在使用深度学习的多类多标签分类问题中，损失函数的计算过程是什么？

def MinMaxScaler(data): numerator = data - np.min(data) denominator = np.max(data) - np.min(data) return numerator / (denominator + 1e-5) kki = pd.read_csv(filename,names=['UE0','UE1','UE2','UE3','selected_UE0','selected_UE1','selected_UE2','selected_UE3']) print(kki) def LoadData(file): xy = np.loadtxt(file, delimiter=',', dtype=np.float32) print("Data set length:", len(xy)) tr_set_size = int(len(xy) * 0.6) xy[:, 0:-number_of_UEs] = MinMaxScaler(xy[:, 0:-number_of_UEs]) #number_of_UES : 4 X_train = xy[:tr_set_size, 0: -number_of_UEs] #6000 row y_train = xy[:tr_set_size, number_of_UEs:number_of_UEs*2] X_valid = xy[tr_set_size:int((tr_set_size/3) + tr_set_size), 0:-number_of_UEs] y_valid = xy[tr_set_size:int((tr_set_size/3) + tr_set_size), number_of_UEs:number_of_UEs *2] X_test = xy[int((tr_set_size/3) + tr_set_size):, 0:-number_of_UEs] y_test = xy[int((tr_set_size/3) + tr_set_size):, number_of_UEs:number_of_UEs*2] print("Training X shape:", X_train.shape) print("Training Y shape:", y_train.shape) print("validation x shape:", X_valid.shape) print("validation y shape:", y_valid.shape) print("Test X shape:", X_test.shape) print("Test Y shape:", y_test.shape) return X_train, y_train, X_valid, y_valid, X_test, y_test, tr_set_size X_train, y_train, X_valid, y_valid, X_test, y_test, tr_set_size = LoadData(filename) model = Sequential() model.add(Dense(64,activation='relu', input_shape=(X_train.shape[1],))) model.add(Dense(46, activation='relu')) model.add(Dense(24, activation='relu')) model.add(Dense(12, activation='relu')) model.add(Dense(4, activation= 'sigmoid')) model.compile( loss ='binary_crossentropy', optimizer='adam', metrics=['accuracy']) hist = model.fit(X_train, y_train, epochs=5, batch_size=1, verbose= 1, validation_data=(X_valid, y_valid), callbacks= es)

Epoch 1/10 6000/6000 [==============================] - 14s 2ms/step - loss: 0.2999 - accuracy: 0.5345 - val_loss: 0.1691 - val_accuracy: 0.5465 Epoch 2/10 6000/6000 [==============================] - 14s 2ms/step - loss: 0.1554 - accuracy: 0.4883 - val_loss: 0.1228 - val_accuracy: 0.4710 Epoch 3/10 6000/6000 [==============================] - 14s 2ms/step - loss: 0.1259 - accuracy: 0.4710 - val_loss: 0.0893 - val_accuracy: 0.4910 Epoch 4/10 6000/6000 [==============================] - 13s 2ms/step - loss: 0.1094 - accuracy: 0.4990 - val_loss: 0.0918 - val_accuracy: 0.5540 Epoch 5/10 6000/6000 [==============================] - 13s 2ms/step - loss: 0.0967 - accuracy: 0.5223 - val_loss: 0.0671 - val_accuracy: 0.5405 Epoch 6/10 6000/6000 [==============================] - 13s 2ms/step - loss: 0.0910 - accuracy: 0.5198 - val_loss: 0.0836 - val_accuracy: 0.5380 Epoch 7/10 6000/6000 [==============================] - 13s 2ms/step - loss: 0.0870 - accuracy: 0.5348 - val_loss: 0.0853 - val_accuracy: 0.5775 Epoch 8/10 6000/6000 [==============================] - 13s 2ms/step - loss: 0.0859 - accuracy: 0.5518 - val_loss: 0.0515 - val_accuracy: 0.6520 Epoch 9/10 6000/6000 [==============================] - 13s 2ms/step - loss: 0.0792 - accuracy: 0.5508 - val_loss: 0.0629 - val_accuracy: 0.4350 Epoch 10/10 6000/6000 [==============================] - 13s 2ms/step - loss: 0.0793 - accuracy: 0.5638 - val_loss: 0.0632 - val_accuracy: 0.6270

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1楼 · 发布于 2024-04-20 16:02:07

错误1- y_train、y_validation和y_test的形状应分别为(6000,)、(2000,)和(2000,)

错误2- 对于多类别分类，损失应为categorical_crossentropy，激活应为softmax。所以，更改这两行，如下所示：

model.add(Dense(4, activation= 'softmax'))

model.compile(loss ='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

建议- 为什么要自己拆分数据？使用scikit学习train_test_split。此代码将为您提供适当的拆分：

from sklearn.model_selection import train_test_split

x, x_test, y, y_test = train_test_split(xtrain, labels, test_size=0.2, train_size=0.8)
x_train, x_validation, y_train, y_validation = train_test_split(x, y, test_size = 0.25, train_size =0.75)

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