Python Keras损失值不变
我正在尝试对贷款状态数据集应用一个深度学习网络，以检查我是否能够获得比传统机器学习算法更好的结果 准确率似乎很低（甚至低于使用正常逻辑回归）。我怎样才能改进它 我尝试过的事情： -改变学习速度 -增加层数 -增加/减少节点数**Python Keras损失值不变,python,keras,Python,Keras,我正在尝试对贷款状态数据集应用一个深度学习网络，以检查我是否能够获得比传统机器学习算法更好的结果 准确率似乎很低（甚至低于使用正常逻辑回归）。我怎样才能改进它 我尝试过的事情： -改变学习速度 -增加层数 -增加/减少节点数** X = df_dummies.drop('Loan_Status', axis=1).values y = df_dummies['Loan_Status'].values model = Sequential() model.add(Dense(50, input_
X = df_dummies.drop('Loan_Status', axis=1).values
y = df_dummies['Loan_Status'].values
model = Sequential()
model.add(Dense(50, input_dim = 17, activation = 'relu'))
model.add(Dense(100, activation = 'relu'))
model.add(Dense(100, activation = 'relu'))
model.add(Dense(100, activation = 'relu'))
model.add(Dense(100, activation = 'relu'))
model.add(Dense(1, activation = 'sigmoid'))
sgd = optimizers.SGD(lr = 0.00001)
model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=`['accuracy'])`
model.fit(X, y, epochs = 50, shuffle=True, verbose=2)
model.summary()
Layer (type) Output Shape Param #
=================================================================
dense_19 (Dense) (None, 50) 900
_________________________________________________________________
dense_20 (Dense) (None, 100) 5100
_________________________________________________________________
dense_21 (Dense) (None, 100) 10100
_________________________________________________________________
dense_22 (Dense) (None, 100) 10100
_________________________________________________________________
dense_23 (Dense) (None, 100) 10100
_________________________________________________________________
dense_24 (Dense) (None, 1) 101
=================================================================
Total params: 36,401
Trainable params: 36,401
Non-trainable params: 0
_________________________________________________________________
**通过加深网络和增加辍学率，我得到了一点改进，但我仍然认为这可以进一步改进，因为使用正态逻辑回归可以提供更好的准确性（80%+） 有人知道进一步改进的方法吗**
model = Sequential()
model.add(Dense(1000, input_dim = 17, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(1000, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(1000, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(1000, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(1000, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(1000, activation = 'relu'))
model.add(Dense(1, activation = 'sigmoid'))
sgd = optimizers.SGD(lr = 0.0001)
model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs = 20, shuffle=True, verbose=2, batch_size=30)
Epoch 1/20
- 2s - loss: 4.8965 - acc: 0.6807
Epoch 2/20
- 1s - loss: 4.6824 - acc: 0.7063
Epoch 3/20
- 1s - loss: 4.6091 - acc: 0.7040
Epoch 4/20
- 1s - loss: 4.5642 - acc: 0.7040
Epoch 5/20
- 1s - loss: 4.6937 - acc: 0.7040
Epoch 6/20
- 1s - loss: 4.6830 - acc: 0.7063
Epoch 7/20
- 1s - loss: 4.6824 - acc: 0.7063
Epoch 8/20
- 1s - loss: 4.6824 - acc: 0.7063
Epoch 9/20
- 1s - loss: 4.6824 - acc: 0.7063
Epoch 10/20
- 1s - loss: 4.6452 - acc: 0.7086
Epoch 11/20
- 1s - loss: 4.6824 - acc: 0.7063
Epoch 12/20
- 1s - loss: 4.6824 - acc: 0.7063
Epoch 13/20
- 1s - loss: 4.7200 - acc: 0.7040
Epoch 14/20
- 1s - loss: 4.6608 - acc: 0.7063
Epoch 15/20
- 1s - loss: 4.6940 - acc: 0.7040
Epoch 16/20
- 1s - loss: 4.7136 - acc: 0.7040
Epoch 17/20
- 1s - loss: 4.6056 - acc: 0.7063
Epoch 18/20
- 1s - loss: 4.5640 - acc: 0.7016
Epoch 19/20
- 1s - loss: 4.7009 - acc: 0.7040
Epoch 20/20
- 1s - loss: 4.6892 - acc: 0.7040