TensorFlow 1.x Deep Learning Cookbook

How to do it...

We can proceed with the recipe as follows:

1. Import the pre-built models and additional modules needed for processing:

from keras.applications.inception_v3 import InceptionV3from keras.preprocessing import imagefrom keras.models import Modelfrom keras.layers import Dense, GlobalAveragePooling2Dfrom keras import backend as K# create the base pre-trained modelbase_model = InceptionV3(weights='imagenet', include_top=False)

2.  We use a trained Inception-v3 but we do not include the top model because we want to fine-tune on D. The top level is a Dense layer with 1,024 inputs and the last output level is a softmax Dense layer with 200 classes of output. x = GlobalAveragePooling2D()(x) is used to convert the input to the correct shape ...