
# coding: utf-8

# https://keras.io/applications/

# In[1]:

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
   print(i, layer.name)


# In[4]:

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in base_model.layers[:249]:
   print(layer.name)


# In[5]:

for layer in base_model.layers[249:]:
   print(layer.name)


# In[ ]:




d152ba25bcf85fa76ce4acdf66f7081eb8249387
<mask token>

<mask token>
for i, layer in enumerate(base_model.layers):
    print(i, layer.name)
for layer in base_model.layers[:249]:
    print(layer.name)
for layer in base_model.layers[249:]:
    print(layer.name)

<mask token>
base_model = InceptionV3(weights='imagenet', include_top=False)
for i, layer in enumerate(base_model.layers):
    print(i, layer.name)
for layer in base_model.layers[:249]:
    print(layer.name)
for layer in base_model.layers[249:]:
    print(layer.name)

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
base_model = InceptionV3(weights='imagenet', include_top=False)
for i, layer in enumerate(base_model.layers):
    print(i, layer.name)
for layer in base_model.layers[:249]:
    print(layer.name)
for layer in base_model.layers[249:]:
    print(layer.name)


# coding: utf-8

# https://keras.io/applications/

# In[1]:

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
   print(i, layer.name)


# In[4]:

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in base_model.layers[:249]:
   print(layer.name)


# In[5]:

for layer in base_model.layers[249:]:
   print(layer.name)


# In[ ]:



