How can Tensorflow used with the pre-trained model to compile the model?

Tensorflow and the pre-trained model can be used to compile the model with the help of the ‘compile’ method. The loss is specified as ‘BinaryCrossentropy’ in the compile parameter.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. 

We will understand how to classify images of cats and dogs with the help of transfer learning from a pre-trained network. The intuition behind transfer learning for image classification is, if a model is trained on a large and general dataset, this model can be used to effectively serve as a generic model for the visual world. It would have learned the feature maps, which means the user won’t have to start from scratch by training a large model on a large dataset.

Read More: How can a customized model be pre-trained?

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

Example

print("The model is being compiled")
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
   optimizer = tf.keras.optimizers.RMSprop(lr=base_learning_rate/10),
   metrics=['accuracy'])
print("The architecture of the model")
model.summary()

Code credit −https://www.tensorflow.org/tutorials/images/transfer_learning

Output

The model is being compiled
The architecture of the model
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape           Param #
=================================================================
input_3 (InputLayer)     [(None, 160, 160, 3)]       0
_________________________________________________________________
sequential_1 (Sequential)  (None, 160, 160, 3)       0
_________________________________________________________________
tf.math.truediv (TFOpLambda) (None, 160, 160, 3)     0
_________________________________________________________________
tf.math.subtract (TFOpLambda (None, 160, 160, 3)     0
_________________________________________________________________
mobilenetv2_1.00_160 (Functi (None, 5, 5, 1280)   2257984
_________________________________________________________________
global_average_pooling2d_2  ( (None, 1280)           0
_________________________________________________________________
dropout (Dropout)     (None, 1280)                   0
_________________________________________________________________
dense_2 (Dense)            (None, 1)               1281
=================================================================
Total params: 2,259,265
Trainable params: 1,862,721
Non-trainable params: 396,544

Explanation

• The model is unfrozen, and then compiled.
• This is done using the ‘compile’ method.