keras实现调用自己训练的模型,并去掉全连接层


Posted in Python onJune 09, 2020

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
__________________________________________________________________________________________________

以上这篇keras实现调用自己训练的模型,并去掉全连接层就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持三水点靠木。

Python 相关文章推荐
python创建只读属性对象的方法(ReadOnlyObject)
Feb 10 Python
Python获取DLL和EXE文件版本号的方法
Mar 10 Python
Python导出DBF文件到Excel的方法
Jul 25 Python
Python基于递归算法求最小公倍数和最大公约数示例
Jul 27 Python
python根据url地址下载小文件的实例
Dec 18 Python
对Xpath 获取子标签下所有文本的方法详解
Jan 02 Python
python实现淘宝秒杀脚本
Jun 23 Python
Python中一些深不见底的“坑”
Jun 12 Python
解决Django连接db遇到的问题
Aug 29 Python
Python 实现文件读写、坐标寻址、查找替换功能
Sep 11 Python
DjangoWeb使用Datatable进行后端分页的实现
May 18 Python
Python 中的Sympy详细使用
Aug 07 Python
Python基于os.environ从windows获取环境变量
Jun 09 #Python
新手学习Python2和Python3中print不同的用法
Jun 09 #Python
Python基于wordcloud及jieba实现中国地图词云图
Jun 09 #Python
Python中的__init__作用是什么
Jun 09 #Python
python小白学习包管理器pip安装
Jun 09 #Python
Python小白垃圾回收机制入门
Jun 09 #Python
Python中如何添加自定义模块
Jun 09 #Python
You might like
BBS(php & mysql)完整版(八)
2006/10/09 PHP
php常用的安全过滤函数集锦
2014/10/09 PHP
thinkPHP学习笔记之安装配置篇
2015/03/05 PHP
thinkphp autoload 命名空间自定义 namespace
2015/07/17 PHP
基于jQuery的让非HTML5浏览器支持placeholder属性的代码
2011/05/24 Javascript
javascript获取隐藏元素(display:none)的高度和宽度的方法
2014/06/06 Javascript
js的[defer]和[async]属性
2014/11/24 Javascript
浅谈javascript中自定义模版
2015/01/29 Javascript
Jquery操作cookie记住用户名
2016/03/29 Javascript
jquery使用on绑定a标签无效 只能用live解决
2016/06/02 Javascript
利用jquery实现瀑布流3种案例
2016/09/18 Javascript
很棒的vue弹窗组件
2017/05/24 Javascript
nodejs实现大文件(在线视频)的读取
2020/10/16 NodeJs
AngularJS基于MVC的复杂操作实例讲解
2017/12/31 Javascript
extjs图形绘制之饼图实现方法分析
2020/03/06 Javascript
[01:17]辉夜杯战队访谈宣传片—EHOME
2015/12/25 DOTA
python网络爬虫采集联想词示例
2014/02/11 Python
Python高级应用实例对比:高效计算大文件中的最长行的长度
2014/06/08 Python
Python爬虫框架Scrapy实战之批量抓取招聘信息
2015/08/07 Python
基于Python实现对PDF文件的OCR识别
2016/08/05 Python
Python使用正则表达式实现文本替换的方法
2017/04/18 Python
windows上彻底删除jupyter notebook的实现
2020/04/13 Python
PageFactory设计模式基于python实现
2020/04/14 Python
Python实现列表中非负数保留,负数转化为指定的数值方式
2020/06/04 Python
配置H5的滚动条样式的示例代码
2018/03/09 HTML / CSS
Nike比利时官网:Nike.com (BE)
2019/02/07 全球购物
意大利咖啡、浓缩咖啡和浓缩咖啡机:illy caffe
2019/03/20 全球购物
什么是抽象
2015/12/13 面试题
绘画设计学生的个人自我评价
2013/09/20 职场文书
就业自我评价
2014/02/04 职场文书
党的群众路线教育实践活动心得体会900字
2014/03/07 职场文书
党员2014两会学习心得体会
2014/03/17 职场文书
旅游与环境专业求职信
2014/06/05 职场文书
企业百日安全活动总结
2015/05/07 职场文书
Docker 镜像介绍以及commit相关操作
2022/04/13 Servers
MySQL索引失效场景及解决方案
2022/07/23 MySQL