Atrous Conv2D Transpose | TensorFlow | Tutorial

In this tutorial, we will be discussing about Atrous Conv2D Transpose in TensorFlow. Atrous Convolutional Transpose (also known as dilated transposed convolution) is a powerful tool in deep learning for upsampling feature maps with learnable parameters. This operation is especially useful in tasks like image segmentation and image generation where high resolution output is required.

What is Atrous Conv2D Transpose?

Atrous Conv2D Transpose is an extension of the standard transpose convolution operation in which the stride is replaced with atrous convolution. This means that the filter is applied to an intermediate feature map with holes, allowing it to capture larger receptive fields without losing spatial resolution.

Implementation in TensorFlow

To implement Atrous Conv2D Transpose in TensorFlow, you can use the tf.nn.conv2d_transpose function with dilation argument set to a non-zero value. Here is an example code snippet demonstrating how to use Atrous Conv2D Transpose in TensorFlow:

import tensorflow as tf

# Define the input tensor
input_tensor = tf.placeholder(tf.float32, shape=[None, height, width, channels])

# Define the atrous convolutional transpose layer
output = tf.nn.conv2d_transpose(input_tensor, filter, output_shape=[None, output_height, output_width, num_output_channels],strides=[1, stride, stride, 1], padding='SAME',dilations=[1, dilation, dilation, 1])

# Add activation function if needed
output = tf.nn.relu(output)

Conclusion

Atrous Conv2D Transpose is a useful operation in deep learning for upsampling feature maps with larger receptive fields. By using TensorFlow, you can easily implement Atrous Conv2D Transpose in your neural network architecture. Experiment with different dilation rates and see how it affects the performance of your model.