Keras中时间一维卷积的错误输入形状

Question

关于输入形状-已经使用LSTM一段时间了，没有任何问题，但现在我尝试1D卷积层来加快处理速度，现在我遇到了麻烦-你能看到下面的问题是什么吗(此处使用的虚拟数据）

我得到一个拟合错误：

ValueError: Error when checking target: expected dense_17 to have 2 dimensions, but got array with shape (400, 20, 2)

我看不出这里怎么了

代码如下所示

#load packages
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, GRU, 
TimeDistributed
from keras.layers import Conv1D, MaxPooling1D, Flatten, 
GlobalAveragePooling1D
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils

nfeat, kernel, timeStep, length, fs = 36, 8, 20, 100, 100

#data (dummy)
data = np.random.rand(length*fs,nfeat)
classes = 0*data[:,0]
classes[:int(length/2*fs)] = 1

#make correct input shape (batch, timestep, feature)
X = np.asarray([data[i*timeStep:(i + 1)*timeStep,:] for i in 
range(0,length * fs // timeStep)])
#classes
Y = np.asarray([classes[i*timeStep:(i + 1)*timeStep] for i in 
range(0,length * fs // timeStep)])

#split into training and test set
from sklearn.model_selection import train_test_split
trainX, testX, trainY, testY = 
train_test_split(X,Y,test_size=0.2,random_state=0)

#one-hot-encoding
trainY_OHC = np_utils.to_categorical(trainY)
trainY_OHC.shape, trainX.shape

#set up model with simple 1D convnet
model = Sequential()
model.add(Conv1D(8,10,activation=’relu’,input_shape=(timeStep,nfeat)))
model.add(MaxPooling1D(3))
model.add(Flatten())
model.add(Dense(10,activation=’tanh’))
model.add(Dense(2,activation=’softmax’))

model.summary()

#compile model
model.compile(loss=’mse’,optimizer=’Adam’ ,metrics=[‘accuracy’])

#train model

 model.fit(trainX,trainY_OHC,epochs=5,batch_size=4,
 validation_split=0.2)