A hidden layer is an artificial neural network that is a layer in between input layers and output layers. Where the artificial neurons take in a set of weighted inputs and produce an output through an activation function. It is a part of nearly and neural in which engineers simulate the types of activity that go on in the human brain.

The hidden neural network is set up in some techniques. In many cases, weighted inputs are randomly assigned. On the other hand, they are fine-tuned and calibrated through a process called backpropagation.

The artificial neuron in the hidden layer of perceptron works as a biological neuron in the brain- it takes in its probabilistic input signals, and works on them. And it converts them into an output corresponding to the biological neuron’s axon.

Layers after the input layer are called hidden because they are directly resolved to the input. The simplest network structure is to have a single neuron in the hidden layer that directly outputs the value.

Deep learning can refer to having many hidden layers in our neural network. They are deep because they will have been unimaginably slow to train historically, but may take seconds or minutes to prepare using modern techniques and hardware.

A single hidden layer will build a simple network.

The code for the hidden layers of the perceptron is shown below:

#Importing the essential modules in the hidden layer
import tensorflow as tf
import numpy as np
import mat
plotlib.pyplot as plt
import math, random
np.random.seed(1000)
function_to_learn = lambda x: np.cos(x) + 0.1*np.random.randn(*x.shape)
layer_1_neurons = 10
NUM_points = 1000
#Train the parameters of hidden layer
batch_size = 100
NUM_EPOCHS = 1500
all_x = np.float32(np.random.uniform(-2*math.pi, 2*math.pi, (1, NUM_points))).T
   np.random.shuffle(all_x)
train_size = int(900)
#Train the first 700 points in the set x_training = all_x[:train_size]
y_training = function_to_learn(x_training)
#Training the last 300 points in the given set x_validation = all_x[train_size:]
y_validation = function_to_learn(x_validation)
plt.figure(1)
plt.scatter(x_training, y_training, c = 'blue', label = 'train')
plt.scatter(x_validation, y_validation, c = 'pink', label = 'validation')
plt.legend()
plt.show()
X = tf.placeholder(tf.float32, [None, 1], name = "X")
Y = tf.placeholder(tf.float32, [None, 1], name = "Y")
#first layer
#Number of neurons = 10
w_h = tf.Variable(
   tf.random_uniform([1, layer_1_neurons],\ minval = -1, maxval = 1, dtype = tf.float32))
b_h = tf.Variable(tf.zeros([1, layer_1_neurons], dtype = tf.float32))
h = tf.nn.sigmoid(tf.matmul(X, w_h) + b_h)
#output layer
#Number of neurons = 10
w_o = tf.Variable(
   tf.random_uniform([layer_1_neurons, 1],\ minval = -1, maxval = 1, dtype = tf.float32))
b_o = tf.Variable(tf.zeros([1, 1], dtype = tf.float32))
#building the model
model = tf.matmul(h, w_o) + b_o
#minimize the cost function (model - Y)
train_op = tf.train.AdamOptimizer().minimize(tf.nn.l2_loss(model - Y))
#Starting the Learning phase
sess = tf.Session() sess.run(tf.initialize_all_variables())
errors = []
for i in range(NUM_EPOCHS):
   for start, end in zip(range(0, len(x_training), batch_size),\
      range(batch_size, len(x_training), batch_size)):
      sess.run(train_op, feed_dict = {X: x_training[start:end],\ Y: y_training[start:end]})
   cost = sess.run(tf.nn.l2_loss(model - y_validation),\ feed_dict = {X:x_validation})
   errors.append(cost)
   if i%100 == 0:
      print("epoch %d, cost = %g" % (i, cost))
plt.plot(errors,label='MLP Function Approximation') plt.xlabel('epochs')
plt.ylabel('cost')
plt.legend()
plt.show()

Output

Following is the illustration of function layer approximation-

Here two data are represented in the shape of W.

The two data are: train and validation, which are described in distinct colors as visible in the legend section.

So, this brings us to the end of blog. This Tecklearn ‘Hidden Layer Perceptron in Tensor Flow’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Installation
Python – NumPy
Python for Data Science and AI
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Python Libraries – Numpy and Pandas
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Pandas for Data Analysis
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Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
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Sigmoid Function
Multi-layered perception
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Activation Functions
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Epoch, Forward & backword propagation
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Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
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Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
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Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

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Introduction to RNN Model
Application use cases of RNN
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Recursive Neural Tensor Network Theory
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