How to use PReLU activation in TF learn

This recipe helps you use PReLU activation in TF learn

Recipe Objective

This recipe explains how to use PReLU.

Importing Libraries

We'll import tflearn, tensorflow as tf and tflearn.datasets.mnist as mnist.

import tflearn
import tensorflow as tf
import tflearn.datasets.mnist as mnist
from __future__ import division, print_function, absolute_import

PReLU

PReLU stands for Parametric Rectified Linear Unit.
Its syntax is: tflearn.activations.prelu (x, channel_shared=False, weights_init='zeros', trainable=True, restore=True, reuse=False, scope=None, name='PReLU')
We have combined TFLearn built-in ops with Tensorflow graph. We have built this using MNIST Dataset.
To create a multilayer perceptron we have used TFLearn PReLU activations ops.

with tf.Graph().as_default():

    x = tf.placeholder("float", [None, 784])
    y = tf.placeholder("float", [None, 10])
    u = tf.Variable(tf.random_normal([784, 256]))
    v = tf.Variable(tf.random_normal([256, 256]))
    w = tf.Variable(tf.random_normal([256, 10]))
    a = tf.Variable(tf.random_normal([256]))
    b = tf.Variable(tf.random_normal([256]))
    c = tf.Variable(tf.random_normal([10]))
    def net(X):
       X = tflearn.prelu(tf.add(tf.matmul(X, u), a))
       tflearn.summaries.monitor_activation(x)
       X = tflearn.prelu(tf.add(tf.matmul(X, v), b))
       tflearn.summaries.monitor_activation(x)
       X = tf.nn.prelu(tf.add(tf.matmul(X, w), c))
       return X
    my_net = net(x)

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