gluon framework

Implementing neural networks using Gluon API

Implementing neural networks using Gluon API

In the previous chapter, we discussed the basics of deep learning and over of Gluon API and MxNet. This chapter explains how to use Gluon API to create a different neural network by exploring Gluon API.

Gluon API is the abstraction over the mathematical computation deep learning framework MxNet. As we have discussed in the last chapter different types of machine learning and different algorithms to implement each method. As part of this chapter, we will look into linear regression, binary classification and multiclass classification using Gluon.

Neural Network using Gluon:

Gluon has a hybrid approach in deep learning programming. It supports both symbolic as well as the imperative style of programming. There are different machine learning algorithms to address different problems. As we stated in the last chapter artificial neural network is a mathematical computation representation of a human brain. It’s mathematical computation to deal with that we need to do a matrix or tensor manipulation and to do that we have Gluon API, NDArray API. Artificial Neural network contains nodes and each node has some weight and bias and the data will get transform from layer to layer up to the output layer. The middle layers between the input layer and the output layer are called hidden layers. Let us explore something:

Linear Regression:

Linear regression is a very basic algorithm in the field of machine learning. Everyone will come across to this algorithm whether you are a novice or expert machine learning engineer or data scientist. Linear regression is categorized under supervised machine learning. As the name state, linear regression used to identify the relationship between two continuous variables. In this case, there are two variables, one is predictor (independent) and another one is the dependent (response) variable. We will be able to model the relationship between two variables by fitting them in a linear equation.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). This is in a mathematical way.

In the above diagram, you can able to see there is a linear equation to fit these data points. On X-axis we have some data points and on Y-axis we have some data. The data points plotted in this diagram states the linear equation between two variable one is dependent and another one is independent.

To understand this, let us take one small example. There is some real-life example like predict the sale of products based on buying history. Predict the house price based on the size of a house, location of a property, amenities, demand and historical records.

There are two different types of Linear regression.

  1. Simple Linear Regression:– Simple linear regression where there are two variables one is dependent and another is an independent variable. X is used to predicate the dependent variable Y. Like Predicate the total fuel expense based on the distance in kilometers.
  2. Multiple(Multi-Variable) Linear Regression:- Multiple linear regression where we have one dependent variable and two or more independent variable. In certain case, we have two or number features that could affect the dependent variable. Like in the case of predicate the house price depends on the size of a house, location of a house, construction year, etc. There are (X1, X2,..) dependent variable to predict Y.

Let us consider the problem when we are in academics we are predicting marks based on how we solved the paper. Let us take one small example you are planning a road trip to Shimla (the city from India) as you recently watched Tripling (India Web series ) with your two siblings. You started from Pune and the total distance have to travel is 1790 km. Its long journey so you have to plan each and every expense such as fuel, meal, and halt, etc. We will take a blank paper you will put when to start and stop and how much fuel is required? how much money need to reserve for meal and hotel charges and to follow these questions you will list out those things and based on your travel car mileage and current fuel prices you can predict total paid for fuel. So, it’s a simple linear relationship between two variables, If I drive for 1790 km, how much will I pay for fuel? If you want to predict the overall expense of a trip then you can convert this simple linear regression into the complex linear regression model. Add more independent variable such as meal cost, lodging charges, other expenses, and historical data in the last trips.

This is the way we can forecast the current trip charges and plan accordingly. The core idea is to obtain a line that best fits the data. Linear Regression is the simplest and far most popular method in machine learning for problem-solving.

Linear regression using Gluon:

Linear regression is the entry pass to the journey of machine learning, given that it is a very straight forward problem and we can solve this using Gluon API. A linear equation is y=Wx+b by constructing the above graph that learns the gradient of the slope (W) and bias (b) through a number of iterations. The target of each iteration to reduce the loss between actual y and predicated y and to achieve this we want to modify the W and b, so inputs of x will give us the y we want. Let us take one small example, implement linear regression using Gluon API, In this example, we are not developing each and everything from scratch but we will take advantage of gluon API to form our implementation,

# let is importantss
import numpy as np
import mxnet as mx
from mxnet import nd, autograd, gluon
# this is for nural layers
from mxnet.gluon import nn, Trainer
# this is for data loading
from mxnet.gluon.data import DataLoader, ArrayDataset

Here is the above code black we have just imported required modules. If you observed carefully gluon API is the part of the mxnet package. We have imported ndarray to numerical tensor processing and autograd for automatic differentiation of a graph of NDArray operations. mxnet.gluon.data is the module which contains API that can help us to load and process the common public dataset such as MNIST.

from mxnet.gluon import nn, Trainer

Gluon provides nn API to define different layers of neural network and Trainer API help us to train the defined neural network. Data is an important part let us build the data set.

We start by generating our dataset, one is for

# set context for optimisation
data_ctx = mx.cpu()
model_ctx = mx.cpu()
# to generate random data
number_inputs = 2
number_outputs = 1
number_examples = 10000
def real_fn(X):
    return 2 * X[:, 0] - 3.4 * X[:, 1] + 4.2
#generate randome records of 10000
X = nd.random_normal(shape=(number_examples, number_inputs))
noise = 0.01 * nd.random_normal(shape=(number_examples,))
y = real_fn(X) + noise

The above code can generate the dataset for the problem.

Now data is ready, load the data using DataLoader API.

batch_size = 4
train_data = gluon.data.DataLoader(gluon.data.ArrayDataset(X, y),
                                      batch_size=batch_size, shuffle=True)

Let us build a Neural network with two input and one output layer as we defined this using nn.Dense(1, in_units=2). It’s called a dense layer because every node in the input is connected to every node in the subsequent layer.

net = gluon.nn.Dense(1, in_units=2)
# dense layer with 2 inputs and 1 output layer
# print just weight and bias for neural network
print(net.weight)
print(net.bias)
# output of above print statements
Parameter dense6_weight (shape=(1, 2), dtype=float32)
Parameter dense6_bias (shape=(1,), dtype=float32)
mxnet.gluon.parameter.ParameterDict

The output of this weight and bias are actually not a ndArrays. They are an instance of Parameter class. We are using Parameter over NDArray for distinct reasons. Parameters can be associated with multiple contexts unlike NDArray. As we discussed in the first chapter Block is the basic building block of neural network in the Gluon, Block will take input and generate output. We can collect all parameters using net.collect_params() irrespective of how complex the neural network is. This method will return the dictionary of parameters.

Next step would be to initialization of parameter of a neural network. The initialization step is very important. In this step, we can access contexts, data and also we can feed data to a neural network.

net.collect_params().initialize(mx.init.Normal(sigma=1.), ctx=model_ctx)
# Deferred initialization
example_data = nd.array([[4,7]])
net(example_data) 
# access the weight and bias data
print(net.weight.data())
print(net.bias.data())
net = gluon.nn.Dense(1)
net.collect_params().initialize(mx.init.Normal(sigma=1.), ctx=model_ctx)

let us observe the difference net = gluon.nn.Dense(1) and the first layer code net = gluon.nn.Dense(1, in_units=2), Gluon inference the shape on parameters.

square_loss = gluon.loss.L2Loss()

Now need to optimize the neural network, Implementing Stochastic gradient descent from scratch to optimize the neural network every time better we can reuse the code gluon.Trainer, pass a parameter dictionary to optimize the network.

trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.0001})

SGD is Stochastic gradient descent implementation given by Gluon, the learning rate is 0.0001 and passing a dictionary of parameters to optimize the neural network. Now we have actual y and y-pred, we want to know how far the predicted y is away from our generated y. The difference between this two y is called as a loss function and to reduce this loss we are using SGD.

epochs = 10
loss_sequence = []
num_batches = num_examples / batch_size
for e in range(epochs):
    cumulative_loss = 0
    # inner loop
    for i, (data, label) in enumerate(train_data):
        data = data.as_in_context(model_ctx)
        label = label.as_in_context(model_ctx)
        with autograd.record():
            output = net(data)
            loss = square_loss(output, label)
        loss.backward()
        trainer.step(batch_size)
        cumulative_loss += nd.mean(loss).asscalar()
    print("Epoch %s, loss: %s" % (e, cumulative_loss / num_examples))
    loss_sequence.append(cumulative_loss)

Let us visualize the learning loss.

# plot the convergence of the estimated loss function 
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
plt.figure(num=None,figsize=(8, 6))
plt.plot(loss_sequence)
# Adding some bells and whistles to the plot
plt.grid(True, which="both")
plt.xlabel('epoch',fontsize=14)
plt.ylabel('average loss',fontsize=14)

SGD learns the linear regression model by plotting the learning curve. The graph indicates the average loss over each epoch. Loss is getting reduced over each iteration.

Now our model is ready and everything working as expected but we need to do some sanity testing for validation purpose.

params = net.collect_params()
print('The type of "params" is a ',type(params))
# A ParameterDict is a dictionary of Parameter class objects
# we will iterate over the dictionary and print the parameters.
for param in params.values():
    print(param.name,param.data())

From this example, we can say that Gluon can help us to build quick, easy prototyping.

In this example, we used a few API that helps to build a neural network without writing everything from scratch. Gluon provides us a more concise way to express model. API is too powerful to prototype, build model quick and easy. Linear regression we can use in many real-life scenarios,

  1. Predicate the house price
  2. Predicate the weather conditions
  3. Predicate the stock price

These are just a few scenarios where you can apply linear regression to predicate the values. The predicted values in linear regression are continuous values.

Binary Classification:

In the above section, we explored linear regression with sample code. When we implemented this linear regression the output value is continuous values, but there are few real-life examples where we don’t have continuous values but we need to classification such email is spam or not or which party will be getting elected in the next elections, customer should buy an insurance policy or not. The classification problem may be binary or multiclass classification where you have more than two classes. In this type of problem, the output neurons are two or more. In classification problem the prediction values are categorical. Logistic regression is the machine learning technique used to solve such classification problems. Basically, logistic regression is an algorithm to solve a binary classification problem.

Let us consider a problem we will provide an image as an input to the neural network and output could be labeled as to whether its dog(1) or non dog(0). In supervised learning there are two types of a problem one is regression and another one is classification problem. In regression problems, the output is a rational number whereas in classification problems the output is categorical. There are different algorithms available to solve such type of classification problems such as support vector machine, discriminant analysis, naive Bayes, nearest neighbor, and logistic regression. Classification problem-solving means identifying in which of the category a new observation.

In the above diagram, you can easily categories data into two classes, one is circled another one is a cross sign. This called binary classification.

Binary classification using logistic regression:

Logistic regression is a very popular and powerful machine learning technique to solve the classification problem. Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables. Logistic regression will answer the question like how likely is it?. Then you will get the question of why are not using linear regression? We have tumor cancer dataset and each of one is malignant or not denoted by zero or one. If we use linear regression then we can construct a line to best fit for an equation y =wx +b then we can decide all values left to the line are non-malignant and values right of the line are malignant based on a threshold (ex. 0.5), what if there is an outlier means some positive class values into the negative class. we need a way to deal with outlier and logistic regression will give us that power. Logistic regression does not try to predict the rational value of a given a set of inputs. Instead, the output is a probability that the given input point belongs to a certain category and based on the threshold we can easily categorize the input observation. Logistic Regression is a type of classification algorithm involving a linear discriminant. The linear discriminant means the input space is separated into two regions by a linear boundary and model will be able to differentiate between points belonging to different category.

Logistic regression technique is useful when several independent variables on a single outcome variable. Let us consider we are watching cricket world cup matches, we want to predicate whether the match will be getting scheduled or not based on weather conditions

OutlookTemperatureHumidityWindyPlaysunnyhothighfalsenosunnyhothightruenoovercasthothighfalseyesrainymildhighfalseyesrainycoolnormalfalseyesrainycoolnormaltruenoovercastcoolnormaltrueyessunnymildhighfalsenosunnycoolnormalfalseyesrainymildnormalfalseyessunnymildnormaltrueyesovercastmildhightrueyesovercasthotnormalfalseyesrainymildhightrueno

In the above dataset, the output is yes(1) or no(0). Here the output is categorical with two output classes that’s why this is aka as binary classification.

Let us start some code, for this example, we are considering the (https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html ) with total sample 569 with 30 dimensions and two classes.

Import the required modules. Here we need sklearn python library which contains breast cancer data inbuild, we can use this dataset and apply logistic regression for binary classification.

import mxnet as mx
from mxnet import gluon, autograd, ndarray
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

Load the data set and use the pandas data frame to hold the data for further processing.

# the dataset is part of below module
from sklearn.datasets import load_breast_cancer 
# load data 
data = load_breast_cancer()
# use pandas data frame to hold the dataset
df = pd.DataFrame(data.data, columns=data.feature_names)
y = data.target
X = data.data
# print first five records
df.head()
# display record shape means number for rows and cloumns
df.shape
# number of dimentions
df.ndim

Now data is available but this data is human readable format and to train neural network it won’t be useful. Before start train our neural network we need to normalize the data. To normalize the data we are using pandas. We can also use gluon to normalize the dataset.

df_norm = (df - df.mean()) / (df.max() - df.min())

Before training any machine learning algorithm the critical part is the dataset, We need to split the dataset into training and testing dataset. Let us do that

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=12345)

Tuning the hyperparameters is another important aspect in training the artificial neural network.

BATCH_SIZE = 32
LEARNING_R = 0.001
EPOCHS = 150

Let us prepare the data for according to gluon API, so that we can feed that data to network and train. To do that we can use mx.gluon.data module

train_dataset = mx.gluon.data.ArrayDataset(X_train.as_matrix(),y_train)
test_dataset = mx.gluon.data.ArrayDataset(X_test.as_matrix(),y_test)
train_data = mx.gluon.data.DataLoader(train_dataset,
                                      batch_size=BATCH_SIZE, shuffle=True)
test_data = mx.gluon.data.DataLoader(test_dataset,
                                     batch_size=BATCH_SIZE, shuffle=False)

Let us use gluons plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers. It has some predefined layers such Dense layer, sequential, etc.

net = gluon.nn.Sequential()
# Define the model architecture
with net.name_scope():
 net.add(gluon.nn.Dense(64, activation="relu"))
 net.add(gluon.nn.Dense(32, activation="relu") ) 
 net.add(gluon.nn.BatchNorm()) 
 net.add(gluon.nn.Dense(1, activation="sigmoid"))
# Intitalize parametes of the model
net.collect_params().initialize(mx.init.Uniform())
# Add binary loss function, sigmoid binary cross Entropy
binary_cross_entropy = gluon.loss.SigmoidBinaryCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': LEARNING_R})

The neural network contains four layers. We are using ‘relu’ as an activation function. ReLU rectified linear unit is an activation function aka a ramp function. The third layer is (gluon.nn.BatchNorm() ) batch normalisation layer. Another activation function we have used is ‘sigmoid’. The sigmoid function is another linear activation function having a characteristic of S-shaped curve. In the binary classification, the loss function we used is binary cross entropy. It measures the performance of a model whose output is a probability number between 0 and 1. Below is binary cross entropy loss function mathematical formula.

Then gluon.Trainer() to train the model.

Now training time for the model

for e in range(EPOCHS):
    for i, (data, label) in enumerate(train_data):
        data = data.as_in_context(mx.cpu()).astype('float32')
        label = label.as_in_context(mx.cpu()).astype('float32')
        with autograd.record(): # Start recording the derivatives
            output = net(data) # the forward iteration
            loss = binary_cross_entropy(output, label)
            loss.backward()
        trainer.step(data.shape[0])
        # Provide stats on the improvement of the model over each epoch
        curr_loss = ndarray.mean(loss).asscalar()
    if e % 20 == 0:
        print("Epoch {}. Current Loss: {}.".format(e, curr_loss))

Look at the above loss function graph, its in S-shape. Let us calculate this

print(accuracy_score(y_test, y_pred_labels))

This is the binary classification problem where we have just observed breast cancer data set with input data set and output is either of the two categories malignant or benign.

Multiclass classification:

We had discussed till linear regression problem, where output is single value and that is also a single rational number, then we have seen some of the categorical problem those aka as classification problems. In Classification problems also there are generally two types of a classification problem.

  1. Binary Classification
  2. MultiClass Classification

Binary classification problem means two categories, such as email is spam or not, breast cancer, and based on weather conditions cricket match will get played or not. In all this scenario the output is either(yes/no) of the categories but there is a real-life scenario where you have more than one category those problems are classified as multiclass classification(more than two classes). MultiClass classification aka multinominal classification. In multiclass classification, classifying observation into one of three or more classes. Don’t be confuse with multi-label classification with multiclass classification.

We went into the grocery shop for shopping at the fruit stall you stopped to buy some fruit, you picked your phone are tried your machine learning algorithm to identify a fruit based on color, shape, etc. Classifies the set of images of fruits which may banana, apple, orange, guava, etc. We will use the same logistic regression algorithm to address this multiclass classification problem. Logistic regression is the classic algorithm to solve the classification problem in supervised learning. As we have seen binary classification is quite useful when We have a dataset with two categories like, use it to predict email spam vs. not spam or breast cancer or not cancer. But this is not for every problem. Sometimes we encounter a problem where each observation could belong to one of the n classes. For example, an image might depict a lion, cat or a dog or a zebra, etc.

Let us dive deeper into the multiclass classification problem for this we will use MNIST (Modified National Institute of Standards and Technology ) dataset. This is the handwritten digits dataset. This dataset is widely used to teach deep learning hello world program. The MNIST dataset contains 60,000 training images and 10,000 testing images. MNIST can be a nice toy dataset for testing new ideas it is like a HelloWorld program for an artificial neural network.

Let us makes our hands dirty with gluon multiclass classification implementation.

from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd
from mxnet import gluon
import numpy as np

Let us import some of the modules that require such as mxnet, gluon, ndArray, autograd for differentiation and numpy.

Set the context, in previous all example we have set is the CPU for simplicity, you can set GPU if you want to execute code on GPU for that you have to install GPU enabled mxnet GLUON API.

( e.g . model_ctx=mx.gpu() ).

data_ctx = mx.cpu()
model_ctx = mx.cpu()

For multiclass classification, we are using the MNIST data set, as part of this we are not explaining what is MNIST data set for more details you can use this link https://en.wikipedia.org/wiki/MNIST_database.

batch_size = 64
num_inputs = 784
num_outputs = 10
num_examples = 60000
def transform(data, label):
    return data.astype(np.float32)/255, label.astype(np.float32)
train_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=True, transform=transform),
                                      batch_size, shuffle=True)
test_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False, transform=transform),
                              batch_size, shuffle=False)

Load the dataset number of inputs is 784 and the number of outputs is 10 (number 0,1…,9) with 60000 examples and 64 is the batch size. mx.gluon.data.vision.MNIST module contains the MNIST dataset which is part of gluon API. For training and validation purpose we are splitting data set into two-part testing data set and training data set.

Data is loaded successfully the next step is to define our module. Revise the code of linear regression for binary classification where we defined the Dense layer with the number inputs and outputs. gluon.nn.Dense(num_ouputs) is the defined layer with output shape and gluon inference the input shape from input data.

net = gluon.nn.Dense(num_outputs)

Parameter initialization is the next step but before going to register an initializer for parameters, gluon doesn’t know the shape of the input parameter because we have mentioned the shape of the output parameters. The parameters will get initialized during the first call to the forward method.

net.collect_params().initialize(mx.init.Normal(sigma=1.), ctx=model_ctx)

When you need to get the output in probabilities then Softmax cross entropy loss function can be useful

Softmax is an activation layer which allows us to interpret the outputs as probabilities, while cross entropy is we use to measure the error at a softmax layer.

Let us consider below softmax code snippet

# just for understanding.
def softmax(z):
    """Softmax function"""
    return np.exp(z) / np.sum(np.exp(z))

As the name suggests, softmax function is a “soft” version of max function. Instead of selecting one maximum rational value, it breaks the value with maximal element getting the largest portion of the distribution, that’s why it’s very good to get the probabilities of the inputs. From the above code, you will able to get that Softmax function takes an N-dimensional vector of real numbers as an input and transforms it into a vector of real number in range (0,1).

softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()

Now initiate an optimizer with learning rate 0.1. sgd (Stochastic gradient decent)

trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})

Now the model is trained, but evaluation of the model is required to identify the accuracy. To do this we are using MxNet built-in metric package. We should have to consider accuracy in the ballpark of .10 because of we initialized model randomly.

def evaluate_accuracy(data_iterator, net):
    acc = mx.metric.Accuracy()
    for i, (data, label) in enumerate(data_iterator):
        data = data.as_in_context(model_ctx).reshape((-1,784))
        label = label.as_in_context(model_ctx)
        output = net(data)
        predictions = nd.argmax(output, axis=1)
        acc.update(preds=predictions, labels=label)
    return acc.get()[1]
# call the above function with test data
evaluate_accuracy(test_data,net).

Now execute the training loop with 10 iterations,

epochs = 10
moving_loss = 0.
for e in range(epochs):
    cumulative_loss = 0
    for i, (data, label) in enumerate(train_data):
        data = data.as_in_context(model_ctx).reshape((-1,784))
        label = label.as_in_context(model_ctx)
        with autograd.record():
            output = net(data)
            loss = softmax_cross_entropy(output, label)
        loss.backward()
        trainer.step(batch_size)
        cumulative_loss += nd.sum(loss).asscalar()
    test_accuracy = evaluate_accuracy(test_data, net)
    train_accuracy = evaluate_accuracy(train_data, net)
    print("Epoch %s. Loss: %s, Train_acc %s, Test_acc %s" % (e, cumulative_loss/num_examples, train_accuracy, test_accuracy))
# output
Epoch 0. Loss: 2.1415544213612874, Train_acc 0.7918833333333334, Test_acc 0.8015
Epoch 1. Loss: 0.9146347909927368, Train_acc 0.8340666666666666, Test_acc 0.8429
Epoch 2. Loss: 0.7468763765970866, Train_acc 0.8524333333333334, Test_acc 0.861
Epoch 3. Loss: 0.65964135333697, Train_acc 0.8633333333333333, Test_acc 0.8696
Epoch 4. Loss: 0.6039828490893046, Train_acc 0.8695833333333334, Test_acc 0.8753
Epoch 5. Loss: 0.5642358363191287, Train_acc 0.8760166666666667, Test_acc 0.8819
Epoch 6. Loss: 0.5329904221892356, Train_acc 0.8797, Test_acc 0.8849
Epoch 7. Loss: 0.5082313110192617, Train_acc 0.8842166666666667, Test_acc 0.8866
Epoch 8. Loss: 0.4875676867882411, Train_acc 0.8860333333333333, Test_acc 0.8891
Epoch 9. Loss: 0.47050906361341477, Train_acc 0.8895333333333333, Test_acc 0.8902

Visualize the prediction

import matplotlib.pyplot as plt
def model_predict(net,data):
    output = net(data.as_in_context(model_ctx))
    return nd.argmax(output, axis=1)
# let's sample 10 random data points from the test set
sample_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False, transform=transform),
                              10, shuffle=True)
for i, (data, label) in enumerate(sample_data):
    data = data.as_in_context(model_ctx)
    print(data.shape)
    im = nd.transpose(data,(1,0,2,3))
    im = nd.reshape(im,(28,10*28,1))
    imtiles = nd.tile(im, (1,1,3))
    plt.imshow(imtiles.asnumpy())
    plt.show()
    pred=model_predict(net,data.reshape((-1,784)))
    print('model predictions are:', pred)
    break

# output of the above code snippet

(10, 28, 28, 1)
model predictions are: 
[3. 6. 7. 8. 3. 8. 1. 8. 2. 1.]
<NDArray 10 @cpu(0)>

From the output of the above program, we can understand our model is able to solve the multiclass classification problem. Multiclass classification problem solved using linear regression algorithm. The activation function we used here is the softmax activation function that will enforce the output should be in the range of (0,1). That allowed us to interpret these outputs as probabilities. Other common names we can use softmax regression and multinomial regression alternatively. In the above example, we have used sgd (stochastic gradient descent)

def SGD(params, lr):
    for param in params:
        param[:] = param - lr * param.grad

Overfitting and regularization:

Overfitting

Till now we have solved regression and classification algorithm and with three different datasets, we achieved almost approximately 90% accuracy over the testing dataset. Sometimes times a model is too closely fit a limited set of data points that time we say its an overfitting error. The above regression and classification algorithm are working fine in the above examples but those are not working for certain of the datasets and running into overfitting they can cause them to perform very poorly. In this section, I would like to explain to you what is overfitting problem and regularization technique that will allow us to reduce this overfitting problem and get this learning algorithm to perform much better.

I find this joke from “Plato and Platypus Walk Into a Bar” does the best analogy to explain this overfitting problem.

“A man tries on a made-to-order suit and says to the tailor, “I need this sleeve taken in! It’s two inches too long!”

The tailor says, “No, just bend your elbow like this. See, it pulls up the sleeve.”

The man says, “Well, okay, but now look at the collar! When I bend my elbow, the collar goes halfway up the back of my head.”

The tailor says, “So? Raise your head up and back. Perfect.”

The man says, “But now the left shoulder is three inches lower than the right one!”

The tailor says, “No problem. Bend at the waist way over to the left and it evens out.”

The man leaves the store wearing the suit, his right elbow crooked and sticking out, his head up and back, all the while leaning down to the left. The only way he can walk is with a choppy, atonic walk.

This suit is perfectly fit that man but it has been overfitted. This suit would neither be useful to him nor to anyone else. I think this is the best analogy to explain this overfitting problem.

Overfitting and underfitting aka overtraining and undertraining and it occurs when an algorithm captures the noise of the data. Underfitting occurs when the model is not fit well enough. Not every algorithm that performs well on training data will also perform well on test data. To identify the overfitting and underfitting using validation and cross-validation data set. Both overfitting and underfitting lead to a poor prediction on the new observations.

Underfitting occurs if the model shows high bias and low variance. Overfitting occurs if the model shows high variance. If we have too many features, the learned model may fit the training set very well but fail to predicate new observations.

Let us ritual our MNIST data set and see how can things go wrong.

from __future__ import print_function
import mxnet as mx
import mxnet.ndarray as nd
from mxnet import autograd
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
ctx = mx.cpu() 
# load the MNIST data set and split it into the training and testing
mnist = mx.test_utils.get_mnist()
num_examples = 1000
batch_size = 64
train_data = mx.gluon.data.DataLoader(
    mx.gluon.data.ArrayDataset(mnist["train_data"][:num_examples],
                               mnist["train_label"][:num_examples].astype(np.float32)),
                               batch_size, shuffle=True)
test_data = mx.gluon.data.DataLoader(
    mx.gluon.data.ArrayDataset(mnist["test_data"][:num_examples],
                               mnist["test_label"][:num_examples].astype(np.float32)),
                               batch_size, shuffle=False)

We are using a linear model with softmax. Allocate the parameter and define the model

# weight
W = nd.random_normal(shape=(784,10))
# bias
b = nd.random_normal(shape=10)
params = [W, b]
for param in params:
    param.attach_grad()
def net(X):
    y_linear = nd.dot(X, W) + b
    yhat = nd.softmax(y_linear, axis=1)
    return yhat

Define loss function to calculate average loss and optimizer to optimize the loss. As we have seen this cross entropy loss function and SGD in multiclass classification.

# cross entropy 
def cross_entropy(yhat, y):
    return - nd.sum(y * nd.log(yhat), axis=0, exclude=True)
# stochastic gradient descent 
def SGD(params, lr):
    for param in params:
        param[:] = param - lr * param.grad
def evaluate_accuracy(data_iterator, net):
    numerator = 0.
    denominator = 0.
    loss_avg = 0.
    for i, (data, label) in enumerate(data_iterator):
        data = data.as_in_context(ctx).reshape((-1,784))
        label = label.as_in_context(ctx)
        label_one_hot = nd.one_hot(label, 10)
        output = net(data)
        loss = cross_entropy(output, label_one_hot)
        predictions = nd.argmax(output, axis=1)
        numerator += nd.sum(predictions == label)
        denominator += data.shape[0]
        loss_avg = loss_avg*i/(i+1) + nd.mean(loss).asscalar()/(i+1)
    return (numerator / denominator).asscalar(), loss_avg

Plot the loss function and visualize the model using matplotlib.

def plot_learningcurves(loss_tr,loss_ts, acc_tr,acc_ts):
    xs = list(range(len(loss_tr)))
    f = plt.figure(figsize=(12,6))
    fg1 = f.add_subplot(121)
    fg2 = f.add_subplot(122)
    fg1.set_xlabel('epoch',fontsize=14)
    fg1.set_title('Comparing loss functions')
    fg1.semilogy(xs, loss_tr)
    fg1.semilogy(xs, loss_ts)
    fg1.grid(True,which="both")
    fg1.legend(['training loss', 'testing loss'],fontsize=14)
    fg2.set_title('Comparing accuracy')
    fg1.set_xlabel('epoch',fontsize=14)
    fg2.plot(xs, acc_tr)
    fg2.plot(xs, acc_ts)
    fg2.grid(True,which="both")
    fg2.legend(['training accuracy', 'testing accuracy'],fontsize=14)

Let us iterate.

epochs = 1000
moving_loss = 0.
niter=0
loss_seq_train = []
loss_seq_test = []
acc_seq_train = []
acc_seq_test = []

for e in range(epochs):
    for i, (data, label) in enumerate(train_data):
        data = data.as_in_context(ctx).reshape((-1,784))
        label = label.as_in_context(ctx)
        label_one_hot = nd.one_hot(label, 10)
        with autograd.record():
            output = net(data)
            loss = cross_entropy(output, label_one_hot)
        loss.backward()
        SGD(params, .001)
        ##########################
        # Keep a moving average of the losses
        ##########################
        niter +=1
        moving_loss = .99 * moving_loss + .01 * nd.mean(loss).asscalar()
        est_loss = moving_loss/(1-0.99**niter)
    test_accuracy, test_loss = evaluate_accuracy(test_data, net)
    train_accuracy, train_loss = evaluate_accuracy(train_data, net)
    # save them for later
    loss_seq_train.append(train_loss)
    loss_seq_test.append(test_loss)
    acc_seq_train.append(train_accuracy)
    acc_seq_test.append(test_accuracy)

    if e % 100 == 99:
        print("Completed epoch %s. Train Loss: %s, Test Loss %s, Train_acc %s, Test_acc %s" %
              (e+1, train_loss, test_loss, train_accuracy, test_accuracy))

## Plotting the learning curves
plot_learningcurves(loss_seq_train,loss_seq_test,acc_seq_train,acc_seq_test)
# output
Completed epoch 100. Train Loss: 0.5582709927111864, Test Loss 1.4102623425424097, Train_acc 0.862, Test_acc 0.725
Completed epoch 200. Train Loss: 0.2390711386688053, Test Loss 1.2993220016360283, Train_acc 0.94, Test_acc 0.734
Completed epoch 300. Train Loss: 0.13671867409721014, Test Loss 1.2758532278239725, Train_acc 0.971, Test_acc 0.748
Completed epoch 400. Train Loss: 0.09426628216169773, Test Loss 1.2602066472172737, Train_acc 0.989, Test_acc 0.758
Completed epoch 500. Train Loss: 0.05988468159921467, Test Loss 1.2470015566796062, Train_acc 0.996, Test_acc 0.764
Completed epoch 600. Train Loss: 0.043480587191879756, Test Loss 1.2396155279129744, Train_acc 0.998, Test_acc 0.762
Completed epoch 700. Train Loss: 0.032956544135231525, Test Loss 1.234715297818184, Train_acc 0.999, Test_acc 0.764
Completed epoch 800. Train Loss: 0.0268415825557895, Test Loss 1.2299001738429072, Train_acc 1.0, Test_acc 0.768
Completed epoch 900. Train Loss: 0.022739565349183977, Test Loss 1.2265239153057337, Train_acc 1.0, Test_acc 0.77
Completed epoch 1000. Train Loss: 0.019902906555216763, Test Loss 1.2242997065186503, Train_acc 1.0, Test_acc 0.772

From the above graph, you can easily get how the model is performing. From the above output, you can say at the 700th epoch, the model gives 100% accuracy on a dataset., this means it only able to classify 75% of the test examples accurately and 25% not. This is a clear high variance means overfitting. Methods to avoid overfitting:

  1. Cross-Validation
  2. Drop out
  3. Regularization

Regularization:

In the above section, we can able to identify the problem of overfitting. Now we know the problem and we also know what are the reasons for this. Now let us talk about the solution. In the regularisation, we will keep all the features but reduce the magnitude of parameters. Regularisation keeps the weights small keeping the model simpler to avoid overfitting. The model will have a lesser accurate if it is overfitting.

We have a linear regression to predicate y, given by plenty of x inputs.

y = a1x1 + a2x2  + a3x3 + a4x4 + a5x5.....

In the above equation a1, a2,….. are the coefficients and x1,x2,……are the independent variables to predicate dependent y.

“Regularisation means generalize the model for the better. “

“Mastering the trade-off between bias and variance is necessary to become a machine learning champion.”

Regularization is a scientific technique to discourage the complexity of the model ( reduce magnitude ). It does this by penalizing the loss function. What is mean by penalizing the loss function? Penalizing the weights makes them too small, almost near to zero. It makes those terms near to zero almost negligible and help us to simplify the model

The loss function is the sum of the squared difference between the predicted value and the actual value. ƛ is the regularization parameter which determines how much to penalizes the weights and the right value of ƛ is somewhere between 0 (zero) and large value.

There are few regularisation techniques.

  1. L1 Regularization or Lasso Regularization
  2. L2 Regularization or Ridge Regularization
  3. Dropout
  4. Data Augmentation
  5. Early stopping

We are solving the above overfitting problem using L2 regularisation technique.

Let us implement and solve the overfitting problem.

Penalizes the coefficient

# penalizes the coefficients
def l2_penalty(params):
 penalty = nd.zeros(shape=1)
 for param in params:
 penalty = penalty + nd.sum(param ** 2)
 return penalty

Reinitialize the parameter because for measures.

for param in params:
    param[:] = nd.random_normal(shape=param.shape)

L2 regularised logistic regression,

L2 regularization is the term of the sum of the square of all the features weight. Consider below formula. L2 regularization performs better when all the input features influence the output and all with weights are of approximately equal size.

Let us implement this L2 regularisation.

epochs = 1000
moving_loss = 0.
l2_strength = .1
niter=0
loss_seq_train = []
loss_seq_test = []
acc_seq_train = []
acc_seq_test = []

for e in range(epochs):
    for i, (data, label) in enumerate(train_data):
        data = data.as_in_context(ctx).reshape((-1,784))
        label = label.as_in_context(ctx)
        label_one_hot = nd.one_hot(label, 10)
        with autograd.record():
            output = net(data)
            loss = nd.sum(cross_entropy(output, label_one_hot)) + l2_strength * l2_penalty(params)
        loss.backward()
        SGD(params, .001)
        ##########################
        # Keep a moving average of the losses
        ##########################
        niter +=1
        moving_loss = .99 * moving_loss + .01 * nd.mean(loss).asscalar()
        est_loss = moving_loss/(1-0.99**niter)

    test_accuracy, test_loss = evaluate_accuracy(test_data, net)
    train_accuracy, train_loss = evaluate_accuracy(train_data, net)
    # save them for later
    loss_seq_train.append(train_loss)
    loss_seq_test.append(test_loss)
    acc_seq_train.append(train_accuracy)
    acc_seq_test.append(test_accuracy)
    if e % 100 == 99:
        print("Completed epoch %s. Train Loss: %s, Test Loss %s, Train_acc %s, Test_acc %s" %
              (e+1, train_loss, test_loss, train_accuracy, test_accuracy))

## Plotting the learning curves
plot_learningcurves(loss_seq_train,loss_seq_test,acc_seq_train,acc_seq_test)

Let us see the graph for more understanding. From the graph, you easily identify the difference between the training loss and testing loss and how values are closer in this graph.

Summary:

This chapter given is bit insight about the gluon API, ndArray along with inbuilt some of the neural network modules from gluon. With the completion of this chapter, you are now know How to create a simple artificial neural network using gluon abstraction. When to use regression and when to use classification technique along with some real-time dataset.

As a machine learning developer, the major problem we face is the overfitting and underfitting and this chapter gives us the regularisation tool to address this overfitting problem. Gluon is very concise, powerful abstraction to help us to design, prototype, built, deploy and test the machine learning module over GPU and CPU. We can now know how to set the context (GPU, CPU). We have solved classification problems such as binary classification and multiclass classification using logistic regression technique. Let us move on to the next adventure.