How can Tensorflow be used to compile the model using Python?

The created model in Tensorflow can be compiled using the ‘compile’ method. The loss is calculated using the ‘SparseCategoricalCrossentropy’ method.

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

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.

print("The model is being compiled")
model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
   metrics=['accuracy'])
print("The architecture of the model")
model.summary()

Code credit: https://www.tensorflow.org/tutorials/images/classification

Output

The model is being compiled
The architecture of the model
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #  
=================================================================
rescaling_1 (Rescaling)      (None, 180, 180, 3)       0        
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 180, 180, 16)      448      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 90, 90, 16)        0        
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 90, 90, 32)        4640    
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 45, 45, 32)        0        
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 45, 45, 64)        18496    
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 22, 22, 64)        0        
_________________________________________________________________
flatten_1 (Flatten)          (None, 30976)             0        
_________________________________________________________________
dense_2 (Dense)              (None, 128)               3965056  
_________________________________________________________________
dense_3 (Dense)              (None, 5)                 645      
=================================================================
Total params: 3,989,285
Trainable params: 3,989,285
Non-trainable params: 0
_________________________________________________________________

Explanation

• The optimizers.Adam optimizer and losses.SparseCategoricalCrossentropy loss function is used.
• Thetraining and validation accuracy for every training epoch can be viewed by passing the metrics argument.
• Once the model is compiled, the summary of the architecture is displayed using the 'summary' method.