tensorflow.keras.save.load.model.py

"""
Author: Kevin
Link: www.kaikai.ai
Github: github.com/loveunk

这个例子用来讲述利用Keras api保存和加载model。
其中涉及的技术点包括：
1. 保存一个模型到存储
2. 加载已有模型
3. 使用已有的模型做分类
"""

import numpy as np
import tensorflow.keras as k
import tensorflow.keras.layers as layers
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns

np.random.seed(13)
sns.set(style='white', context='talk', palette='deep')

(X_train, Y_train), (X_test, Y_test) = k.datasets.mnist.load_data()

# 看看数据的shape
print(X_train.shape)
print(Y_train.shape)

# 画一个数据集的例子来看看
plt.imshow(X_train[0][:,:])
plt.show()

# 打印数据的直方图
sns.countplot(Y_train)
plt.show()

# 归一化数据，让CNN更快
X_train = X_train / 255.0
X_test = X_test / 255.0

X_train = X_train.reshape(-1, 28, 28, 1)
X_test = X_test.reshape(-1, 28, 28, 1)

# 把label转换为one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])
Y_train = k.utils.to_categorical(Y_train, num_classes=10)

X_train, X_val, Y_train, Y_val = train_test_split(X_train,
                                                  Y_train,
                                                  test_size=0.1,
                                                  random_state=2)


# 创建一个图片分类的CNN模型
def create_model():
    # 创建CNN model
    # 模型：
    """
      [[Conv2D->relu]*2 -> BatchNormalization -> MaxPool2D -> Dropout]*2 ->
      [Conv2D->relu]*2 -> BatchNormalization -> Dropout ->
      Flatten -> Dense -> BatchNormalization -> Dropout -> Out
    """
    model = k.Sequential()

    model.add(layers.Conv2D(filters=64, kernel_size=(5,5), padding='Same', activation='relu', input_shape = (28,28,1)))
    model.add(layers.BatchNormalization())

    model.add(layers.Conv2D(filters=64, kernel_size=(5,5), padding='Same', activation='relu'))
    model.add(layers.BatchNormalization())

    model.add(layers.MaxPool2D(pool_size=(2,2)))
    model.add(layers.Dropout(0.25))

    model.add(layers.Conv2D(filters=64, kernel_size=(3,3),padding='Same', activation='relu'))
    model.add(layers.BatchNormalization())

    model.add(layers.Conv2D(filters=64, kernel_size=(3,3),padding='Same', activation='relu'))
    model.add(layers.BatchNormalization())
    model.add(layers.MaxPool2D(pool_size=(2,2), strides=(2,2)))
    model.add(layers.Dropout(0.25))

    model.add(layers.Conv2D(filters=64, kernel_size=(3,3), padding='Same',  activation='relu'))
    model.add(layers.BatchNormalization())
    model.add(layers.Dropout(0.25))

    model.add(layers.Flatten())
    model.add(layers.Dense(256, activation="relu"))
    model.add(layers.BatchNormalization())
    model.add(layers.Dropout(0.25))

    model.add(layers.Dense(10, activation="softmax"))

    # 打印出model 看看
    k.utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)
    plt.imshow(mpimg.imread('model.png'))
    plt.show()

    # 定义Optimizer
    optimizer = k.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

    # 编译model
    model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])

    # 设置学习率的动态调整
    learning_rate_reduction = k.callbacks.ReduceLROnPlateau(monitor='val_acc',
                                                            patience=3,
                                                            verbose=1,
                                                            factor=0.5,
                                                            min_lr=0.00001)

    epochs = 20
    batch_size = 128

    # 通过数据增强来防止过度拟合
    datagen = k.preprocessing.image.ImageDataGenerator(
            featurewise_center=False, # set input mean to 0 over the dataset
            samplewise_center=False,  # set each sample mean to 0
            featurewise_std_normalization=False,  # divide inputs by std of the dataset
            samplewise_std_normalization=False,  # divide each input by its std
            zca_whitening=False,  # apply ZCA whitening
            rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
            zoom_range = 0.1, # Randomly zoom image
            width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
            height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
            horizontal_flip=False,  # randomly flip images
            vertical_flip=False)  # randomly flip images
    datagen.fit(X_train)

    # 训练模型
    history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
                                  epochs=epochs,
                                  validation_data=(X_val, Y_val),
                                  verbose=2,
                                  steps_per_epoch=X_train.shape[0] // batch_size,
                                  callbacks=[learning_rate_reduction])

    # 画训练集和验证集的loss和accuracy曲线。可以判断是否欠拟合或过拟合
    fig, ax = plt.subplots(2,1)
    ax[0].plot(history.history['loss'], color='b', label="Training loss")
    ax[0].plot(history.history['val_loss'], color='r', label="validation loss", axes=ax[0])
    ax[0].legend(loc='best', shadow=True)

    ax[1].plot(history.history['acc'], color='b', label="Training accuracy")
    ax[1].plot(history.history['val_acc'], color='r', label="Validation accuracy")
    ax[1].legend(loc='best', shadow=True)
    plt.show()
    return model


def predict_results(model):
    # 对测试集做预测
    results = model.predict(X_test)

    # 把one-hot vector转换为数字
    y_pred = np.argmax(results, axis=1)

    print("precision = ", precision_score(Y_test, y_pred, average="macro"))
    print("recall = ", recall_score(Y_test, y_pred, average="macro"))
    print("f1_score = ", f1_score(Y_test, y_pred, average="macro"))
    print("accuracy = ", accuracy_score(Y_test, y_pred))


# 保存模型
def save_model(model):
    model = create_model()
    model.save('keras.classifier.h5')


# ################## Section 1 ####################
# 创建和保存模型的
model = create_model()
save_model(model)

# ################## Section 2 ####################
# 加载已有的模型并做预测
model = k.models.load_model('keras.classifier.h5')
predict_results(model)