Welcome to the TensorFlow Tutorial: 102: average pool 1d

In this tutorial, we will be focusing on the average pooling operation for a 1-dimensional array in TensorFlow. Pooling is a common operation used in convolutional neural networks to reduce the spatial dimensions of the input data. Average pooling calculates the average value of a given window of the input array and outputs a single value.

Implementation in TensorFlow

Let’s see how we can implement average pooling operation for a 1-dimensional array in TensorFlow:

“`python
import tensorflow as tf

input_data = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32)
pooling_window = 2

output_data = tf.nn.pool(input_data, window_shape=[pooling_window], pooling_type=’AVG’, strides=[1])

with tf.Session() as sess:
result = sess.run(output_data)

print(result)
“`

In the above code snippet, we first create a TensorFlow constant with a 1-dimensional array of values. We then specify the pooling window size and use the `tf.nn.pool` function to perform average pooling with the specified window size and stride. Finally, we run the operation in a TensorFlow session to get the output result.

Conclusion

Overall, average pooling is a useful operation in convolutional neural networks for reducing the spatial dimensions of the input data. By following this tutorial and understanding the implementation in TensorFlow, you can effectively apply average pooling to your own machine learning projects.