Keras LSTM 输入特征和不正确的维度数据输入

Question

所以我正在尝试练习如何在 Keras 和所有参数（样本、时间步长、特征）中使用 LSTM。 3D 列表让我很困惑。

所以我有一些股票数据，如果列表中的下一个项目高于 5 的阈值，即 +-2.50，它会买入或卖出，如果它处于它持有的阈值的中间，这些是我的标签：我的Y。

对于我的 X 功能，我的 500 个样本的数据帧为 [500, 1, 3]，每个时间步长为 1，因为每个数据增量为 1 小时，3 个功能为 3。但我得到这个错误：

ValueError: Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (500, 3)

如何修复此代码，我做错了什么？

import json
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

"""
Sample of JSON file
{"time":"2017-01-02T01:56:14.000Z","usd":8.14},
{"time":"2017-01-02T02:56:14.000Z","usd":8.16},
{"time":"2017-01-02T03:56:15.000Z","usd":8.14},
{"time":"2017-01-02T04:56:16.000Z","usd":8.15}
"""
file = open("E.json", "r", encoding="utf8")
file = json.load(file)

"""
If the price jump of the next item is > or < +-2.50 the append 'Buy or 'Sell'
If its in the range of +- 2.50 then append 'Hold'
This si my classifier labels
"""
data = []
for row in range(len(file['data'])):
    row2 = row + 1
    if row2 == len(file['data']):
        break
    else:
        difference = file['data'][row]['usd'] - file['data'][row2]['usd']
        if difference > 2.50:
            data.append((file['data'][row]['usd'], 'SELL'))
        elif difference < -2.50:
            data.append((file['data'][row]['usd'], 'BUY'))
        else:
            data.append((file['data'][row]['usd'], 'HOLD'))

"""
add the price the time step which si 1 and the features which is 3
"""
frame = pd.DataFrame(data)
features = pd.DataFrame()
# train LSTM
for x in range(500):
    series = pd.Series(data=[500, 1, frame.iloc[x][0]])
    features = features.append(series, ignore_index=True)

labels = frame.iloc[16000:16500][1]

# test
#yt = frame.iloc[16500:16512][0]
#xt = pd.get_dummies(frame.iloc[16500:16512][1])


# create LSTM
model = Sequential()
model.add(LSTM(3, input_shape=features.shape, activation='relu', return_sequences=False))
model.add(Dense(2, activation='relu'))
model.add(Dense(1, activation='relu'))

model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])


model.fit(x=features.as_matrix(), y=labels.as_matrix())

"""
ERROR
Anaconda3\envs\Final\python.exe C:/Users/Def/PycharmProjects/Ether/Main.py
Using Theano backend.
Traceback (most recent call last):
  File "C:/Users/Def/PycharmProjects/Ether/Main.py", line 62, in <module>
    model.fit(x=features.as_matrix(), y=labels.as_matrix())
  File "\Anaconda3\envs\Final\lib\site-packages\keras\models.py", line 845, in fit
    initial_epoch=initial_epoch)
  File "\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 1405, in fit
    batch_size=batch_size)
  File "\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 1295, in _standardize_user_data
    exception_prefix='model input')
  File "\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 121, in _standardize_input_data
    str(array.shape))
ValueError: Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (500, 3)
"""

谢谢。

Answer 1

这是我在这里的第一篇文章，希望有用，我会尽力而为

首先，您需要创建 3 维数组以在 keras 中使用 input_shape，您可以在 keras 文档中或以更好的方式观看此内容： 从 keras.models 导入顺序 顺序？ 层的线性堆叠。

参数

layers: list of layers to add to the model.

#注意 第一层传递给顺序模型 应该有一个定义的输入形状。那是什么 意味着它应该收到input_shape 或batch_input_shape 参数， 或者对于某些类型的层（循环，密集......） input_dim 参数。

示例

```python
    model = Sequential()
    # first layer must have a defined input shape
    model.add(Dense(32, input_dim=500))
    # afterwards, Keras does automatic shape inference
    model.add(Dense(32))

    # also possible (equivalent to the above):
    model = Sequential()
    model.add(Dense(32, input_shape=(500,)))
    model.add(Dense(32))

    # also possible (equivalent to the above):
    model = Sequential()
    # here the batch dimension is None,
    # which means any batch size will be accepted by the model.
    model.add(Dense(32, batch_input_shape=(None, 500)))
    model.add(Dense(32))

之后如何在 3 维中转换 2 维数组 检查 np.newaxis

有用的命令可以帮助您超出您的预期：

• 顺序？ -顺序？？， -print(list(dir(Sequential)))

最好的