Keras神经网络的精度在训练期间始终为0

问题描述 投票:0回答:1

我正在使用keras神经网络进行简单的分类算法。目标是获取有关天气的3个数据点,并确定是否有野火。这是我用来训练模型的.csv数据集的图像(此图像只是最上面的几行,并不是全部):wildfire weather dataset如您所见,有4列,第四列是“ 1”表示“ fire”或“ 0”表示“ no fire”。我希望算法预测1或0。这是我编写的代码:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import csv


#THIS IS USED TO TRAIN THE MODEL
# Importing the dataset
dataset = pd.read_csv('Fire_Weather.csv')
dataset.head()

X=dataset.iloc[:,0:3]
Y=dataset.iloc[:,3]

X.head()
obj=StandardScaler()
X=obj.fit_transform(X)

X_train,X_test,y_train,y_test=train_test_split(X, Y, test_size=0.25)


print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)


classifier = Sequential()

    # Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 
                                                      'relu', input_dim = 3))
   # classifier.add(Dropout(p = 0.1))

   # Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation 
                                                                   = 'relu'))
   # classifier.add(Dropout(p = 0.1))

   # Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation 
                                                               = 'sigmoid'))

       # Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics 
                                                          = ['accuracy'])

classifier.fit(X_train, y_train, batch_size = 3, epochs = 10)
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
print(y_pred)

classifier.save("weather_model.h5")

问题是,每当我运行此命令时,我的精度始终为“ 0.0000e + 00”,并且我的训练输出如下所示:

    Epoch 1/10
2146/2146 [==============================] - 2s 758us/step - loss: nan - accuracy: 0.0238
Epoch 2/10
2146/2146 [==============================] - 1s 625us/step - loss: nan - accuracy: 0.0000e+00
Epoch 3/10
2146/2146 [==============================] - 1s 604us/step - loss: nan - accuracy: 0.0000e+00
Epoch 4/10
2146/2146 [==============================] - 1s 609us/step - loss: nan - accuracy: 0.0000e+00
Epoch 5/10
2146/2146 [==============================] - 1s 624us/step - loss: nan - accuracy: 0.0000e+00
Epoch 6/10
2146/2146 [==============================] - 1s 633us/step - loss: nan - accuracy: 0.0000e+00
Epoch 7/10
2146/2146 [==============================] - 1s 481us/step - loss: nan - accuracy: 0.0000e+00
Epoch 8/10
2146/2146 [==============================] - 1s 476us/step - loss: nan - accuracy: 0.0000e+00
Epoch 9/10
2146/2146 [==============================] - 1s 474us/step - loss: nan - accuracy: 0.0000e+00
Epoch 10/10
2146/2146 [==============================] - 1s 474us/step - loss: nan - accuracy: 0.0000e+00

有人知道为什么会这样吗,我可以对我的代码做些什么来解决这个问题?谢谢!

python tensorflow machine-learning keras neural-network
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