Demonstrate a basic implementation of ‘tf.keras.layers.Dense’ in Python

Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes.

The ‘tensorflow’ package can be installed on Windows using the below line of code −

pip install tensorflow

Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but a multidimensional array or a list.

Keras is a deep learning API, which is written in Python. It is a high-level API that has a productive interface that helps solve machine learning problems. It runs on top of the Tensorflow framework. It was built to help experiment in a quick manner. It provides essential abstractions and building blocks that are essential in developing and encapsulating machine learning solutions.

Keras is already present within the Tensorflow package. It can be accessed using the below line of code.

import tensorflow
from tensorflow import keras

The Keras functional API helps create models that are more flexible in comparison to models created using sequential API. The functional API can work with models that have non-linear topology, can share layers and work with multiple inputs and outputs. A deep learning model is usually a directed acyclic graph (DAG) that contains multiple layers. The functional API helps build the graph of layers.

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. Following is the code snippet −

Example

class CustomDense(layers.Layer):
   def __init__(self, units=32):
      super(CustomDense, self).__init__()
      self.units = units
   def build(self, input_shape):
      self.w = self.add_weight(
         shape=(input_shape[-1], self.units),
         initializer="random_normal",
         trainable=True,
      )
      self.b = self.add_weight(
         shape=(self.units,), initializer="random_normal", trainable=True
      )
   def call(self, inputs):
      return tf.matmul(inputs, self.w) + self.b
inputs = keras.Input((4,))
outputs = CustomDense(10)(inputs)
print("Keras model is being generated")
model = keras.Model(inputs, outputs)

Code credit − https://www.tensorflow.org/guide/keras/functional

Output

Keras model is being generated

Explanation

• Keras comes with multiple built-in layers, and some of them include ‘Conv1D’, ‘Conv2D’, ‘Conv2DTranspose’, and so on.

• The ‘call’ method specifies computation that is performed by the layer.

• The ‘build’ method creates weights for the layer.

• The model is generated.