Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How can Tensorflow and pre-trained model be used to chain data augmentation, rescaling and base model?
Tensorflow and the pre-trained model can be used to chain data augmentation, rescaling and base model by first defining the size of the input. Once this is done, the previously defined functions namely ‘data_augmentation’, ‘base_model’ are called by passing the relevant output.
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("Chaining together data augmentation, rescaling, base_model and feature extractor layers")
inputs = tf.keras.Input(shape=(160, 160, 3))
x = data_augmentation(inputs)
x = preprocess_input(x)
x = base_model(x, training=False)
x = global_average_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)
model = tf.keras.Model(inputs, outputs)Output
Chaining together data augmentation, rescaling, base_model and feature extractor layers
Explanation
A model is built by chaining data augmentation, rescaling, base_model and feature extractor layers.
This is done with the help of the Keras Functional API.
The training=False is used since the model contains a BatchNormalization layer.
181 Views
