Tensorflow-add_layer (add neural layer)

1 Introduction

Defining a function to add a layer in Tensorflow can easily add a neural layer, saving a lot of time for subsequent additions.

Common parameters in the neural layer are usually weights, biases and excitation functions.

2.def add_layer()

First, we need to import the tensorflow module.
Then define the function def add_layer () to add the neural layer. It has four parameters: input value, input size, output size and excitation function. We set the default excitation function to None.

import tensorflow as tf

def add_layer(inputs, in_size, out_size, activation_function=None):

Next, we begin to define weights and biases.

When generating initial parameters, the random distribution (normal distribution) is much better than all zeros, so our weights here are a random variable matrix with in_size rows and out_size columns.

Weights = tf.Variable.random_normal([in_size, out_size])
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)

Next, we define Wx_plus_b, which is the inactive value of the neural network. Among them, tf.matmul () is the matrix multiplication.

Wx_plus_b = tf.matmul(inputs, Weights) + biases

When activation_function-the excitation function is None, the output is the current predicted value-Wx_plus_b, when it is not None, Wx_plus_b is passed to the activation_function () function to get the output.

if activation_function is None:
	outputs = Wx_plus_b
else:
	outputs = activation_function(Wx_plus_b)
return outputs

Finally, return the output and add a nerve layer function-def add_layer () is defined.

Complete function:

def add_layer(inputs, in_size, out_size, activation_function=None):
    Weights = tf.Variable.random_normal([in_size, out_size])
    biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs