检查模型输入时出错:预期lstm_1_input具有3个维，但数组的形状为(339732，29)

问题描述

我的输入只是一个具有339732行和两列的csv文件:

My input is simply a csv file with 339732 rows and two columns :

• 第一个是29个特征值，即X
• 第二个是二进制标签值，即Y

我正在尝试在堆叠的LSTM模型上训练数据:

I am trying to train my data on a stacked LSTM model:

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:

ValueError:检查模型输入时出错:预期的lstm_1_input为 有3个维度，但数组的形状为(339732，29)

ValueError: Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29)

我尝试使用X_train.reshape((1,339732, 29))重塑我的输入，但显示错误不起作用:

I tried reshaping my input using X_train.reshape((1,339732, 29)) but it did not work showing error:

如何将我的输入输入LSTM?

How can I feed in my input to the LSTM ?

推荐答案

设置timesteps = 1(因为每个实例我要一个时间步长)，并将X_train和X_test重塑为:

Setting timesteps = 1 (since, I want one timestep for each instance) and reshaping the X_train and X_test as:

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]))

这行得通！

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