Keras transfer learning

inputs = keras.Input(shape=(150, 150, 3))
# We make sure that the base_model is running in inference mode here,
# by passing `training=False`. This is important for fine-tuning, as you will
# learn in a few paragraphs.
x = base_model(inputs, training=False)
# Convert features of shape `base_model.output_shape[1:]` to vectors
# Use golbal averaging pool instead of flatten in some cases
x = keras.layers.GlobalAveragePooling2D()(x)
# A Dense classifier with a single unit (binary classification)
outputs = keras.layers.Dense(1)(x)
model = keras.Model(inputs, outputs)

Source: keras.io

Similar codes

4 votes215 views1 answer
Orderd set in python
Python
0 vote236 views1 answer
Django model specify table name
Python Django
1 vote236 views1 answer
Discord.py say something
Python
14 votes238 views6 answers
Python print variable
Python
1 vote283 views1 answer
Check datatype python
Python
0 vote226 views1 answer
Pytesseract restrict char
Python
7 votes250 views4 answers
Install python in mac
Python
0 vote222 views1 answer
Opencv export image
Python
0 vote219 views6 answers
Gcd
Python
5 votes236 views3 answers
Stop function python
Python