Multi-Layer perceptron defines the most complex architecture of artificial neural networks. It is substantially formed from multiple layers of the perceptron. TensorFlow is a very popular deep learning framework released by, and this notebook will guide to build a neural network with this library. If we want to understand what is a multi-layer perceptron, we have to develop a multi-layer perceptron from scratch using Numpy.

The pictorial representation of multi-layer perceptron learning is as shown below-

keras 10

MLP networks are used for supervised learning format. A typical learning algorithm for MLP networks is also called back propagation’s algorithm.

A multilayer perceptron (MLP) is a feed forward artificial neural network that generates a set of outputs from a set of inputs. An MLP is characterized by several layers of input nodes connected as a directed graph between the input nodes connected as a directed graph between the input and output layers. MLP uses backpropagation for training the network. MLP is a deep learning method.

Now, we are focusing on the implementation with MLP for an image classification problem.# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
import tensorflow as tf
import matplotlib.pyplot as plt
# Parameters
learning_rate = 0.001
training_epochs = 20
batch_size = 100
display_step = 1
# Network Parameters
n_hidden_1 = 256
# 1st layer num features
n_hidden_2 = 256 # 2nd layer num features
n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10
# MNIST total classes (0-9 digits)
# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
# weights layer 1
h = tf.Variable(tf.random_normal([n_input, n_hidden_1])) # bias layer 1
bias_layer_1 = tf.Variable(tf.random_normal([n_hidden_1]))
# layer 1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, h), bias_layer_1))
# weights layer 2
w = tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]))
# bias layer 2
bias_layer_2 = tf.Variable(tf.random_normal([n_hidden_2]))
# layer 2
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, w), bias_layer_2))
# weights output layer
output = tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
# biar output layer
bias_output = tf.Variable(tf.random_normal([n_classes])) # output layer
output_layer = tf.matmul(layer_2, output) + bias_output
# cost function
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
   logits = output_layer, labels = y))
#cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output_layer, y))
# optimizer
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
# optimizer = tf.train.GradientDescentOptimizer(
   learning_rate = learning_rate).minimize(cost)
# Plot settings
avg_set = []
epoch_set = []
# Initializing the variables
init = tf.global_variables_initializer()
# Launch the graph
with tf.Session() as sess:
   sess.run(init)
   # Training cycle
   for epoch in range(training_epochs):
      avg_cost = 0.
      total_batch = int(mnist.train.num_examples / batch_size)
      # Loop over all batches
      for i in range(total_batch):
         batch_xs, batch_ys = mnist.train.next_batch(batch_size)
         # Fit training using batch data sess.run(optimizer, feed_dict = {
            x: batch_xs, y: batch_ys})
         # Compute average loss
         avg_cost += sess.run(cost, feed_dict = {x: batch_xs, y: batch_ys}) / total_batch
      # Display logs per epoch step
      if epoch % display_step == 0:
         print
         Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost)
      avg_set.append(avg_cost)
      epoch_set.append(epoch + 1)
   print
   "Training phase finished"
   plt.plot(epoch_set, avg_set, 'o', label = 'MLP Training phase')
   plt.ylabel('cost')
   plt.xlabel('epoch')
   plt.legend()
   plt.show()
   # Test model
   correct_prediction = tf.equal(tf.argmax(output_layer, 1), tf.argmax(y, 1))
   # Calculate accuracy
   accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
   print
   "Model Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

The above line of codes generating the following output-

keras 11

Creating an interactive section

We have two basic options when using TensorFlow to run our code:

Build graphs and run sessions [Do all the set-up and then execute a session to implement a session to evaluate tensors and run operations].
Create our coding and run on the fly.

For this first part, we will use the interactive session that is more suitable for an environment like Jupiter notebook.

```
sess = tf.InteractiveSession()
```

Creating placeholders

It’s a best practice to create placeholder before variable assignments when using TensorFlow. Here we’ll create placeholders to inputs (“Xs”) and outputs (“Ys”).

Placeholder “X”: Represent the ‘space’ allocated input or the images.

Each input has 784 pixels distributed by a 28 width x 28 height matrix.
The ‘shape’ argument defines the tensor size by its dimensions.

So, this brings us to the end of blog. This Tecklearn ‘Multi-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:

https://www.tecklearn.com/course/artificial-intelligence-and-deep-learning-with-tensorflow/

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What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
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
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

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

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

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