Keras LSTM输入功能和不正确的三维数据输入
所以我想练习如何在Keras和所有参数(样本,时间步长,功能)使用LSTMs。 3D列表令我困惑。Keras LSTM输入功能和不正确的三维数据输入
因此,我有一些股票数据,如果列表中的下一个项目高于5的门槛值+2.50,它会购买或出售,如果它处于该阈值的中间,则这些是我的标签:我的Y.
对于我的特征我的XI具有[500,1,3]为我的500个样本的数据帧和每个时步为1,因为每个数据为3个特征1小时增量和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)
我该如何解决这段代码,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。
回答:
这是我的第一篇文章在这里,我想这可能是有用的,我会尽我的最佳
首先,你需要创建3维数组与keras input_shape工作,你可以在keras文档或者观看这个一个更好的办法: 从keras.models导入顺序 顺序? 线性叠层。
参数layers: list of layers to add to the model.
#注意 传递到顺序模型 第一层应具有定义的输入形状。 意味着它应该收到一个input_shape
或batch_input_shape
变元, 或某些类型的层(经常性,密集...) 和input_dim
变元。
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))
这之后如何改变阵列在3 dimmension 2名维 检查np.newaxis
有用的命令可以帮助你,比你预期:
- 顺序?, -Sequential ??, -print(清单(目录(顺序)))
最好
以上是 Keras LSTM输入功能和不正确的三维数据输入 的全部内容, 来源链接: utcz.com/qa/262768.html