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Introduction to TensorFlow Layers

The following article provides an outline for TensorFlow Layers. TensorFlow’s tf$layers module provides a high-level API for quickly building a neural network. It includes tools for creating dense (completely linked) layers and convolutional layers and adding activation functions and dropout regularisation.

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What are TensorFlow layers?

Layer Description

Add Calculates element-by-element addition.

AvgPool Average pooling is given to the input data.

BatchToSpaceND Rearranges data from batch into blocks of spatial data

BiasAdd Adds bias

Const Creates a constant tensor

Conv2D Computes a 2-D convolution

Conv2DBackpropInput Reorganizes data from a batch into spatial data chunks.

Identity Calculates the convolution gradients concerning the source

Maximum Computes element-wise maximization.

MaxPool Mostly on input, MaxPool performs maximum pooling.

Mean The mean element is calculated with the dimensions.

Mul Computes element-wise multiplication

Neg Computes numerical negative value element-wise

Pad

Placeholder Inserts a placeholder for a tensor that will always be fed

Prod manipulates the product of elements across tensor

RandomUniform Outputs random values from a uniform distribution

Different Types include:

6. Drop out

Creating models with the Layers

Keras (tf.keras), a popular high-level neural network API that is concise, quick, and adaptable, is suggested for TensorFlow models. TensorFlow has made it official and fully supports it. Model and Layer are two fundamental notions in Keras. The layers encapsulate numerous computational tasks and variables (for example, fully connected layers, convolutional layers, pooling layers, and so on), whereas the model connects and encapsulates the layers overall, explaining how the input information is then passed through the layers and operations to achieve the result.

return result

An example

To demonstrate the model-building process in TensorFlow 2, we utilize the simplest multilayer perceptron (MLP), often known as a “multilayer fully connected neural network.” The following steps are taken in this part.

4. Process for evaluating a model. Calculate assessment indicators with tf.keras.metrics (e.g., accuracy)

MNIST image sample

So here, an MNIST loader is installed to read data from the datasets. The model takes a vector as input (in this case, a compressed 1784 handwritten digit image) and produces a 10-dimensional vector representing the likelihood that the image corresponds to one of the nine categories.

return result

Custom layers

A model’s building blocks are called layers. We can define a custom layer that interacts effectively with the other levels if the model performs a custom computation. We’ll create a custom layer that manipulates the sum of a cube as follows:

TensorFlow Layers Models

Models are determined in the open API technique by generating layers and correlating them in sets, then defining a Model that consists of the layers to act as the input and output. We can define the model layer by layer using the Keras API. A layer is just a tensor with its associated weights. Each layer accepts as an input a tensor value, which is the tensor supplied from the previous layer. Next, the layer’s internal operation performs a computation on the input tensor and the internal weight tensor. The final result is the resultant tensor, which is passed to the next layer in the network.

Keras model construction

There are two ways to create models with tf.keras:

1. Sequential API

We can use the sequential model if we have a most simple model in which each layer node is connected sequentially from the input layer to the output layer. This model has a continuous chain of layers from the source to the destination, and there are no layers with numerous inputs. A single input data and output are also required for this technique.

– add()

2. Functional API

model = Model([in1, in2], output_layer)

use for classification

TensorFlow is used to deploy a very easy neural network classifier. This model categorizes photographs of handwritten digits from the MNIST data set, which has ten classes.

TensorFlow layers Examples

Example

print(layer.name, layer)

Explanation

The above code builds a sequential model, and the model provides the necessary input. The last step is to increment all the layers in the model.

Output:

Conclusion

Finally, in this article, we had utilized the convolutional network in the classification. We have also built a Neural network using tensor flow for implementation. TensorFlow includes a Model class that we may use to create a model using the layers we had created.