我的输入只是一个 csv 文件237124行和37列 :
首先36列作为特征
The last列是一个二进制类标签
我正在尝试在 conv1D 模型上训练我的数据。
我尝试过建立一个一层 CNN,但我有一些问题。
编译器输出:
ValueError:检查输入时出错:预期 conv1d_9_input 具有形状
(213412, 36) 但得到形状为 (36, 1) 的数组
Code:
import pandas as pd
import numpy as np
import sklearn
from sklearn import metrics
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.layers import Conv2D,Conv1D, MaxPooling2D,MaxPooling1D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dropout,BatchNormalization
dataset=pd.read_csv("C:/Users/User/Desktop/data.csv",encoding='cp1252')
dataset.shape
#output: (237124, 37)
array = dataset.values
X = array[:,0:36]
Y = array[:,36]
kf = KFold(n_splits=10)
kf.get_n_splits(X)
for trainindex, testindex in kf.split(X):
Xtrain, Xtest = X[trainindex], X[testindex]
Ytrain, Ytest = Y[trainindex], Y[testindex]
Xtrain.shape[0]
#output: 213412
Xtrain.shape[1]
#output: 36
Ytrain.shape[0]
#output: 213412
n_timesteps, n_features, n_outputs =Xtrain.shape[0], Xtrain.shape[1],
Ytrain.shape[0]
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=1,
activation='relu',input_shape=(n_timesteps,n_features)))
model.add(Conv1D(filters=64, kernel_size=1, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=
['accuracy'])
# fit network
model.fit(Xtrain, Ytrain, epochs=10, batch_size=32, verbose=0)
# Testing CNN model BY X test
Predictions = model.predict(Xtest,batch_size =100)
rounded = [round(x[0]) for x in Predictions]
Y_predection = pd.DataFrame(rounded)
Y_predection = Y_predection.iloc[:, 0]
.
.
.
我尝试这样修改代码:
Xtrain = np.expand_dims(Xtrain, axis=2)
但错误仍然是一样的。