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keras vgg19 64x64と128x128と224x224のときの違い

from keras.preprocessing import image
import keras.applications.vgg19 as vgg19

model = vgg19.VGG19(weights=None, input_shape=(64, 64, 3))
model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         (None, 64, 64, 3)         0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    
_________________________________________________________________
block3_conv4 (Conv2D)        (None, 16, 16, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   
_________________________________________________________________
block4_conv4 (Conv2D)        (None, 8, 8, 512)         2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_conv4 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 2048)              0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              8392704   
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 1000)              4097000   
=================================================================
Total params: 49,295,400
Trainable params: 49,295,400
Non-trainable params: 0
_________________________________________________________________

f:id:none53:20190416134227p:plain

20190416_vgg19_models.ipynb