Tom Hardy

keras调用自己训练的模型，并去掉全连接层

其实很简单

from keras.models import load_model

base_model = load_model('model_resenet.h5')#加载指定的模型
print(base_model.summary())#输出网络的结构图

这是我的网络模型的输出，其实就是它的结构图

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 227, 227, 1)  0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 225, 225, 32) 320         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 225, 225, 32) 128         conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 225, 225, 32) 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 225, 225, 32) 9248        activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 225, 225, 32) 128         conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 225, 225, 32) 0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 225, 225, 32) 9248        activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 225, 225, 32) 128         conv2d_3[0][0]                   
__________________________________________________________________________________________________
merge_1 (Merge)                 (None, 225, 225, 32) 0           batch_normalization_3[0][0]      
                                                                 activation_1[0][0]               
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 225, 225, 32) 0           merge_1[0][0]                    
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 225, 225, 32) 9248        activation_3[0][0]               
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 225, 225, 32) 128         conv2d_4[0][0]                   
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 225, 225, 32) 0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 225, 225, 32) 9248        activation_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 225, 225, 32) 128         conv2d_5[0][0]                   
__________________________________________________________________________________________________
merge_2 (Merge)                 (None, 225, 225, 32) 0           batch_normalization_5[0][0]      
                                                                 activation_3[0][0]               
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 225, 225, 32) 0           merge_2[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 112, 112, 32) 0           activation_5[0][0]               
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 110, 110, 64) 18496       max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 110, 110, 64) 256         conv2d_6[0][0]                   
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 110, 110, 64) 0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 110, 110, 64) 36928       activation_6[0][0]               
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 110, 110, 64) 256         conv2d_7[0][0]                   
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 110, 110, 64) 0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 110, 110, 64) 36928       activation_7[0][0]               
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 110, 110, 64) 256         conv2d_8[0][0]                   
__________________________________________________________________________________________________
merge_3 (Merge)                 (None, 110, 110, 64) 0           batch_normalization_8[0][0]      
                                                                 activation_6[0][0]               
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 110, 110, 64) 0           merge_3[0][0]                    
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 110, 110, 64) 36928       activation_8[0][0]               
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 110, 110, 64) 256         conv2d_9[0][0]                   
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 110, 110, 64) 0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 110, 110, 64) 36928       activation_9[0][0]               
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 110, 110, 64) 256         conv2d_10[0][0]                  
__________________________________________________________________________________________________
merge_4 (Merge)                 (None, 110, 110, 64) 0           batch_normalization_10[0][0]     
                                                                 activation_8[0][0]               
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 110, 110, 64) 0           merge_4[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_10[0][0]              
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 53, 53, 64)   36928       max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 53, 53, 64)   256         conv2d_11[0][0]                  
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 53, 53, 64)   0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)  (None, 26, 26, 64)   0           activation_11[0][0]              
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 26, 26, 64)   36928       max_pooling2d_3[0][0]            
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 26, 26, 64)   256         conv2d_12[0][0]                  
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 26, 26, 64)   0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 26, 26, 64)   36928       activation_12[0][0]              
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 26, 26, 64)   256         conv2d_13[0][0]                  
__________________________________________________________________________________________________
merge_5 (Merge)                 (None, 26, 26, 64)   0           batch_normalization_13[0][0]     
                                                                 max_pooling2d_3[0][0]            
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 26, 26, 64)   0           merge_5[0][0]                    
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 26, 26, 64)   36928       activation_13[0][0]              
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 26, 26, 64)   256         conv2d_14[0][0]                  
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 26, 26, 64)   0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 26, 26, 64)   36928       activation_14[0][0]              
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 26, 26, 64)   256         conv2d_15[0][0]                  
__________________________________________________________________________________________________
merge_6 (Merge)                 (None, 26, 26, 64)   0           batch_normalization_15[0][0]     
                                                                 activation_13[0][0]              
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 26, 26, 64)   0           merge_6[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)  (None, 13, 13, 64)   0           activation_15[0][0]              
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 11, 11, 32)   18464       max_pooling2d_4[0][0]            
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 11, 11, 32)   128         conv2d_16[0][0]                  
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 11, 11, 32)   0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 11, 11, 32)   9248        activation_16[0][0]              
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 11, 11, 32)   128         conv2d_17[0][0]                  
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 11, 11, 32)   0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 11, 11, 32)   9248        activation_17[0][0]              
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 11, 11, 32)   128         conv2d_18[0][0]                  
__________________________________________________________________________________________________
merge_7 (Merge)                 (None, 11, 11, 32)   0           batch_normalization_18[0][0]     
                                                                 activation_16[0][0]              
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 11, 11, 32)   0           merge_7[0][0]                    
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 11, 11, 32)   9248        activation_18[0][0]              
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 11, 11, 32)   128         conv2d_19[0][0]                  
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 11, 11, 32)   0           batch_normalization_19[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 11, 11, 32)   9248        activation_19[0][0]              
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 11, 11, 32)   128         conv2d_20[0][0]                  
__________________________________________________________________________________________________
merge_8 (Merge)                 (None, 11, 11, 32)   0           batch_normalization_20[0][0]     
                                                                 activation_18[0][0]              
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 11, 11, 32)   0           merge_8[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D)  (None, 5, 5, 32)     0           activation_20[0][0]              
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 3, 3, 64)     18496       max_pooling2d_5[0][0]            
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 3, 3, 64)     256         conv2d_21[0][0]                  
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 3, 3, 64)     0           batch_normalization_21[0][0]     
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 3, 3, 64)     36928       activation_21[0][0]              
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 3, 3, 64)     256         conv2d_22[0][0]                  
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 3, 3, 64)     0           batch_normalization_22[0][0]     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 3, 3, 64)     36928       activation_22[0][0]              
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 3, 3, 64)     256         conv2d_23[0][0]                  
__________________________________________________________________________________________________
merge_9 (Merge)                 (None, 3, 3, 64)     0           batch_normalization_23[0][0]     
                                                                 activation_21[0][0]              
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 3, 3, 64)     0           merge_9[0][0]                    
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 3, 3, 64)     36928       activation_23[0][0]              
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 3, 3, 64)     256         conv2d_24[0][0]                  
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 3, 3, 64)     0           batch_normalization_24[0][0]     
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 3, 3, 64)     36928       activation_24[0][0]              
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 3, 3, 64)     256         conv2d_25[0][0]                  
__________________________________________________________________________________________________
merge_10 (Merge)                (None, 3, 3, 64)     0           batch_normalization_25[0][0]     
                                                                 activation_23[0][0]              
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 3, 3, 64)     0           merge_10[0][0]                   
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D)  (None, 1, 1, 64)     0           activation_25[0][0]              
__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 64)           0           max_pooling2d_6[0][0]            
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 256)          16640       flatten_1[0][0]                  
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 256)          0           dense_1[0][0]                    
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 2)            514         dropout_1[0][0]                  
==================================================================================================
Total params: 632,098
Trainable params: 629,538
Non-trainable params: 2,560
__________________________________________________________________________________________________

去掉模型的全连接层

from keras.models import load_model

base_model = load_model('model_resenet.h5')
resnet_model = Model(inputs=base_model.input, outputs=base_model.get_layer('max_pooling2d_6').output)
#'max_pooling2d_6'其实就是上述网络中全连接层的前面一层，当然这里你也可以选取其它层，把该层的名称代替'max_pooling2d_6'即可，这样其实就是截取网络，输出网络结构就是方便读取每层的名字。
print(resnet_model.summary())

新输出的网络结构：

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 227, 227, 1)  0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 225, 225, 32) 320         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 225, 225, 32) 128         conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 225, 225, 32) 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 225, 225, 32) 9248        activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 225, 225, 32) 128         conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 225, 225, 32) 0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 225, 225, 32) 9248        activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 225, 225, 32) 128         conv2d_3[0][0]                   
__________________________________________________________________________________________________
merge_1 (Merge)                 (None, 225, 225, 32) 0           batch_normalization_3[0][0]      
                                                                 activation_1[0][0]               
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 225, 225, 32) 0           merge_1[0][0]                    
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 225, 225, 32) 9248        activation_3[0][0]               
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 225, 225, 32) 128         conv2d_4[0][0]                   
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 225, 225, 32) 0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 225, 225, 32) 9248        activation_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 225, 225, 32) 128         conv2d_5[0][0]                   
__________________________________________________________________________________________________
merge_2 (Merge)                 (None, 225, 225, 32) 0           batch_normalization_5[0][0]      
                                                                 activation_3[0][0]               
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 225, 225, 32) 0           merge_2[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 112, 112, 32) 0           activation_5[0][0]               
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 110, 110, 64) 18496       max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 110, 110, 64) 256         conv2d_6[0][0]                   
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 110, 110, 64) 0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 110, 110, 64) 36928       activation_6[0][0]               
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 110, 110, 64) 256         conv2d_7[0][0]                   
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 110, 110, 64) 0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 110, 110, 64) 36928       activation_7[0][0]               
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 110, 110, 64) 256         conv2d_8[0][0]                   
__________________________________________________________________________________________________
merge_3 (Merge)                 (None, 110, 110, 64) 0           batch_normalization_8[0][0]      
                                                                 activation_6[0][0]               
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 110, 110, 64) 0           merge_3[0][0]                    
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 110, 110, 64) 36928       activation_8[0][0]               
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 110, 110, 64) 256         conv2d_9[0][0]                   
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 110, 110, 64) 0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 110, 110, 64) 36928       activation_9[0][0]               
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 110, 110, 64) 256         conv2d_10[0][0]                  
__________________________________________________________________________________________________
merge_4 (Merge)                 (None, 110, 110, 64) 0           batch_normalization_10[0][0]     
                                                                 activation_8[0][0]               
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 110, 110, 64) 0           merge_4[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_10[0][0]              
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 53, 53, 64)   36928       max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 53, 53, 64)   256         conv2d_11[0][0]                  
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 53, 53, 64)   0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)  (None, 26, 26, 64)   0           activation_11[0][0]              
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 26, 26, 64)   36928       max_pooling2d_3[0][0]            
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 26, 26, 64)   256         conv2d_12[0][0]                  
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 26, 26, 64)   0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 26, 26, 64)   36928       activation_12[0][0]              
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 26, 26, 64)   256         conv2d_13[0][0]                  
__________________________________________________________________________________________________
merge_5 (Merge)                 (None, 26, 26, 64)   0           batch_normalization_13[0][0]     
                                                                 max_pooling2d_3[0][0]            
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 26, 26, 64)   0           merge_5[0][0]                    
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 26, 26, 64)   36928       activation_13[0][0]              
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 26, 26, 64)   256         conv2d_14[0][0]                  
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 26, 26, 64)   0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 26, 26, 64)   36928       activation_14[0][0]              
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 26, 26, 64)   256         conv2d_15[0][0]                  
__________________________________________________________________________________________________
merge_6 (Merge)                 (None, 26, 26, 64)   0           batch_normalization_15[0][0]     
                                                                 activation_13[0][0]              
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 26, 26, 64)   0           merge_6[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)  (None, 13, 13, 64)   0           activation_15[0][0]              
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 11, 11, 32)   18464       max_pooling2d_4[0][0]            
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 11, 11, 32)   128         conv2d_16[0][0]                  
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 11, 11, 32)   0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 11, 11, 32)   9248        activation_16[0][0]              
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 11, 11, 32)   128         conv2d_17[0][0]                  
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 11, 11, 32)   0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 11, 11, 32)   9248        activation_17[0][0]              
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 11, 11, 32)   128         conv2d_18[0][0]                  
__________________________________________________________________________________________________
merge_7 (Merge)                 (None, 11, 11, 32)   0           batch_normalization_18[0][0]     
                                                                 activation_16[0][0]              
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 11, 11, 32)   0           merge_7[0][0]                    
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 11, 11, 32)   9248        activation_18[0][0]              
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 11, 11, 32)   128         conv2d_19[0][0]                  
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 11, 11, 32)   0           batch_normalization_19[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 11, 11, 32)   9248        activation_19[0][0]              
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 11, 11, 32)   128         conv2d_20[0][0]                  
__________________________________________________________________________________________________
merge_8 (Merge)                 (None, 11, 11, 32)   0           batch_normalization_20[0][0]     
                                                                 activation_18[0][0]              
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 11, 11, 32)   0           merge_8[0][0]                    
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D)  (None, 5, 5, 32)     0           activation_20[0][0]              
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 3, 3, 64)     18496       max_pooling2d_5[0][0]            
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 3, 3, 64)     256         conv2d_21[0][0]                  
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 3, 3, 64)     0           batch_normalization_21[0][0]     
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 3, 3, 64)     36928       activation_21[0][0]              
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 3, 3, 64)     256         conv2d_22[0][0]                  
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 3, 3, 64)     0           batch_normalization_22[0][0]     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 3, 3, 64)     36928       activation_22[0][0]              
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 3, 3, 64)     256         conv2d_23[0][0]                  
__________________________________________________________________________________________________
merge_9 (Merge)                 (None, 3, 3, 64)     0           batch_normalization_23[0][0]     
                                                                 activation_21[0][0]              
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 3, 3, 64)     0           merge_9[0][0]                    
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 3, 3, 64)     36928       activation_23[0][0]              
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 3, 3, 64)     256         conv2d_24[0][0]                  
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 3, 3, 64)     0           batch_normalization_24[0][0]     
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 3, 3, 64)     36928       activation_24[0][0]              
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 3, 3, 64)     256         conv2d_25[0][0]                  
__________________________________________________________________________________________________
merge_10 (Merge)                (None, 3, 3, 64)     0           batch_normalization_25[0][0]     
                                                                 activation_23[0][0]              
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 3, 3, 64)     0           merge_10[0][0]                   
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D)  (None, 1, 1, 64)     0           activation_25[0][0]              
==================================================================================================
Total params: 614,944
Trainable params: 612,384
Non-trainable params: 2,560
__________________________________________________________________________________________________

你可能感兴趣的:(深度学习框架)

云服务业界动态简报-20180128 Captain7
一、青云青云QingCloud推出深度学习平台DeepLearningonQingCloud，包含了主流的深度学习框架及数据科学工具包，通过QingCloudAppCenter一键部署交付，可以让算法工程师和数据科学家快速构建深度学习开发环境，将更多的精力放在模型和算法调优。二、腾讯云1.腾讯云正式发布腾讯专有云TCE(TencentCloudEnterprise)矩阵，涵盖企业版、大数据版、AI
Keras深度学习框架入门及实战指南司莹嫣Maude
Keras深度学习框架入门及实战指南keraskeras-team/keras:是一个基于Python的深度学习库，它没有使用数据库。适合用于深度学习任务的开发和实现，特别是对于需要使用Python深度学习库的场景。特点是深度学习库、Python、无数据库。项目地址:https://gitcode.com/gh_mirrors/ke/keras一、项目介绍Keras简介Keras是一款高级神经网络
深度学习入门篇：PyTorch实现手写数字识别 AI_Guru人工智能深度学习 pytorch 人工智能
深度学习作为机器学习的一个分支，近年来在图像识别、自然语言处理等领域取得了显著的成就。在众多的深度学习框架中，PyTorch以其动态计算图、易用性强和灵活度高等特点，受到了广泛的喜爱。本篇文章将带领大家使用PyTorch框架，实现一个手写数字识别的基础模型。手写数字识别简介手写数字识别是计算机视觉领域的一个经典问题，目的是让计算机能够识别并理解手写数字图像。这个问题通常作为深度学习入门的练习，因为
【ShuQiHere】小白也能懂的 TensorFlow 和 PyTorch GPU 配置教程 ShuQiHere tensorflow pytorch 人工智能
【ShuQiHere】在深度学习中，GPU的使用对于加速模型训练至关重要。然而，对于许多刚刚入门的小白来说，如何在TensorFlow和PyTorch中指定使用GPU进行训练可能会感到困惑。在本文中，我将详细介绍如何在这两个主流的深度学习框架中指定使用GPU进行训练，并确保每一个步骤都简单易懂，跟着我的步骤来，你也能轻松上手！1.安装所需库首先，确保你已经安装了TensorFlow或PyTorch
解决ModuleNotFoundError: No module named ‘torch的方法梅菊林各种问题解决方案开发语言
ModuleNotFoundError:Nomodulenamed‘torch’错误是Python在尝试导入名为torch的模块时找不到该模块而抛出的异常。torch是PyTorch深度学习框架的核心库，如果你的Python环境中没有安装这个库，尝试导入时就会遇到这个错误。文章目录报错问题报错原因解决方法报错问题当你尝试在Python脚本或交互式环境中执行以下命令时：importtorch如果Py
NLP_jieba中文分词的常用模块 Hiweir · NLP_jieba的使用自然语言处理中文分词人工智能 nlp
1.jieba分词模式（1）精确模式:把句子最精确的切分开,比较适合文本分析.默认精确模式.（2）全模式:把句子中所有可能成词的词都扫描出来,cut_all=True,缺点:速度快,不能解决歧义（3）paddle:利用百度的paddlepaddle深度学习框架.简单来说就是使用百度提供的分词模型.use_paddle=True.（4）搜索引擎模式:在精确模式的基础上,对长词再进行切分,提高召回率,
每天五分钟玩转深度学习框架PyTorch：获取神经网络模型的参数幻风_huanfeng 深度学习框架pytorch 深度学习 pytorch 神经网络人工智能模型参数 python
本文重点当我们定义好神经网络之后，这个网络是由多个网络层构成的，每层都有参数，我们如何才能获取到这些参数呢？我们将再下面介绍几个方法来获取神经网络的模型参数，此文我们是为了学习第6步（优化器）。获取所有参数Parametersfromtorchimportnnnet=nn.Sequential(nn.Linear(4,2),nn.Linear(2,2))print(list(net.paramet
每天五分钟玩转深度学习框架PyTorch：将nn的神经网络层连接起来幻风_huanfeng 深度学习框架pytorch 深度学习 pytorch 神经网络人工智能机器学习 python
本文重点前面我们学习pytorch中已经封装好的神经网络层，有全连接层，激活层，卷积层等等，我们可以直接使用。如代码所示我们直接使用了两个nn.Linear（），这两个linear之间并没有组合在一起，所以forward的之后，分别调用了，在实际使用中我们常常将几个神经层组合在一起，这样不仅操作方便，而且代码清晰。这里介绍一下Sequential()和ModuleList()，它们可以将多个神经网
【Python】已解决：ModuleNotFoundError: No module named ‘paddle‘ 屿小夏 python paddle 开发语言
文章目录一、分析问题背景二、可能出错的原因三、错误代码示例四、正确代码示例步骤1：安装PaddlePaddle库步骤2：验证安装五、注意事项已解决：ModuleNotFoundError:Nomodulenamed‘paddle‘一、分析问题背景在使用Python进行深度学习开发时，开发者可能会选择使用PaddlePaddle作为深度学习框架。然而，有时在导入PaddlePaddle库时，可能会遇
深度学习算法，该如何深入，举例说明 liyy614 深度学习
深度学习算法的深入学习可以从理论和实践两个方面进行。理论上，深入理解深度学习需要掌握数学基础（如线性代数、概率论、微积分）、机器学习基础和深度学习框架原理。实践上，可以通过实现和优化深度学习模型来提升技能。理论深入数学基础线性代数：理解向量、矩阵、特征值和特征向量等，对于理解神经网络的权重和偏置矩阵至关重要。概率论：用于理解模型的不确定性，如Dropout等正则化技术。微积分：理解梯度下降等优化算
深度学习框架人工智能操作系统训练&前向推理 PyTorch Tensorflow MindSpore caffe 张量加速引擎TBE 深度学习编译器多面体 polyhedral AI集群框架 EwenWanW 深度学习人工智能 pytorch 深度学习编译器
深度学习框架人工智能操作系统训练&前向推理深度学习框架发展到今天，目前在架构上大体已经基本上成熟并且逐渐趋同。无论是国外的Tensorflow、PyTorch，亦或是国内最近开源的MegEngine、MindSpore，目前基本上都是支持EagerMode和GraphMode两种模式。AI嵌入式框架OneFlow&清华计图Jittor&华为深度学习框架MindSpore&旷视深度学习框架MegEn
自然语言处理系列六十一》分布式深度学习实战》TensorFlow深度学习框架陈敬雷-充电了么-CEO兼CTO 人工智能算法 python 深度学习自然语言处理 AIGC chatgpt gpt-3 gpt ai
注：此文章内容均节选自充电了么创始人，CEO兼CTO陈敬雷老师的新书《自然语言处理原理与实战》（人工智能科学与技术丛书）【陈敬雷编著】【清华大学出版社】文章目录自然语言处理系列六十一分布式深度学习实战》TensorFlow深度学习框架安装和部署过程总结自然语言处理系列六十一分布式深度学习实战》TensorFlow深度学习框架TensorFlow作为最流行的深度学习框架之一，表达了高层次的机器学习计
深度学习框架相关-Python模块的介绍和使用---torch sccum Python常用库的介绍和使用深度学习 python 人工智能
文章摘要：'''1.torch模块，是一个开源的深度学习框架，主要用于构建和训练神经网络。PyTorch的设计目标是提供灵活且高效的工具集，用于深度学习和科学计算；2.下面主要介绍torch模块的五个功能：数据加载和处理，GPU加速，建立网络模型，模型的保存和加载，梯度更新和参数优化；上面功能主要用到的子模块如下：torch.utils.data、torch.cuda、torch.nn、torch
深度学习之深度学习框架——Tensorflow 只求毕业
目录Tensorflow框架Tensorflow的特点Tensorflow基础Tensorflow的编程习惯Tensorflow的设计基本思想Tensorflow进阶图创一个新的图——tf.Graph()op有哪些会话会话的run()方法张量张量的阶张量的数据类型张量的属性张量的静态形状和动态形状张量操作——生成张量张量操作——张量变换Tensorflow框架Tensorflow的特点Tensor
【Python】TensorFlow介绍与实战 Ustinian_310 python tensorflow 开发语言深度学习
TensorFlow介绍与使用1.前言在人工智能领域的快速发展中，深度学习框架的选择至关重要。TensorFlow以其灵活性和强大的社区支持，成为了许多研究者和开发者的首选。本文将进一步扩展对TensorFlow的介绍，包括其优势、应用场景以及在最新版本中的新特性，旨在为读者提供一个全面的学习指南。2.TensorFlow简介2.1TensorFlow的优势社区支持：TensorFlow拥有庞大的
TensorFlow库详解：Python中的深度学习框架极客代码玩转AI 玩转Python 开发语言 python pygame
引言TensorFlow是由GoogleBrain团队开发的开源机器学习库，用于各种复杂的数学计算，特别是涉及深度学习的计算。它提供了大量工具和资源，用于构建和训练机器学习模型。TensorFlow因其强大的功能和灵活性，在机器学习和深度学习领域得到了广泛应用。一、TensorFlow的基本结构TensorFlow的核心是计算图，它是一种用于表示计算的图。这种图可以包含许多节点，每个节点代表一个操
深度学习入门：使用 PyTorch 构建和训练你的第一个神经网络 Mr' 郑深度学习 pytorch 神经网络
引言深度学习是机器学习的一个分支，它利用多层非线性处理单元（即神经网络）来解决复杂的模式识别问题。PyTorch是一个强大的深度学习框架，它提供了灵活的API和动态计算图，非常适合初学者和研究者使用。安装PyTorch确保安装了Python和pip。然后通过以下命令安装PyTorch：pipinstalltorchtorchvision导入库我们需要导入一些必要的库：importtorchimpo
并行处理的艺术：深入探索PyTorch中的torch.nn.parallel模块杨哥带你写代码 pytorch 人工智能 python
标题：并行处理的艺术：深入探索PyTorch中的torch.nn.parallel模块在深度学习领域，模型的规模和复杂性不断增长，这要求我们利用所有可用的计算资源来加速训练和推理过程。PyTorch，作为当前流行的深度学习框架之一，提供了torch.nn.parallel模块，允许我们轻松地将模型部署到多个GPU上。本文将详细介绍如何使用这个模块，以及如何通过并行化来提高模型的计算效率。引言深度学
《昇思25天学习打卡营第1天|快速入门》一只IT攻城狮其他学习
昇思MindSpore介绍昇思MindSpore是一个全场景深度学习框架，旨在实现易开发、高效执行、全场景统一部署三大目标。其中，易开发表现为API友好、调试难度低；高效执行包括计算效率、数据预处理效率和分布式训练效率；全场景则指框架同时支持云、边缘以及端侧场景。实操本节通过MindSpore的API来快速实现一个简单的深度学习模型。MindSpore提供基于Pipeline的数据引擎，通过数据集
AI深度学习项目-yolo4_tiny 垃圾分类识别系统毕设宇航 yolov4 垃圾识别 QQ767172261
项目概述目标本项目旨在开发一个高效的垃圾分类识别系统，利用深度学习技术特别是YOLOv4-tiny版本来实现垃圾的自动分类。YOLOv4-tiny作为YOLOv4的一个轻量化版本，在保证较高精度的同时，能够提供更快的检测速度，非常适合资源受限的设备或者要求实时性的应用场景。技术栈深度学习框架：PyTorch目标检测算法：YOLOv4-tiny编程语言：Python硬件加速：GPU（如果可用）功能特
【pytorch】TensorBoard的使用 hhhhhhkkkyyy pytorch 人工智能 python
TensorBoardTensorBoard是TensorFlow提供的一个可视化工具，用于实时监控、调试和可视化深度学习模型的训练过程和性能指标。虽然它是为TensorFlow设计的，但也可以与其他深度学习框架（如PyTorch）一起使用。下面是一些关于TensorBoard的详细知识和使用方法：可视化功能：Scalars（标量）：用于显示训练过程中的标量数据，比如损失和准确率的变化趋势。Gra
动手学深度学习（pytorch）学习记录20-自定义层[学习记录] walfar pytorch 深度学习 pytorch 学习
在深度学习中，自定义层是指开发者根据特定需求编写的神经网络层，而不是使用深度学习框架（如PyTorch、TensorFlow等）提供的现成层。自定义层可以让模型更加灵活，以适应特定的任务或数据集。目录没有参数的自定义层带参数的层没有参数的自定义层下面的CenteredLayer类要从其输入中减去均值。要构建它，只需继承基础层类并实现前向传播功能。importtorchimporttorch.nn.
释放GPU潜能：PyTorch中torch.nn.DataParallel的数据并行实践 2401_85762266 pytorch 人工智能 python
释放GPU潜能：PyTorch中torch.nn.DataParallel的数据并行实践在深度学习模型的训练过程中，计算资源的需求往往随着模型复杂度的提升而增加。PyTorch，作为当前领先的深度学习框架之一，提供了torch.nn.DataParallel这一工具，使得开发者能够利用多个GPU进行数据并行处理，从而显著加速模型训练。本文将详细介绍如何在PyTorch中使用torch.nn.Dat
并行处理的魔法：PyTorch中torch.multiprocessing的多进程训练指南 liuxin33445566 人工智能深度学习机器学习
并行处理的魔法：PyTorch中torch.multiprocessing的多进程训练指南在深度学习领域，模型训练往往需要大量的计算资源和时间。PyTorch，作为当前最流行的深度学习框架之一，提供了torch.multiprocessing模块，使得开发者能够利用多核CPU进行多进程训练，从而显著加速训练过程。本文将深入探讨如何在PyTorch中使用torch.multiprocessing进行
并行计算的艺术：PyTorch中torch.cuda.nccl的多GPU通信精粹 2401_85763639 pytorch 人工智能 python
并行计算的艺术：PyTorch中torch.cuda.nccl的多GPU通信精粹在深度学习领域，模型的规模和复杂性不断增长，单GPU的计算能力已难以满足需求。多GPU并行计算成为提升训练效率的关键。PyTorch作为灵活且强大的深度学习框架，通过torch.cuda.nccl模块提供了对NCCL（NVIDIACollectiveCommunicationsLibrary）的支持，为多GPU通信提供
如何本地搭建 Whisper 语音识别模型？一文解决玩AI的小胡子 whisper AIGC 人工智能语音识别
Whisper是OpenAI开发的强大语音识别模型，适用于多种语言的语音转文字任务。要在本地搭建Whisper模型，需要完成以下几个步骤，确保模型在你的设备上顺利运行。1.准备环境首先，确保你的系统上安装了Python（版本3.8到3.11之间）。此外，还需要安装PyTorch，这是Whisper依赖的深度学习框架。2.安装Whisper在命令行中运行以下命令来安装Whisper和其依赖项：pip
精准掌控GPU：深度学习中PyTorch的torch.cuda.device应用指南 2401_85760095 深度学习 pytorch 人工智能
精准掌控GPU：深度学习中PyTorch的torch.cuda.device应用指南在深度学习的世界里，GPU加速已成为提升模型训练和推理速度的关键。PyTorch，作为当下最流行的深度学习框架之一，提供了torch.cuda.device这一强大的工具，允许开发者精确指定和控制GPU设备。本文将深入探讨如何在PyTorch中使用torch.cuda.device来指定GPU设备，优化你的深度学习
打手机检测算法源码样本展示打手机检测算法实际应用场景介绍 LNTON羚通算法智能手机算法大数据安全数据库音视频人工智能
打手机检测算法是一种利用计算机视觉技术来监测和识别人们在特定区域如驾驶舱、考场或其他敏感区域非法使用手机的行为。这种算法对于提高安全性和确保规则的遵守具有重要意义。以下是关于打手机检测算法源码及其实际应用的详细阐述：1.算法实现-深度学习框架：打手机检测算法通常采用卷积神经网络(CNN)等深度学习模型，这些模型能够从图像或视频中提取使用手机时的视觉特征。-数据集准备：为了训练这样的模型，需要大量的
python深度学习框架——TensorFlow 零度° python python tensorflow
TensorFlow，由Google开发的开源机器学习库，以其强大的功能和灵活性，在深度学习、计算机视觉和自然语言处理等领域发挥着重要作用。本文将深入探讨TensorFlow中的一些常用函数及其参数。TensorFlow核心概念在TensorFlow中，**张量（Tensor）**是基本的数据单元，可以视为多维数组。**计算图（ComputationGraph）是由节点（代表数学运算）和边（代表张
人工智能开源库有哪些 openwin_top 人工智能人工智能开源 python
TensorFlow：由Google开发的深度学习库，提供了丰富的工具和API，支持CPU和GPU计算。PyTorch：由Facebook开发的深度学习框架，提供动态图和静态图两种模式，并且易于使用。Keras：基于TensorFlow、Theano和CNTK等深度学习库的高级API，可帮助用户快速构建神经网络。Scikit-learn：用Python编写的机器学习库，提供了许多常见的机器学习算法
Dom 周华华 JavaScript html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml&q
【Spark九十六】RDD API之combineByKey bit1129 spark
1. combineByKey函数的运行机制 RDD提供了很多针对元素类型为(K,V)的API，这些API封装在PairRDDFunctions类中，通过Scala隐式转换使用。这些API实现上是借助于combineByKey实现的。combineByKey函数本身也是RDD开放给Spark开发人员使用的API之一首先看一下combineByKey的方法说明：
msyql设置密码报错：ERROR 1372 (HY000): 解决方法详解 daizj mysql 设置密码
MySql给用户设置权限同时指定访问密码时，会提示如下错误： ERROR 1372 (HY000): Password hash should be a 41-digit hexadecimal number；问题原因：你输入的密码是明文。不允许这么输入。解决办法：用select password('你想输入的密码');查询出你的密码对应的字符串，然后
路漫漫其修远兮吾将上下而求索周凡杨学习思索
王国维在他的《人间词话》中曾经概括了为学的三种境界古今之成大事业、大学问者，罔不经过三种之境界。“昨夜西风凋碧树。独上高楼，望尽天涯路。”此第一境界也。“衣带渐宽终不悔，为伊消得人憔悴。”此第二境界也。“众里寻他千百度，蓦然回首，那人却在灯火阑珊处。”此第三境界也。学习技术，这也是你必须经历的三种境界。第一层境界是说，学习的路是漫漫的，你必须做好充分的思想准备，如果半途而废还不如不要开始。这里，注
Hadoop(二)对话单的操作朱辉辉33 hadoop
Debug： 1、 A = LOAD '/user/hue/task.txt' USING PigStorage(' ') AS (col1,col2,col3); DUMP A; //输出结果前几行示例： (>ggsnPDPRecord(21),,) (-->recordType(0),,) (-->networkInitiation(1),,)
web报表工具FineReport常用函数的用法总结（日期和时间函数）老A不折腾 finereport 报表工具 web开发
web报表工具FineReport常用函数的用法总结（日期和时间函数）说明：凡函数中以日期作为参数因子的，其中日期的形式都必须是yy/mm/dd。而且必须用英文环境下双引号(" ")引用。 DATE DATE(year,month,day):返回一个表示某一特定日期的系列数。 Year:代表年，可为一到四位数。 Month:代表月份。
c++ 宏定义中的##操作符墙头上一根草 C++
#与##在宏定义中的--宏展开 #include <stdio.h> #define f(a,b) a##b #define g(a) #a #define h(a) g(a) int main() { &nbs
分析Spring源代码之，DI的实现 aijuans spring DI 现源代码
(转) 分析Spring源代码之，DI的实现 2012/1/3 by tony 接着上次的讲，以下这个sample [java] view plain copy print
for循环的进化 alxw4616 JavaScript
// for循环的进化 // 菜鸟 for (var i = 0; i < Things.length ; i++) { // Things[i] } // 老鸟 for (var i = 0, len = Things.length; i < len; i++) { // Things[i] } // 大师 for (var i = Things.le
网络编程Socket和ServerSocket简单的使用百合不是茶网络编程基础 IP地址端口
网络编程;TCP/IP协议网络:实现计算机之间的信息共享,数据资源的交换协议:数据交换需要遵守的一种协议,按照约定的数据格式等写出去端口:用于计算机之间的通信每运行一个程序，系统会分配一个编号给该程序，作为和外界交换数据的唯一标识 0~65535 查看被使用的
JDK1.5 生产消费者 bijian1013 java thread 生产消费者 java多线程
ArrayBlockingQueue：一个由数组支持的有界阻塞队列。此队列按 FIFO（先进先出）原则对元素进行排序。队列的头部是在队列中存在时间最长的元素。队列的尾部是在队列中存在时间最短的元素。新元素插入到队列的尾部，队列检索操作则是从队列头部开始获得元素。 ArrayBlockingQueue的常用方法：
JAVA版身份证获取性别、出生日期及年龄 bijian1013 java 性别出生日期年龄
工作中需要根据身份证获取性别、出生日期及年龄，且要还要支持15位长度的身份证号码，网上搜索了一下，经过测试好像多少存在点问题，干脆自已写一个。 CertificateNo.java package com.bijian.study; import java.util.Calendar; import
【Java范型六】范型与枚举 bit1129 java
首先，枚举类型的定义不能带有类型参数，所以，不能把枚举类型定义为范型枚举类，例如下面的枚举类定义是有编译错的 public enum EnumGenerics<T> { //编译错，提示枚举不能带有范型参数 OK, ERROR; public <T> T get(T type) { return null;
【Nginx五】Nginx常用日志格式含义 bit1129 nginx
1. log_format 1.1 log_format指令用于指定日志的格式，格式： log_format name(格式名称) type(格式样式) 1.2 如下是一个常用的Nginx日志格式： log_format main '[$time_local]|$request_time|$status|$body_bytes
Lua 语言 15 分钟快速入门 ronin47 lua 基础
- - 单行注释 - - [[ [多行注释] - - ]] - - - - - - - - - - - 1. 变量 & 控制流 - - - - - - - - - - num = 23 - - 数字都是双精度 str = 'aspythonstring'
java-35.求一个矩阵中最大的二维矩阵 ( 元素和最大 ) bylijinnan java
the idea is from: http://blog.csdn.net/zhanxinhang/article/details/6731134 public class MaxSubMatrix { /**see http://blog.csdn.net/zhanxinhang/article/details/6731134 * Q35 求一个矩阵中最大的二维
mongoDB文档型数据库特点开窍的石头 mongoDB文档型数据库特点
MongoDD: 文档型数据库存储的是Bson文档-->json的二进制特点：内部是执行引擎是js解释器，把文档转成Bson结构，在查询时转换成js对象。 mongoDB传统型数据库对比传统类型数据库：结构化数据，定好了表结构后每一个内容符合表结构的。也就是说每一行每一列的数据都是一样的文档型数据库：不用定好数据结构，
[毕业季节]欢迎广大毕业生加入JAVA程序员的行列 comsci java
一年一度的毕业季来临了。。。。。。。。正在投简历的学弟学妹们。。。如果觉得学校推荐的单位和公司不适合自己的兴趣和专业，可以考虑来我们软件行业，做一名职业程序员。。。软件行业的开发工具中，对初学者最友好的就是JAVA语言了，网络上不仅仅有大量的
PHP操作Excel – PHPExcel 基本用法详解 cuiyadll PHP Excel
导出excel属性设置//Include classrequire_once('Classes/PHPExcel.php');require_once('Classes/PHPExcel/Writer/Excel2007.php');$objPHPExcel = new PHPExcel();//Set properties 设置文件属性$objPHPExcel->getProperties
IBM Webshpere MQ Client User Issue (MCAUSER) darrenzhu IBM jms user MQ MCAUSER
IBM MQ JMS Client去连接远端MQ Server的时候，需要提供User和Password吗？答案是根据情况而定，取决于所定义的Channel里面的属性Message channel agent user identifier (MCAUSER)的设置。 http://stackoverflow.com/questions/20209429/how-mca-user-i
网线的接法 dcj3sjt126com
一、PC连HUB (直连线)A端：（标准568B）：白橙，橙，白绿，蓝，白蓝，绿，白棕，棕。 B端：（标准568B）：白橙，橙，白绿，蓝，白蓝，绿，白棕，棕。二、PC连PC （交叉线）A端：(568A)：白绿，绿，白橙，蓝，白蓝，橙，白棕，棕； B端：（标准568B）：白橙，橙，白绿，蓝，白蓝，绿，白棕，棕。三、HUB连HUB&nb
Vimium插件让键盘党像操作Vim一样操作Chrome dcj3sjt126com chrome vim
什么是键盘党？键盘党是指尽可能将所有电脑操作用键盘来完成，而不去动鼠标的人。鼠标应该说是新手们的最爱，很直观，指哪点哪，很听话！不过常常使用电脑的人，如果一直使用鼠标的话，手会发酸，因为操作鼠标的时候，手臂不是在一个自然的状态，臂肌会处于绷紧状态。而使用键盘则双手是放松状态，只有手指在动。而且尽量少的从鼠标移动到键盘来回操作，也省不少事。在chrome里安装 vimium 插件
MongoDB查询（2）——数组查询[六] eksliang mongodb MongoDB查询数组
MongoDB查询数组转载请出自出处：http://eksliang.iteye.com/blog/2177292 一、概述 MongoDB查询数组与查询标量值是一样的，例如，有一个水果列表，如下所示： > db.food.find() { "_id" : "001", "fruits" : [ "苹
cordova读写文件（1） gundumw100 JavaScript Cordova
使用cordova可以很方便的在手机sdcard中读写文件。首先需要安装cordova插件：file 命令为： cordova plugin add org.apache.cordova.file 然后就可以读写文件了，这里我先是写入一个文件，具体的JS代码为： var datas=null;//datas need write var directory=&
HTML5 FormData 进行文件jquery ajax 上传到又拍云 ileson jquery Ajax html5 FormData
html5 新东西：FormData 可以提交二进制数据。页面test.html <!DOCTYPE> <html> <head> <title> formdata file jquery ajax upload</title> </head> <body> <
swift appearanceWhenContainedIn:(version1.2 xcode6.4) 啸笑天 version
swift1.2中没有oc中对应的方法： + (instancetype)appearanceWhenContainedIn:(Class <UIAppearanceContainer>)ContainerClass, ... NS_REQUIRES_NIL_TERMINATION; 解决方法：在swift项目中新建oc类如下： #import &
java实现SMTP邮件服务器 macroli java 编程
电子邮件传递可以由多种协议来实现。目前，在Internet 网上最流行的三种电子邮件协议是SMTP、POP3 和 IMAP，下面分别简单介绍。　　◆ SMTP 协议　　简单邮件传输协议(Simple Mail Transfer Protocol,SMTP)是一个运行在TCP/IP之上的协议，用它发送和接收电子邮件。SMTP 服务器在默认端口25上监听。SMTP客户使用一组简单的、基于文本的
mongodb group by having where 查询sql qiaolevip 每天进步一点点学习永无止境 mongo 纵观千象
SELECT cust_id, SUM(price) as total FROM orders WHERE status = 'A' GROUP BY cust_id HAVING total > 250 db.orders.aggregate( [ { $match: { status: 'A' } }, { $group: {
Struts2 Pojo（六） Luob. POJO strust2
注意：附件中有完整案例 1.采用POJO对象的方法进行赋值和传值 2.web配置 <?xml version="1.0" encoding="UTF-8"?> <web-app version="2.5" xmlns="http://java.sun.com/xml/ns/javaee&q
struts2步骤 wuai struts
1、添加jar包 2、在web.xml中配置过滤器 <filter> <filter-name>struts2</filter-name> <filter-class>org.apache.st