keras 模型参数,模型保存,中间结果输出操作

我就废话不多说了，大家还是直接看代码吧~

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									'''

									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)