# How can Tensorflow be used to create a feature extractor using Python?

Tensorflow can be used to create a feature extractor by using buffered prefetching. It is done by setting trainable=False.

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.

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.

TensorFlow Hub is a repository that contains pre-trained TensorFlow models. TensorFlow can be used to fine-tune learning models.

We will understand how to use models from TensorFlow Hub with tf.keras, use an image classification model from TensorFlow Hub.  Once this is done, transfer learning can be performed to fine-tune a model for customized image classes. This is done by using a pretrained classifier model to take an image and predict what it is. This can be done without needing any training.

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

AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
print("The dimensions of data")
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break

## Output

The dimensions of data
(32, 224, 224, 3)
(32,)

## Explanation

• TFHub distributes models without needing the top classification layer.
• They can be used for transfer learning.
• Any compatible image feature vector model from tfhub.dev can be used.
• A feature extractor can be created, with the help of trainable=False.
• This can be used to freeze the variables in the feature extractor layer.
• This is done so that the training modifies the new classifier layer only.
• It will return a 1280-length vector for each image.