Keras学习笔记二：保存本地模型和调用本地模型

使用深度学习模型时当然希望可以保存下训练好的模型，需要的时候直接调用，不再重新训练

一、保存模型到本地

以mnist数据集下的AutoEncoder 去噪为例。添加：

file_path="MNIST_data/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5"


tensorboard = TensorBoard(log_dir='/tmp/tb', histogram_freq=0, write_graph=False)
checkpoint = ModelCheckpoint(filepath=file_path,verbose=1,monitor='val_loss', save_weights_only=False,mode='auto' ,save_best_only=True,period=1)

autoencoder.fit(x_train_noisy, x_train,
                epochs=100,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test_noisy, x_test),
                callbacks=[checkpoint,tensorboard])

这里的tensorboard和checkpoint分别是

1、启用tensorboard可视化工具，新建终端使用tensorboard --logdir=/tmp/tb 命令

2、保存ModelCheckpoint到MNIST_data/文件夹下，这里的参数设置为观察val_loss ，当有提升时保存一次模型，如下

Keras学习笔记二：保存本地模型和调用本地模型

二、从本地读取模型

假设读取模型后使用三个图片做去噪实验：（测试的图片数量修改 pic_num ）

import os
import numpy as np
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)
import matplotlib.pyplot as plt
from keras.models import Model,Sequential,load_model
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.preprocessing.image import ImageDataGenerator,img_to_array, load_img
from keras.callbacks import TensorBoard , ModelCheckpoint
print("_________________________keras start_____________________________")
pic_num = 3
base_dir = 'MNIST_data' #基准目录
train_dir = os.path.join(base_dir,'my_test') #train目录
validation_dir="".join(train_dir)
test_datagen = ImageDataGenerator(rescale= 1./255)
validation_generator  = test_datagen.flow_from_directory(validation_dir,
                                                    target_size = (28,28),
                                                    color_mode = "grayscale",
                                                    batch_size = pic_num,
                                                    class_mode =  "categorical")#利用test_datagen.flow_from_directory（图像地址，目标size，批量数目，标签分类情况）
for x_train,batch_labels in validation_generator:
    break
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
y_train = x_train

# create model
model = load_model('MNIST_data/my_model.hdf5')
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print("Created model and loaded weights from file")

# estimate accuracy on whole dataset using loaded weights
y_train=model.predict(x_train)

n = pic_num
for i in range(n):
    ax = plt.subplot(2, n, i+1)
    plt.imshow(x_train[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    ax = plt.subplot(2, n, i+1+n)
    plt.imshow(y_train[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()