标签：tick load ros iter normal 迁移 卷积层 https mod
在https://www.cnblogs.com/zhengbiqing/p/11780161.html中直接在resnet网络的卷积层后添加一层分类层，得到一个最简单的迁移学习模型，得到的结果为95.3%。
这里对最后的分类网络做些优化：用GlobalAveragePooling2D替换Flatten、增加一个密集连接层（同时添加BN、Activation、Dropout）：
conv_base = ResNet50(weights=‘imagenet‘, include_top=False, input_shape=(150, 150, 3)) for layers in conv_base.layers[:]: layers.trainable = False x = conv_base.output x = GlobalAveragePooling2D()(x) x = Dense(1024)(x) x = BatchNormalization()(x) x = Activation(‘relu‘)(x) x = Dropout(0.3)(x) predictions = Dense(1, activation=‘sigmoid‘)(x) model = Model(inputs=conv_base.input, outputs=predictions)
另外采用动态学习率，并且打印显示出学习率：
optimizer = optimizers.RMSprop(lr=1e-3) def get_lr_metric(optimizer): def lr(y_true, y_pred): return optimizer.lr return lr lr_metric = get_lr_metric(optimizer) model.compile(loss=‘binary_crossentropy‘, optimizer=optimizer, metrics=[‘acc‘,lr_metric])
当模型的val_loss训练多轮不再下降时，提前结束训练：
from keras.callbacks import ReduceLROnPlateau,EarlyStopping early_stop = EarlyStopping(monitor=‘val_loss‘, patience=13) reduce_lr = ReduceLROnPlateau(monitor=‘val_loss‘, patience=7, mode=‘auto‘, factor=0.2) callbacks = [early_stop,reduce_lr] history = model.fit_generator( train_generator, steps_per_epoch=train_generator.samples//batch_size, epochs=100, validation_data=validation_generator, validation_steps=validation_generator.samples//batch_size, callbacks = callbacks)
共训练了61epochs，学习率从0.001下降到1.6e-6：
Epoch 1/100 281/281 [==============================] - 141s 503ms/step - loss: 0.3322 - acc: 0.8589 - lr: 0.0010 - val_loss: 0.2344 - val_acc: 0.9277 - val_lr: 0.0010 Epoch 2/100 281/281 [==============================] - 79s 279ms/step - loss: 0.2591 - acc: 0.8862 - lr: 0.0010 - val_loss: 0.2331 - val_acc: 0.9288 - val_lr: 0.0010 Epoch 3/100 281/281 [==============================] - 78s 279ms/step - loss: 0.2405 - acc: 0.8959 - lr: 0.0010 - val_loss: 0.2292 - val_acc: 0.9303 - val_lr: 0.0010
......
281/281 [==============================] - 77s 275ms/step - loss: 0.1532 - acc: 0.9407 - lr: 1.6000e-06 - val_loss: 0.1871 - val_acc: 0.9412 - val_lr: 1.6000e-06 Epoch 60/100 281/281 [==============================] - 78s 276ms/step - loss: 0.1492 - acc: 0.9396 - lr: 1.6000e-06 - val_loss: 0.1687 - val_acc: 0.9450 - val_lr: 1.6000e-06 Epoch 61/100 281/281 [==============================] - 77s 276ms/step - loss: 0.1468 - acc: 0.9414 - lr: 1.6000e-06 - val_loss: 0.1825 - val_acc: 0.9454 - val_lr: 1.6000e-06
加载模型：
optimizer = optimizers.RMSprop(lr=1e-3) def get_lr_metric(optimizer): def lr(y_true, y_pred): return optimizer.lr return lr lr_metric = get_lr_metric(optimizer) model = load_model(model_file, custom_objects={‘lr‘:lr_metric})
修改混淆矩阵函数，以打印每个类别的精确度：
def plot_sonfusion_matrix(cm, classes, normalize=False, title=‘Confusion matrix‘, cmap=plt.cm.Blues): plt.imshow(cm, interpolation=‘nearest‘, cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks([-0.5,1.5], classes) print(cm) ok_num = 0 for k in range(cm.shape[0]): print(cm[k,k]/np.sum(cm[k,:])) ok_num += cm[k,k] print(ok_num/np.sum(cm)) if normalize: cm = cm.astype(‘float‘) / cm.sum(axis=1)[:, np.newaxis] thresh = cm.max() / 2.0 for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment=‘center‘, color=‘white‘ if cm[i, j] > thresh else ‘black‘) plt.tight_layout() plt.ylabel(‘True label‘) plt.xlabel(‘Predict label‘)
测试结果为：
[[1200 50] [ 45 1205]] 0.96 0.964 0.962
猫的准确度为96%，狗的为96.4%，总的准确度为96.2%。混淆矩阵图：
Keras猫狗大战七：resnet50预训练模型迁移学习优化，动态调整学习率，精度提高到96.2%
标签：tick load ros iter normal 迁移 卷积层 https mod
原文地址：https://www.cnblogs.com/zhengbiqing/p/11964301.html
