Deep Learning

Introduction to Deep Learning

Introduction to Deep Learning {#sec:534-intro-to-dnn}

In this tutorial we will learn the fist lab on deep neural networks. Basic classification using deep learning will be discussed in this chapter.


MNIST Classification Version 1

Using Cloudmesh Common

Here we do a simple benchmark. We calculate compile time, train time, test time and data loading time for this example. Installing cloudmesh-common library is the first step. Focus on this section because the ** Assignment 4 ** will be focused on the content of this lab.


!pip install cloudmesh-common
    Collecting cloudmesh-common
     Downloading (55kB)
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    Collecting python-hostlist (from cloudmesh-common)
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    Building wheels for collected packages: simplejson, python-hostlist
      Building wheel for simplejson ( ... done
      Created wheel for simplejson: filename=simplejson-3.16.0-cp36-cp36m-linux_x86_64.whl size=114018 sha256=a6f35adb86819ff3de6c0afe475229029305b1c55c5a32b442fe94cda9500464
      Stored in directory: /root/.cache/pip/wheels/5d/1a/1e/0350bb3df3e74215cd91325344cc86c2c691f5306eb4d22c77
      Building wheel for python-hostlist ( ... done
      Created wheel for python-hostlist: filename=python_hostlist-1.18-cp36-none-any.whl size=38517 sha256=71fbb29433b52fab625e17ef2038476b910bc80b29a822ed00a783d3b1fb73e4
      Stored in directory: /root/.cache/pip/wheels/56/db/1d/b28216dccd982a983d8da66572c497d6a2e485eba7c4d6cba3
    Successfully built simplejson python-hostlist
    Installing collected packages: pathlib2, simplejson, python-hostlist, colorama, oyaml, cloudmesh-common
    Successfully installed cloudmesh-common-4.2.13 colorama-0.4.1 oyaml-0.9 pathlib2-2.3.5 python-hostlist-1.18 simplejson-3.16.0

In this lesson we discuss in how to create a simple IPython Notebook to solve an image classification problem. MNIST contains a set of pictures

! python3 --version
Python 3.6.8
! pip install tensorflow-gpu==1.14.0
Collecting tensorflow-gpu==1.14.0
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Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard 1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (0.15.6)
Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.14.0

Import Libraries


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import time

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.utils import to_categorical, plot_model
from keras.datasets import mnist

from cloudmesh.common.StopWatch import StopWatch

Using TensorFlow backend.

Pre-process data


Load data

First we load the data from the inbuilt mnist dataset from Keras

(x_train, y_train), (x_test, y_test) = mnist.load_data()
Downloading data from
11493376/11490434 [==============================] - 1s 0us/step

Identify Number of Classes

As this is a number classification problem. We need to know how many classes are there. So we’ll count the number of unique labels.

num_labels = len(np.unique(y_train))

Convert Labels To One-Hot Vector

|Exercise MNIST_V1.0.0: Understand what is an one-hot vector?

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

Image Reshaping

The training model is designed by considering the data as a vector. This is a model dependent modification. Here we assume the image is a squared shape image.

image_size = x_train.shape[1]
input_size = image_size * image_size

Resize and Normalize

The next step is to continue the reshaping to a fit into a vector and normalize the data. Image values are from 0 - 255, so an easy way to normalize is to divide by the maximum value.

|Execrcise MNIST_V1.0.1: Suggest another way to normalize the data preserving the accuracy or improving the accuracy.

x_train = np.reshape(x_train, [-1, input_size])
x_train = x_train.astype('float32') / 255
x_test = np.reshape(x_test, [-1, input_size])
x_test = x_test.astype('float32') / 255

Create a Keras Model


Keras is a neural network library. Most important thing with Keras is the way we design the neural network.

In this model we have a couple of ideas to understand.

|Exercise MNIST_V1.1.0: Find out what is a dense layer?

A simple model can be initiated by using an Sequential instance in Keras. For this instance we add a single layer.

  1. Dense Layer
  2. Activation Layer (Softmax is the activation function)

Dense layer and the layer followed by it is fully connected. For instance the number of hidden units used here is 64 and the following layer is a dense layer followed by an activation layer.

|Execrcise MNIST_V1.2.0: Find out what is the use of an activation function. Find out why, softmax was used as the last layer.

batch_size = 4
hidden_units = 64

model = Sequential()
model.add(Dense(hidden_units, input_dim=input_size))
plot_model(model, to_file='mnist_v1.png', show_shapes=True)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/ The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/ The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/ The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

Model: "sequential_1"
Layer (type)                 Output Shape              Param #
dense_1 (Dense)              (None, 64)                50240
dense_2 (Dense)              (None, 10)                650
activation_1 (Activation)    (None, 10)                0
Total params: 50,890
Trainable params: 50,890
Non-trainable params: 0


Compile and Train


A keras model need to be compiled before it can be used to train the model. In the compile function, you can provide the optimization that you want to add, metrics you expect and the type of loss function you need to use.

Here we use the adam optimizer, a famous optimizer used in neural networks.

Exercise MNIST_V1.3.0: Find 3 other optimizers used on neural networks.

The loss funtion we have used is the categorical_crossentropy.

Exercise MNIST_V1.4.0: Find other loss functions provided in keras. Your answer can limit to 1 or more.

Once the model is compiled, then the fit function is called upon passing the number of epochs, traing data and batch size.

The batch size determines the number of elements used per minibatch in optimizing the function.

Note: Change the number of epochs, batch size and see what happens.

Exercise MNIST_V1.5.0: Figure out a way to plot the loss function value. You can use any method you like.

StopWatch.start("train"), y_train, epochs=1, batch_size=batch_size)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/ The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/ The name tf.log is deprecated. Please use tf.math.log instead.

add_dispatch_support. locals.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/ The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.

Epoch 1/1
60000/60000 [==============================] - 20s 336us/step - loss: 0.3717 - acc: 0.8934


Now we can test the trained model. Use the evaluate function by passing test data and batch size and the accuracy and the loss value can be retrieved.

Exercise MNIST_V1.6.0: Try to optimize the network by changing the number of epochs, batch size and record the best accuracy that you can gain

loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))
10000/10000 [==============================] - 1s 138us/step

Test accuracy: 91.0%
| Machine Attribute   | Value                                                            |
| BUG_REPORT_URL      | ""                             |
| DISTRIB_CODENAME    | bionic                                                           |
| DISTRIB_DESCRIPTION | "Ubuntu 18.04.3 LTS"                                             |
| DISTRIB_ID          | Ubuntu                                                           |
| DISTRIB_RELEASE     | 18.04                                                            |
| HOME_URL            | ""                                        |
| ID                  | ubuntu                                                           |
| ID_LIKE             | debian                                                           |
| NAME                | "Ubuntu"                                                         |
| PRETTY_NAME         | "Ubuntu 18.04.3 LTS"                                             |
| SUPPORT_URL         | ""                                       |
| UBUNTU_CODENAME     | bionic                                                           |
| VERSION             | "18.04.3 LTS (Bionic Beaver)"                                    |
| VERSION_CODENAME    | bionic                                                           |
| VERSION_ID          | "18.04"                                                          |
| cpu_count           | 2                                                                |
| mac_version         |                                                                  |
| machine             | ('x86_64',)                                                      |
| mem_active          | 973.8 MiB                                                        |
| mem_available       | 11.7 GiB                                                         |
| mem_free            | 5.1 GiB                                                          |
| mem_inactive        | 6.3 GiB                                                          |
| mem_percent         | 8.3%                                                             |
| mem_total           | 12.7 GiB                                                         |
| mem_used            | 877.3 MiB                                                        |
| node                | ('8281485b0a16',)                                                |
| platform            | Linux-4.14.137+-x86_64-with-Ubuntu-18.04-bionic                  |
| processor           | ('x86_64',)                                                      |
| processors          | Linux                                                            |
| python              | 3.6.8 (default, Jan 14 2019, 11:02:34)                           |
|                     | [GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]      |
| release             | ('4.14.137+',)                                                   |
| sys                 | linux                                                            |
| system              | Linux                                                            |
| user                |                                                                  |
| version             | #1 SMP Thu Aug 8 02:47:02 PDT 2019                               |
| win_version         |                                                                  |
| timer     | time  | start               | tag | node              | user | system | mac_version | win_version |
| data-load | 1.335 | 2019-09-27 13:37:41 |     | ('8281485b0a16',) |      | Linux  |             |             |
| compile   | 0.047 | 2019-09-27 13:37:43 |     | ('8281485b0a16',) |      | Linux  |             |             |
| train     | 20.58 | 2019-09-27 13:37:43 |     | ('8281485b0a16',) |      | Linux  |             |             |
| test      | 1.393 | 2019-09-27 13:38:03 |     | ('8281485b0a16',) |      | Linux  |             |             |


Final Note

This programme can be defined as a hello world programme in deep learning. Objective of this exercise is not to teach you the depths of deep learning. But to teach you basic concepts that may need to design a simple network to solve a problem. Before running the whole code, read all the instructions before a code section. Solve all the problems noted in bold text with Exercise keyword (Exercise MNIST_V1.0 - MNIST_V1.6). Write your answers and submit a PDF by following the Assignment 5. Include codes or observations you made on those sections.


Mnist Database

Advanced Deep Learning Models

Minist Deep Learning

Last modified June 17, 2021 : add aliasses (6b7beab5)