- Trending Categories
- Data Structure
- Networking
- RDBMS
- Operating System
- Java
- iOS
- HTML
- CSS
- Android
- Python
- C Programming
- C++
- C#
- MongoDB
- MySQL
- Javascript
- PHP

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

Tensorflow can be used to decode the predictions by converting the image to a Numpy array.

**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 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.

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.

print("Decoding the predictions") labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt') imagenet_labels = np.array(open(labels_path).read().splitlines()) plt.imshow(grace_hopper) plt.axis('off') predicted_class_name = imagenet_labels[predicted_class] _ = plt.title("Prediction is: " + predicted_class_name.title())

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

Decoding the predictions Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt 16384/10484 [==============================================] - 0s 0us/step

The predicted class ID can be used to fetch the ImageNet labels to decode the predictions.

The predicted data/image is displayed on the console.

- Related Questions & Answers
- How can Tensorflow be used to check the predictions using Python?
- How can TensorFlow be used to verify the predictions for Fashion MNIST in Python?
- How can TensorFlow be used to make predictions for Fashion MNIST dataset in Python?
- How can Tensorflow be used with Estimator to make predictions from trained model?
- How can Tensorflow be used to visualize the data using Python?
- How can Tensorflow be used to standardize the data using Python?
- How can Tensorflow be used to compile the model using Python?
- How can Tensorflow be used to train the model using Python?
- How can Tensorflow be used to check the predicrion using Python?
- How can Tensorflow be used to plot the results using Python?
- How can predictions be made on Auto MPG dataset using TensorFlow?
- How can Tensorflow be used to compose layers using Python?
- How can Tensorflow be used to export the model built using Python?
- How can Tensorflow be used to load the Illiad dataset using Python?
- How can Tensorflow be used to train the Illiad dataset using Python?

Advertisements