维度（假设您使用TensorFlow作为后端） tf expand
input_layer = Input((30,24,48))model = Conv2D(20,(5,5), input_shape = (30,24,48), activation = "relu", strides = 1, padding = "valid")(input_layer)model = MaxPooling2D(pool_size=(2,2))(model) model = Conv2D(50, (5,5), use_bias = 50)(model) model = MaxPooling2D(pool_size=(2,2))(model) model = Flatten()(model)model = Dense(20, activation = "relu")(model)model = tf.expand_dims(model, axis=-1)model = LSTM(50, activation="relu", return_sequences=True)(model)（我没有使用Sequential模式，我使用的是函数式API，因为它更灵活）
如果您想使用Sequential模型：
model = Sequential() model.add(Conv2D(20,(5,5), input_shape = (30, 24, 48), activation = "relu", strides = 1, padding = "valid")) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(50, (5,5), use_bias = 50)) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Flatten()) model.add(Dense(20, activation = "relu")) model.add(Lambda(lambda x: tf.expand_dims(model.output, axis=-1))) model.add(LSTM(50, activation="relu", return_sequences=True))您必须在Lambda中使用expand dims
