打印tensorflow结构

代码：

import tensorflow as tf

sess=tf.Session
with tf.Graph().as_default():
    with tf.gfile.FastGFile('./models/emotion_model.pb', 'rb') as modelfile:
        graph_def=tf.GraphDef()
        graph_def.ParseFromString(modelfile.read())
        tf.import_graph_def(graph_def)
        [print(n.name) for n in tf.get_default_graph().as_graph_def().node]

结果：

Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 256, 256, 3)  0                                            
__________________________________________________________________________________________________
block1_conv1 (Conv2D)           (None, 127, 127, 32) 864         input_1[0][0]                    
__________________________________________________________________________________________________
block1_conv1_bn (BatchNormaliza (None, 127, 127, 32) 128         block1_conv1[0][0]               
__________________________________________________________________________________________________
block1_conv1_act (Activation)   (None, 127, 127, 32) 0           block1_conv1_bn[0][0]            
__________________________________________________________________________________________________
block1_conv2 (Conv2D)           (None, 125, 125, 64) 18432       block1_conv1_act[0][0]           
__________________________________________________________________________________________________
block1_conv2_bn (BatchNormaliza (None, 125, 125, 64) 256         block1_conv2[0][0]               
__________________________________________________________________________________________________
block1_conv2_act (Activation)   (None, 125, 125, 64) 0           block1_conv2_bn[0][0]            
__________________________________________________________________________________________________
block2_sepconv1 (SeparableConv2 (None, 125, 125, 128 8768        block1_conv2_act[0][0]           
__________________________________________________________________________________________________
block2_sepconv1_bn (BatchNormal (None, 125, 125, 128 512         block2_sepconv1[0][0]            
__________________________________________________________________________________________________
block2_sepconv2_act (Activation (None, 125, 125, 128 0           block2_sepconv1_bn[0][0]         
__________________________________________________________________________________________________
block2_sepconv2 (SeparableConv2 (None, 125, 125, 128 17536       block2_sepconv2_act[0][0]        
__________________________________________________________________________________________________
block2_sepconv2_bn (BatchNormal (None, 125, 125, 128 512         block2_sepconv2[0][0]            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 63, 63, 128)  8192        block1_conv2_act[0][0]           
__________________________________________________________________________________________________
block2_pool (MaxPooling2D)      (None, 63, 63, 128)  0           block2_sepconv2_bn[0][0]         
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 63, 63, 128)  512         conv2d_1[0][0]                   
__________________________________________________________________________________________________
add_1 (Add)                     (None, 63, 63, 128)  0           block2_pool[0][0]                
                                                                 batch_normalization_1[0][0]      
__________________________________________________________________________________________________
block3_sepconv1_act (Activation (None, 63, 63, 128)  0           add_1[0][0]                      
__________________________________________________________________________________________________
block3_sepconv1 (SeparableConv2 (None, 63, 63, 256)  33920       block3_sepconv1_act[0][0]        
__________________________________________________________________________________________________
block3_sepconv1_bn (BatchNormal (None, 63, 63, 256)  1024        block3_sepconv1[0][0]            
__________________________________________________________________________________________________
block3_sepconv2_act (Activation (None, 63, 63, 256)  0           block3_sepconv1_bn[0][0]         
__________________________________________________________________________________________________
block3_sepconv2 (SeparableConv2 (None, 63, 63, 256)  67840       block3_sepconv2_act[0][0]        
__________________________________________________________________________________________________
block3_sepconv2_bn (BatchNormal (None, 63, 63, 256)  1024        block3_sepconv2[0][0]            
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 32, 32, 256)  32768       add_1[0][0]                      
__________________________________________________________________________________________________
block3_pool (MaxPooling2D)      (None, 32, 32, 256)  0           block3_sepconv2_bn[0][0]         
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 32, 32, 256)  1024        conv2d_2[0][0]                   
__________________________________________________________________________________________________
add_2 (Add)                     (None, 32, 32, 256)  0           block3_pool[0][0]