keras处理欠拟合和过拟合的实例讲解
更新时间:2020年05月25日 08:46:01 作者:Lzj000lzj
这篇文章主要介绍了keras处理欠拟合和过拟合的实例讲解,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
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。
以上这篇keras处理欠拟合和过拟合的实例讲解就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持脚本之家。
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