我就廢話不多說了，大家還是直接看程式碼吧~

'''
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','units': 10,'activation': 'softmax','name': 'dense_2'}}]
# model.fit(x_train,epochs=1,batch_size=128,callbacks=[checkpoint],validation_data=(x_valid,y_valid))
model.fit(x_train,y_valid),steps_per_epoch=10,validation_steps=1)
# score = model.evaluate(x_test,y_test,batch_size=128)
# print(score)
# #獲取模型結構狀況
# 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 )    
# =================================================================
# #根據下標和名稱返回層物件
# layer = model.get_layer(index = 0)
# 獲取模型權重,設定權重model.set_weights()
weights = np.array(model.get_weights())
print(weights.shape)
# (4,)權重由4部分組成
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

# # 儲存權重和載入權重
# model.save_weights("D:\\xxx\\weights.h5")
# model.load_weights("D:\\xxx\\weights.h5",by_name=False)#by_name=True，可以根據名字匹配和層載入權重

# 檢視中間結果，必須要先宣告個函式式模型
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)

# 如果是函式式模型，則可以直接輸出
# 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)
# 
# # 第一層conv
# image = image.reshape([-1,386,580,3])
# img_input = Input(shape=(386,3))
# x = Conv2D(64,(3,3),padding='same',name='block1_conv1')(img_input)
# x = Conv2D(64,name='block1_conv2')(x)
# x = MaxPooling2D((2,2),strides=(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)

中間結果輸出可以檢視conv過之後的影象：

原始影象：

經過一層conv以後,輸出其中4張圖片：

以上這篇keras 模型引數,中間結果輸出操作就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。