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Я пытаюсь предсказать базовую модель цен на акции. Вот мой код

 data = pd.read_csv("total_cases.csv")
x = data["date"]
world_cases = data["Turkey"].fillna(0)
time = np.arange(len(world_cases), dtype="float32")

split_time = 200
x_train = time[:split_time]
x_valid = time[split_time:]

y_train = world_cases[:split_time]
y_valid = world_cases[split_time:]
window_size = 20
batch_size = 32
shuffle_buffer_size=1000
train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))
valid_data = tf.data.Dataset.from_tensor_slices((x_valid, y_valid))
model = Sequential()
model.add(LSTM(16, return_sequences=True))

model.add(LSTM(16))

model.add(Dense(16, activation='relu'))
model.compile(optimizer='adam', loss='mae', metrics=['mae'])

r = model.fit(train_data, validation_data=valid_data, epochs=100)
  

При запуске модели возникла ошибка

 ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=3, found ndim=0. Full shape received: []
  

Редактировать
Вот часть csv-файла, world_cases -Turkey- column

 0           0.0
1           0.0
2           0.0
3           0.0
4           0.0
         ...
258    291162.0
259    292878.0
260    294620.0
261    296391.0
262    298039.0
  

Я воспроизвел вашу проблему.

 import tensorflow as tf
inputs = tf.random.normal([])
lstm = tf.keras.layers.LSTM(4)
output = lstm(inputs)
print(output.shape)
  

Вывод

 ValueError: Input 0 of layer lstm_1 is incompatible with the layer: expected ndim=3, found ndim=0. Full shape received: ()
  

Проблема с формой входных данных

Начиная с Tensorflow.Keras LSTM ожидает ввода формы 3D. Измените свой ввод в соответствии с этим

 inputs: A 3D tensor with shape [batch, timesteps, feature] 
model.add(LSTM(16,return_sequences=False)).
  

Рабочий пример кода

 import tensorflow as tf
inputs = tf.random.normal([32, 10, 8])
lstm = tf.keras.layers.LSTM(4)
output = lstm(inputs)
print(output.shape)
  

Вывод

 (32, 4)