Keras LSTM:检查模型输入尺寸时出错 [英] Keras LSTM: Error when checking model input dimension

问题描述

我是keras的新用户，并尝试实现LSTM模型.为了进行测试，我按如下所示声明了该模型，但是由于输入尺寸的差异，该模型失败了.尽管我在该站点中发现了类似的问题，但我自己找不到错误.

I am a new user of keras, and trying to implement a LSTM model. For test I declared the model like below, but it fails because of difference of input dimension. Although I found similar problems in this site, I could not find my mistakes by myself.

ValueError: 
Error when checking model input: 
expected lstm_input_4 to have 3 dimensions, but got array with shape (300, 100)

我的环境

• python 3.5.2
• keras 1.2.0(Theano)

My environment

• python 3.5.2
• keras 1.2.0 (Theano)

from keras.layers import Input, Dense
from keras.models import Sequential
from keras.layers import LSTM
from keras.optimizers import RMSprop, Adadelta
from keras.layers.wrappers import TimeDistributed
import numpy as np

in_size = 100
out_size = 10
nb_hidden = 8

model = Sequential()
model.add(LSTM(nb_hidden, 
               name='lstm',
               activation='tanh',
               return_sequences=True,
               input_shape=(None, in_size)))
model.add(TimeDistributed(Dense(out_size, activation='softmax')))

adadelta = Adadelta(clipnorm=1.)
model.compile(optimizer=adadelta,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# create dummy data
data_size = 300
train = np.zeros((data_size, in_size,), dtype=np.float32)
labels = np.zeros((data_size, out_size,), dtype=np.float32)
model.fit(train, labels)

编辑1(在MarcinMożejko发表评论后无效)

谢谢MarcinMożejko.但是我有一个类似下面的错误.我更新了虚拟数据进行检查.该代码有什么问题?

Edit 1 (not working, after Marcin Możejko's comment)

Thank you Marcin Możejko. But I have a similar error like below. I updated dummy data for check. What is wrong of this code?

ValueError:检查模型目标时出错:预期 timedistributed_36具有3个维度，但具有形状的数组 (208，1)

ValueError: Error when checking model target: expected timedistributed_36 to have 3 dimensions, but got array with shape (208, 1)

def create_dataset(X, Y, loop_back=1):
    dataX, dataY = [], []
    for i in range(len(X) - loop_back-1):
        a = X[i:(i+loop_back), :]
        dataX.append(a)
        dataY.append(Y[i+loop_back, :])
    return np.array(dataX), np.array(dataY)

data_size = 300
dataset = np.zeros((data_size, feature_size), dtype=np.float32)
dataset_labels = np.zeros((data_size, 1), dtype=np.float32)

train_size = int(data_size * 0.7)
trainX = dataset[0:train_size, :]
trainY = dataset_labels[0:train_size, :]
testX = dataset[train_size:, :]
testY = dataset_labels[train_size:, 0]
trainX, trainY = create_dataset(trainX, trainY)
print(trainX.shape, trainY.shape) # (208, 1, 1) (208, 1)

# in_size = 100
feature_size = 1
out_size = 1
nb_hidden = 8

model = Sequential()
model.add(LSTM(nb_hidden, 
               name='lstm',
               activation='tanh',
               return_sequences=True,
               input_shape=(1, feature_size)))

model.add(TimeDistributed(Dense(out_size, activation='softmax')))
adadelta = Adadelta(clipnorm=1.)
model.compile(optimizer=adadelta,
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(trainX, trainY, nb_epoch=10, batch_size=1)

推荐答案

这是Keras中LSTM的一个非常经典的问题. LSTM输入形状应为2d-形状为(sequence_length, nb_of_features).额外的第三个维度来自示例维度-因此，馈送给模型的表格的形状为(nb_of_examples, sequence_length, nb_of_features).这就是您的问题出处.请记住，1-d序列应显示为形状为(sequence_length, 1)的2-d数组.这应该是您的LSTM的输入形状:

This is a really classic problem with LSTM in Keras. LSTM input shape should be 2d - with shape (sequence_length, nb_of_features). Additional third dimension comes from examples dimension - so the table fed to model has shape (nb_of_examples, sequence_length, nb_of_features). This is where your problem comes from. Remember that a 1-d sequence should be presented as a 2-d array with shape (sequence_length, 1). This should be a input shape of your LSTM:

model.add(LSTM(nb_hidden, 
           name='lstm',
           activation='tanh',
           return_sequences=True,
           input_shape=(in_size, 1)))

请记住，将输入的内容reshape设置为适当的格式.

And remember to reshape your input to appropriate format.

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