keras中LSTM的形状不匹配

Question

我想在我的自定义功能集上运行使用Keras的LSTM。我已经在单独的文件中训练和测试功能。每个csv文件包含11列，最后一列作为类标签。我的数据集中共有40个课程。问题是我无法找出正确的input_shape到第一层。我已经探索了所有的stackoverflow和github，但仍然无法解决这个问题 下面是我的完整代码。

import numpy 
from keras.models import Sequential 
from keras.layers import Dense 
from keras.layers import LSTM 

numpy.random.seed(7) 

train_dataset = numpy.loadtxt("train.csv", delimiter=",") 
X_train = train_dataset[:, 0:10] 
y_train = train_dataset[:, 10] 

test_dataset = numpy.loadtxt("test.csv", delimiter=",") 
X_test = test_dataset[:, 0:10] 
y_test = test_dataset[:, 10] 

model = Sequential() 

model.add(LSTM(32, return_sequences=True, input_shape=X_train.shape)) 
model.add(LSTM(32, return_sequences=True)) 
model.add(LSTM(32)) 
model.add(Dense(1, activation='softmax')) 
model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy']) 
model.fit(X_train, y_train, batch_size=10, epochs=1) 

score, acc = model.evaluate(X_test, y_test, batch_size=10) 
print('Test score:', score) 
print('Test accuracy:', acc * 100) 

无论我在input_shape参数中发生什么变化，我都会在fit方法的第一个LSTM层中出错。

Answer 1

您在输入中没有时间维度。 RNN的输入应为(batch_size, time_step, features)，而输入的维数为(batch_size, features)。

如果你想用你的10列一次一个，你应该重塑阵列 numpy.reshape(train_dataset, (-1, train_dataset.shape[1], 1))

试试这个代码：

train_dataset = numpy.loadtxt("train.csv", delimiter=",") 
train_dataset = numpy.reshape(train_dataset, (-1, train_dataset.shape[1], 1)) 

X_train = train_dataset[:, 0:10] 
y_train = train_dataset[:, 10] 

test_dataset = numpy.loadtxt("test.csv", delimiter=",") 
test_dataset = numpy.reshape(test_dataset, (-1, train_dataset.shape[1], 1)) 

X_test = test_dataset[:, 0:10] 
y_test = test_dataset[:, 10] 

model = Sequential() 

model.add(LSTM(32, return_sequences=True, input_shape=(X_train.shape[1], 1))) 
model.add(LSTM(32, return_sequences=True)) 
model.add(LSTM(32)) 
model.add(Dense(1, activation='softmax')) 
model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy']) 
model.fit(X_train, y_train, batch_size=10, epochs=1) 

score, acc = model.evaluate(X_test, y_test, batch_size=10) 
print('Test score:', score) 
print('Test accuracy:', acc * 100)