Keras猫狗大战八：resnet50预训练模型迁移学习，图片先做归一化预处理，精度提高到97.5%

在上一篇的基础上，对数据调用keras图片预处理函数preprocess_input做归一化预处理，进行训练。

导入preprocess_input：

import os

from keras import layers, optimizers, models
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.layers import *    
from keras.models import Model

数据生成添加preprocessing_function=preprocess_input

from keras.preprocessing.image import ImageDataGenerator

batch_size = 64

train_datagen = ImageDataGenerator(
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    vertical_flip=True,
    preprocessing_function=preprocess_input)

test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)


train_generator = train_datagen.flow_from_directory(
        # This is the target directory
        train_dir,
        # All images will be resized to 150x150
        target_size=(150, 150),
        batch_size=batch_size,
        # Since we use binary_crossentropy loss, we need binary labels
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=batch_size,
        class_mode='binary')

训练25epoch，学习率从1e-3下降到4e-5：

Epoch 1/100
281/281 [==============================] - 152s 540ms/step - loss: 0.2849 - acc: 0.8846 - lr: 0.0010 - val_loss: 0.1195 - val_acc: 0.9694 - val_lr: 0.0010
Epoch 2/100
281/281 [==============================] - 79s 282ms/step - loss: 0.2234 - acc: 0.9079 - lr: 0.0010 - val_loss: 0.1105 - val_acc: 0.9673 - val_lr: 0.0010
Epoch 3/100
281/281 [==============================] - 80s 285ms/step - loss: 0.2070 - acc: 0.9135 - lr: 0.0010 - val_loss: 0.1061 - val_acc: 0.9716 - val_lr: 0.0010
Epoch 4/100
281/281 [==============================] - 80s 283ms/step - loss: 0.1939 - acc: 0.9203 - lr: 0.0010 - val_loss: 0.0998 - val_acc: 0.9748 - val_lr: 0.0010
Epoch 5/100
......

Epoch 22/100
281/281 [==============================] - 80s 284ms/step - loss: 0.1368 - acc: 0.9470 - lr: 4.0000e-05 - val_loss: 0.0943 - val_acc: 0.9777 - val_lr: 4.0000e-05
Epoch 23/100
281/281 [==============================] - 80s 283ms/step - loss: 0.1346 - acc: 0.9479 - lr: 4.0000e-05 - val_loss: 0.1046 - val_acc: 0.9720 - val_lr: 4.0000e-05
Epoch 24/100
281/281 [==============================] - 79s 283ms/step - loss: 0.1320 - acc: 0.9476 - lr: 4.0000e-05 - val_loss: 0.0938 - val_acc: 0.9759 - val_lr: 4.0000e-05
Epoch 25/100
281/281 [==============================] - 79s 282ms/step - loss: 0.1356 - acc: 0.9476 - lr: 4.0000e-05 - val_loss: 0.1063 - val_acc: 0.9745 - val_lr: 4.0000e-05

在测试图片时也需要进行归一化预处理：

def get_input_xy(src=[]):
    pre_x = []
    true_y = []

    class_indices = {'cat': 0, 'dog': 1}

    for s in src:
        input = cv2.imread(s)
        input = cv2.resize(input, (150, 150))
        input = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
        pre_x.append(preprocess_input(input))

        _, fn = os.path.split(s)
        y = class_indices.get(fn[:3])
        true_y.append(y)

    pre_x = np.array(pre_x)

    return pre_x, true_y

    
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))
    print(tick_marks, type(tick_marks))
    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97.5%，较前面提高了1.3%：

[[1225   25]
 [  38 1212]]
0.98
0.9696
0.9748

猫的准确度为98%，狗的为97%，总的准确度为97.5%。混淆矩阵图：