我的输入只是一个包含339732行和两列的csv文件:
我正在尝试在堆叠的LSTM模型上训练我的数据:
data_dim = 29
timesteps = 8
num_classes = 2
model = Sequential()
model.add(LSTM(30, return_sequences=True,
input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 30
model.add(LSTM(30, return_sequences=True)) # returns a sequence of vectors of dimension 30
model.add(LSTM(30)) # return a single vector of dimension 30
model.add(Dense(1, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, batch_size = 400, epochs = 20, verbose = 1)
这会引发错误:
追踪(最近一次通话): 文件" first_approach.py",第80行,in model.fit(X_train,y_train,batch_size = 400,epochs = 20,verbose = 1)
ValueError:检查模型输入时出错:预期lstm_1_input为 有3个尺寸,但有阵形(339732,29)
我尝试使用X_train.reshape((1,339732, 29))
重塑我的输入,但它无法显示错误:
ValueError:检查模型输入时出错:预期lstm_1_input为 有形状(无,8,29)但有阵形(1,339732,29)
如何输入我对LSTM的输入?
答案 0 :(得分:20)
设置timesteps = 1
(因为,我希望每个实例有一个时间步长)并将X_train和X_test重新整形为:
import numpy as np
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
这很有用!
答案 1 :(得分:4)
对于timesteps != 1
,您可以使用以下功能(改编自here)
import numpy as np
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back+1):
a = dataset[i:(i+look_back), :]
dataX.append(a)
dataY.append(dataset[i + look_back - 1, :])
return np.array(dataX), np.array(dataY)
实施例
X = np.reshape(range(30),(3,10)).transpose()
array([[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]])
create_dataset(X, look_back=1 )
(array([[[ 0, 10, 20]],
[[ 1, 11, 21]],
[[ 2, 12, 22]],
[[ 3, 13, 23]],
[[ 4, 14, 24]],
[[ 5, 15, 25]],
[[ 6, 16, 26]],
[[ 7, 17, 27]],
[[ 8, 18, 28]],
[[ 9, 19, 29]]]),
array([[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]]))
create_dataset(X, look_back=3)
(array([[[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22]],
[[ 1, 11, 21],
[ 2, 12, 22],
[ 3, 13, 23]],
[[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24]],
[[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25]],
[[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26]],
[[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27]],
[[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28]],
[[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]]]),
array([[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]]))
答案 2 :(得分:2)
为LSTM重塑输入:
X = array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
X_train = X.reshape(1, 3, 3) # X.reshape(samples, timesteps, features)