keras 模型参数，模型保存，中间结果输出

比眉伴天荒 2022-05-27 09:52 269阅读 0赞

''' Created on 2018-4-16 '''
    import keras
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.models import Model
    from  keras.callbacks import ModelCheckpoint,Callback
    import numpy as np
    import tflearn
    import tflearn.datasets.mnist as mnist
    
    x_train, y_train, x_test, y_test = mnist.load_data(one_hot=True)
    x_valid = x_test[:5000]
    y_valid = y_test[:5000]
    x_test = x_test[5000:]
    y_test = y_test[5000:]
    print(x_valid.shape)
    print(x_test.shape)
    
    model = Sequential()
    model.add(Dense(units=64, activation='relu', input_dim=784))
    model.add(Dense(units=10, activation='softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='sgd',
                  metrics=['accuracy'])
    filepath = 'D:\\machineTest\\model-ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5'
    # filepath = 'D:\\machineTest\\model-ep{epoch:03d}-loss{loss:.3f}.h5'
    checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
    print(model.get_config())
    # [{'class_name': 'Dense', 'config': {'bias_regularizer': None, 'use_bias': True, 'kernel_regularizer': None, 'batch_input_shape': (None, 784), 'trainable': True, 'kernel_constraint': None, 'bias_constraint': None, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'distribution': 'uniform', 'mode': 'fan_avg', 'seed': None}}, 'activity_regularizer': None, 'units': 64, 'dtype': 'float32', 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'activation': 'relu', 'name': 'dense_1'}}, {'class_name': 'Dense', 'config': {'bias_regularizer': None, 'use_bias': True, 'kernel_regularizer': None, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_constraint': None, 'bias_constraint': None, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'distribution': 'uniform', 'mode': 'fan_avg', 'seed': None}}, 'activity_regularizer': None, 'trainable': True, 'units': 10, 'activation': 'softmax', 'name': 'dense_2'}}]
    # model.fit(x_train, y_train, epochs=1, batch_size=128, callbacks=[checkpoint],validation_data=(x_valid, y_valid))
    model.fit(x_train, y_train, epochs=1,validation_data=(x_valid, y_valid),steps_per_epoch=10,validation_steps=1)
    # score = model.evaluate(x_test, y_test, batch_size=128)
    # print(score)
    # #获取模型结构状况
    # model.summary()
    # _________________________________________________________________
    # Layer (type) Output Shape Param # 
    # =================================================================
    # dense_1 (Dense) (None, 64) 50240(784*64+64(b)) 
    # _________________________________________________________________
    # dense_2 (Dense) (None, 10) 650(64*10 + 10 ) 
    # =================================================================
    # #根据下标和名称返回层对象
    # layer = model.get_layer(index = 0)
    # 获取模型权重,设置权重model.set_weights()
    weights = np.array(model.get_weights())
    print(weights.shape)
    # (4,)权重由4部分组成
    print(weights[0].shape)
    # (784, 64)dense_1 w1
    print(weights[1].shape)
    # (64,)dense_1 b1
    print(weights[2].shape)
    # (64, 10)dense_2 w2
    print(weights[3].shape)
    # (10,)dense_2 b2
    
    
    # # 保存权重和加载权重
    # model.save_weights("D:\\xxx\\weights.h5")
    # model.load_weights("D:\\xxx\\weights.h5", by_name=False)#by_name=True，可以根据名字匹配和层载入权重
    
    # 查看中间结果，必须要先声明个函数式模型
    dense1_layer_model = Model(inputs=model.input,outputs=model.get_layer('dense_1').output)
    out = dense1_layer_model.predict(x_test)
    print(out.shape)
    # (5000, 64)
    
    # 如果是函数式模型，则可以直接输出
    # import keras
    # from keras.models import Model
    # from keras.callbacks import ModelCheckpoint,Callback
    # import numpy as np
    # from keras.layers import Input,Conv2D,MaxPooling2D
    # import cv2
    # 
    # image = cv2.imread("D:\\machineTest\\falali.jpg")
    # print(image.shape)
    # cv2.imshow("1",image)
    # 
    # # 第一层conv
    # image = image.reshape([-1, 386, 580, 3])
    # img_input = Input(shape=(386, 580, 3))
    # x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
    # x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    # x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
    # model = Model(inputs=img_input, outputs=x)
    # out = model.predict(image)
    # print(out.shape)
    # out = out.reshape(193, 290,64)
    # image_conv1 = out[:,:,1].reshape(193, 290)
    # image_conv2 = out[:,:,20].reshape(193, 290)
    # image_conv3 = out[:,:,40].reshape(193, 290)
    # image_conv4 = out[:,:,60].reshape(193, 290)
    # cv2.imshow("conv1",image_conv1)
    # cv2.imshow("conv2",image_conv2)
    # cv2.imshow("conv3",image_conv3)
    # cv2.imshow("conv4",image_conv4)
    # cv2.waitKey(0)

中间结果输出可以查看conv过之后的图像:  
原始图像:  
![这里写图片描述][SouthEast]  
经过一层conv以后,输出其中4张图片:  
![这里写图片描述][SouthEast 1]  
![这里写图片描述][SouthEast 2]  
![这里写图片描述][SouthEast 3]  
![这里写图片描述][SouthEast 4]

[SouthEast]: /images/20220527/be087a288eec4c37b376f4bde8060e2d.png
[SouthEast 1]: /images/20220527/4f7fd67d464b4dc09a6cc293db52cbab.png
[SouthEast 2]: /images/20220527/458b786c2cba44948c4652a715b8f17c.png
[SouthEast 3]: /images/20220527/eab17ab386ef49a6934b25ef59c87bac.png
[SouthEast 4]: /images/20220527/dc76c355063a4c8d993c8eda20b04e78.png