对于使用LSTM，keras模型的分类，我得到loss = nan和accuracy =0
对于使用LSTM，keras模型的分类，我得到loss = nan和accuracy =0
提问于 2020-11-18 09:39:38
我正在尝试使用lstm进行运动分类。这是我的模型
def evaluate_model(trainX, trainy, testX, testy):
verbose, epochs, batch_size = 0, 10, 32
n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
model = Sequential()
model.add(LSTM(32, input_shape=(n_timesteps,n_features)))
# model.add(Dropout(0.5))
# model.add(Dense(32, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
loss, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
return loss, accuracy
for r in range(repeats):
loss, score = evaluate_model(trainx, trainy, testx, testy)
score = score * 100.0
print('>#%d: %.3f' % (r+1, score))
print('>#%d: %.3f' % (r+1, loss))这是我的输出
>#1: 0.000
>#1: nan
>#2: 0.000
>#2: nan
>#3: 0.000
>#3: nan
>#4: 0.000
>#4: nan
>#5: 0.000
>#5: nan
>#6: 0.000
>#6: nan
>#7: 0.000
>#7: nan
>#8: 0.000
>#8: nan
>#9: 0.000
>#9: nan
>#10: 0.000
>#10: nan
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Accuracy: 0.000% (+/-0.000)我哪里错了？我见过一些回归模型得到nan损失，但我使用的是分类模型。是因为我的数据吗？
回答 1
Stack Overflow用户
发布于 2020-11-18 16:56:54
检查您的数据。
你的模型能很好地处理随机数据：
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import LSTM, Dense
def evaluate_model(trainX, trainy, testX, testy):
verbose, epochs, batch_size = 0, 10, 32
n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
model = Sequential()
model.add(LSTM(32, input_shape=(n_timesteps,n_features)))
model.add(Dense(n_outputs, activation='softmax'))
#model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
loss, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
return loss, accuracy
for r in range(5):
trainx = tf.random.uniform([10, 10, 10])
trainy = tf.random.uniform([10, 10])
testx = tf.random.uniform([10, 10, 10])
testy = tf.random.uniform([10, 10])
loss, score = evaluate_model(trainx, trainy, testx, testy)
score = score * 100.0
print('>#%d: %.3f' % (r+1, score))
print('>#%d: %.3f' % (r+1, loss))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/64885786
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