使用keras训练神经网络分类时的损失

我有一些数据，想分类

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2474 entries, 0 to 5961
Data columns (total 4 columns):
Age                             2474 non-null int64
Pre_Hospitalization_Disposal    2474 non-null object
Injury_to_hospital_time         2474 non-null float64
Discharge_results               2474 non-null int64
dtypes: float64(1), int64(2), object(1)
memory usage: 96.6+ KB

我的代码：

(train, test) = train_test_split(DataSet, test_size=0.2, random_state=42)
trainY = train["Discharge_results"].astype('float')
testY = test["Discharge_results"].astype('float')

cs = MinMaxScaler()
trainContinuous = cs.fit_transform(train[['Age','Injury_to_hospital_time']])
testContinuous = cs.transform(test[['Age','Injury_to_hospital_time']])

zipBinarizer = LabelBinarizer().fit(DataSet["Pre_Hospitalization_Disposal"])
trainCategorical = zipBinarizer.transform(train["Pre_Hospitalization_Disposal"])
testCategorical = zipBinarizer.transform(test["Pre_Hospitalization_Disposal"])

trainX = np.hstack([trainCategorical, trainContinuous])
testX = np.hstack([testCategorical, testContinuous])

model = Sequential()
model.add(Dense(16, input_dim=trainX.shape[1] ,activation="relu"))
model.add(Dense(8, activation="relu"))
model.add(Dense(1, activation="softmax"))
model.compile(loss="sparse_categorical_crossentropy", optimizer='Adam')

history = model.fit(trainX, trainY, validation_data=(testX, testY),epochs=200, batch_size=32)

但我在训练时受到了损失

结果:

Train on 1979 samples, validate on 495 samples
Epoch 1/10
1979/1979 [==============================] - 2s 1ms/step - loss: nan - val_loss: nan
Epoch 2/10
1979/1979 [==============================] - 0s 165us/step - loss: nan - val_loss: nan
Epoch 3/10
1979/1979 [==============================] - 0s 139us/step - loss: nan - val_loss: nan
Epoch 4/10
1979/1979 [==============================] - 0s 137us/step - loss: nan - val_loss: nan
Epoch 5/10
1979/1979 [==============================] - 0s 137us/step - loss: nan - val_loss: nan
Epoch 6/10
1979/1979 [==============================] - 0s 141us/step - loss: nan - val_loss: nan
Epoch 7/10
1979/1979 [==============================] - 0s 138us/step - loss: nan - val_loss: nan
Epoch 8/10
1979/1979 [==============================] - 0s 141us/step - loss: nan - val_loss: nan
Epoch 9/10
1979/1979 [==============================] - 0s 140us/step - loss: nan - val_loss: nan
Epoch 10/10
1979/1979 [==============================] - 0s 144us/step - loss: nan - val_loss: nan

---

有人能帮我吗？非常感谢

看来您的标签和培训损失之间不匹配。损失

稀疏\分类\交叉熵

适用于具有多个类别的分类模型。如果要使用此丢失，则标签应该是整数（正确类别的索引），但我在代码中看到标签是浮动的：

trainY = train["Discharge_results"].astype('float')

此外，模型的最后一个密集层应该有

n_类

隐藏单元，而不是只有1个

如果您的标签确实是浮动的，那么您可能正在处理回归问题，并且应该使用不同的损失函数（例如

均方误差

）

trainY = train["Discharge_results"].astype('float')