keras处理欠拟合和过拟合的实例讲解

baseline

import tensorflow.keras.layers as layers
baseline_model = keras.Sequential(
[
layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),
layers.Dense(16, activation='relu'),
layers.Dense(1, activation='sigmoid')
]
)
baseline_model.compile(optimizer='adam',
     loss='binary_crossentropy',
     metrics=['accuracy', 'binary_crossentropy'])
baseline_model.summary()

baseline_history = baseline_model.fit(train_data, train_labels,
         epochs=20, batch_size=512,
         validation_data=(test_data, test_labels),
         verbose=2)

小模型

small_model = keras.Sequential(
[
layers.Dense(4, activation='relu', input_shape=(NUM_WORDS,)),
layers.Dense(4, activation='relu'),
layers.Dense(1, activation='sigmoid')
]
)
small_model.compile(optimizer='adam',
     loss='binary_crossentropy',
     metrics=['accuracy', 'binary_crossentropy'])
small_model.summary()
small_history = small_model.fit(train_data, train_labels,
         epochs=20, batch_size=512,
         validation_data=(test_data, test_labels),
         verbose=2)

大模型

big_model = keras.Sequential(
[
layers.Dense(512, activation='relu', input_shape=(NUM_WORDS,)),
layers.Dense(512, activation='relu'),
layers.Dense(1, activation='sigmoid')
]
)
big_model.compile(optimizer='adam',
     loss='binary_crossentropy',
     metrics=['accuracy', 'binary_crossentropy'])
big_model.summary()
big_history = big_model.fit(train_data, train_labels,
         epochs=20, batch_size=512,
         validation_data=(test_data, test_labels),
         verbose=2)

绘图比较上述三个模型

def plot_history(histories, key='binary_crossentropy'):
plt.figure(figsize=(16,10))

for name, history in histories:
val = plt.plot(history.epoch, history.history['val_'+key],
    '--', label=name.title()+' Val')
plt.plot(history.epoch, history.history[key], color=val[0].get_color(),
   label=name.title()+' Train')

plt.xlabel('Epochs')
plt.ylabel(key.replace('_',' ').title())
plt.legend()

plt.xlim([0,max(history.epoch)])

plot_history([('baseline', baseline_history),
   ('small', small_history),
   ('big', big_history)])

三个模型在迭代过程中在训练集的表现都会越来越好，并且都会出现过拟合的现象

大模型在训练集上表现更好，过拟合的速度更快

l2正则减少过拟合

l2_model = keras.Sequential(
[
layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),
    activation='relu', input_shape=(NUM_WORDS,)),
layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),
    activation='relu'),
layers.Dense(1, activation='sigmoid')
]
)
l2_model.compile(optimizer='adam',
     loss='binary_crossentropy',
     metrics=['accuracy', 'binary_crossentropy'])
l2_model.summary()
l2_history = l2_model.fit(train_data, train_labels,
         epochs=20, batch_size=512,
         validation_data=(test_data, test_labels),
         verbose=2)
plot_history([('baseline', baseline_history),
   ('l2', l2_history)])

可以发现正则化之后的模型在验证集上的过拟合程度减少

添加dropout减少过拟合

dpt_model = keras.Sequential(
[
layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),
layers.Dropout(0.5),
layers.Dense(16, activation='relu'),
layers.Dropout(0.5),
layers.Dense(1, activation='sigmoid')
]
)
dpt_model.compile(optimizer='adam',
     loss='binary_crossentropy',
     metrics=['accuracy', 'binary_crossentropy'])
dpt_model.summary()
dpt_history = dpt_model.fit(train_data, train_labels,
         epochs=20, batch_size=512,
         validation_data=(test_data, test_labels),
         verbose=2)
plot_history([('baseline', baseline_history),
   ('dropout', dpt_history)])

批正则化

model = keras.Sequential([
layers.Dense(64, activation='relu', input_shape=(784,)),
layers.BatchNormalization(),
layers.Dense(64, activation='relu'),
layers.BatchNormalization(),
layers.Dense(64, activation='relu'),
layers.BatchNormalization(),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer=keras.optimizers.SGD(),
   loss=keras.losses.SparseCategoricalCrossentropy(),
   metrics=['accuracy'])
model.summary()
history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['training', 'validation'], loc='upper left')
plt.show()

总结

防止神经网络中过度拟合的最常用方法：

获取更多训练数据。

减少网络容量。

添加权重正规化。

添加dropout。

来源：https://blog.csdn.net/Lzj000lzj/article/details/94132842