检查模型输入时出错:预期 lstm_1_input 有 3 个维度，但得到了具有形状的数组 (339732, 29) [英] Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29)

问题描述

我的输入只是一个包含 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 模型上训练我的数据:

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:

回溯(最近一次调用最后一次):文件first_approach.py​​"，第 80 行，在模型.fit(X_train，y_train，batch_size = 400，epochs = 20，verbose = 1)

Traceback (most recent call last): File "first_approach.py", line 80, 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 到有 3 个维度，但得到了形状为 (339732, 29) 的数组

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，但它没有工作，显示错误:

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

ValueError: 检查模型输入时出错:预期 lstm_1_input 到有形状 (None, 8, 29) 但得到形状为 (1, 339732, 29) 的数组

ValueError: Error when checking model input: expected lstm_1_input to have shape (None, 8, 29) but got array with shape (1, 339732, 29)

如何将我的输入提供给 LSTM?

How can I feed in my input to the LSTM ?

推荐答案

设置 timesteps = 1(因为我想要每个实例一个时间步长)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]))

这奏效了！

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