- Trending Categories
- Data Structure
- Operating System
- C Programming
- 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 be used with keras.Model to track the variables defined using sequential model?
Tensorflow can be used to create a model that tracks internal layers by creating a sequential model and using this model to call ‘tf.zeros’ method.
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.
print("It tracks internal layers") my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1), input_shape=(None, None, 3)), tf.keras.layers.BatchNormalization(), tf.keras.layers.Conv2D(2, 1, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.Conv2D(3, (1, 1)), tf.keras.layers.BatchNormalization()]) my_seq(tf.zeros([1, 2, 3, 3])) print("The architecture of the model is") my_seq.summary()
It tracks internal layers The architecture of the model is Model: "sequential_4" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_3 (Conv2D) (None, None, None, 1) 4 _________________________________________________________________ batch_normalization_3 (Batch (None, None, None, 1) 4 _________________________________________________________________ conv2d_4 (Conv2D) (None, None, None, 2) 4 _________________________________________________________________ batch_normalization_4 (Batch (None, None, None, 2) 8 _________________________________________________________________ conv2d_5 (Conv2D) (None, None, None, 3) 9 _________________________________________________________________ batch_normalization_5 (Batch (None, None, None, 3) 12 ================================================================= Total params: 41 Trainable params: 29 Non-trainable params: 12 _________________________________________________________________
Many times, models that have many layers usually call one layer after the other.
This is done using tf.keras.Sequential.
- How can Tensorflow be used to create a sequential model using Python?
- How can Tensorflow be used with abalone dataset to build a sequential model?
- How can a sequential model be built on Auto MPG using TensorFlow?
- How can Tensorflow be used with Estimator to compile the model using Python?
- How can Tensorflow be used with Estimators to evaluate the model using Python?
- How can a sequential model be created incrementally with Tensorflow in 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 with Estimators to optimize the model?
- How can a sequential model be built on Auto MPG dataset using TensorFlow?
- How can Tensorflow be used to download the flower dataset using keras sequential API?
- How can Tensorflow be used to explore the flower dataset using keras sequential API?
- How can Tensorflow be used to export the model built using Python?
- How can Tensorflow be used to export the built model using Python?
- How can Tensorflow be used to compile the exported model using Python?