How can Tensorflow be used to implement custom layers?

Tensorflow can be used to implement custom layers by creating a class and defining a function to build the layers, and defining another function to call the matrix multiplication by passing the input to it.

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. 

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

print("Implementing custom layers")
class MyDenseLayer(tf.keras.layers.Layer):
   def __init__(self, num_outputs):
      super(MyDenseLayer, self).__init__()
      self.num_outputs = num_outputs
   def build(self, input_shape):
      self.kernel = self.add_weight("kernel",
      shape=[int(input_shape[-1]),
      self.num_outputs])
def call(self, input):
return tf.matmul(input, self.kernel)

Code credit −https://www.tensorflow.org/tutorials/customization/custom_layers

Output

Implementing custom layers

Explanation

• The customized layers are implemented.
• This is done by creating a class and extending it to the ‘tf.keras.layer’.
• __init__ helps perform an all input-independent initialization.
• The build method can be used to know the shapes of the input tensors and complete the initialization process.
• The call method helps with forward computation.