小编这次要给大家分享的是keras如何处理欠拟合和过拟合，文章内容丰富，感兴趣的小伙伴可以来了解一下，希望大家阅读完这篇文章之后能够有所收获。

baseline

import tensorflow.keras.layers as layersbaseline_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()

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