This blog explains about how to compile the model. The compilation is the final step in creating a model. Once the compilation is done, we can move on to training phase.

Let us learn few concepts required to better understand the compilation process.

Loss

In machine learning, Loss function is used to find error or deviation in the learning process. Keras requires loss function during model compilation process.

Keras provides quite a few loss functions in the losses module and they are as follows −

mean_squared_error
mean_absolute_error
mean_absolute_percentage_error
mean_squared_logarithmic_error
squared_hinge
hinge
categorical_hinge
logcosh
huber_loss
categorical_crossentropy
sparse_categorical_crossentropy
binary_crossentropy
kullback_leibler_divergence
poisson
cosine_proximity
is_categorical_crossentropy

All above loss function accepts two arguments −

y_true − true labels as tensors
y_pred − prediction with same shape as y_true

Import the losses module before using loss function as specified below −

from keras import losses

Optimizer

In machine learning, Optimization is an important process which optimize the input weights by comparing the prediction and the loss function. Keras provides quite a few optimizer as a module, optimizers and they are as follows:

SGD − Stochastic gradient descent optimizer.

keras.optimizers.SGD(learning_rate = 0.01, momentum = 0.0, nesterov = False)

RMSprop − RMSProp optimizer.

keras.optimizers.RMSprop(learning_rate = 0.001, rho = 0.9)

Adagrad − Adagrad optimizer.

keras.optimizers.Adagrad(learning_rate = 0.01)

Adadelta − Adadelta optimizer.

keras.optimizers.Adadelta(learning_rate = 1.0, rho = 0.95)

Adam − Adam optimizer.

keras.optimizers.Adam(

learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999, amsgrad = False

)

Adamax − Adamax optimizer from Adam.

keras.optimizers.Adamax(learning_rate = 0.002, beta_1 = 0.9, beta_2 = 0.999)

Nadam − Nesterov Adam optimizer.

keras.optimizers.Nadam(learning_rate = 0.002, beta_1 = 0.9, beta_2 = 0.999)

Import the optimizers module before using optimizers as specified below −

from keras import optimizers

Metrics

In machine learning, Metrics is used to evaluate the performance of your model. It is similar to loss function, but not used in training process. Keras provides quite a few metrics as a module, metrics and they are as follows

accuracy
binary_accuracy
categorical_accuracy
sparse_categorical_accuracy
top_k_categorical_accuracy
sparse_top_k_categorical_accuracy
cosine_proximity
clone_metric

Similar to loss function, metrics also accepts below two arguments −

y_true − true labels as tensors
y_pred − prediction with same shape as y_true

Import the metrics module before using metrics as specified below −

from keras import metrics

Compile the model

Keras model provides a method, compile() to compile the model. The argument and default value of the compile() method is as follows

compile(

optimizer,

loss = None,

metrics = None,

loss_weights = None,

sample_weight_mode = None,

weighted_metrics = None,

target_tensors = None

)

The important arguments are as follows −

loss function
Optimizer
metrics

A sample code to compile the mode is as follows −

from keras import losses
from keras import optimizers
from keras import metrics
model.compile(loss = 'mean_squared_error',
optimizer = 'sgd', metrics = [metrics.categorical_accuracy])

where,

loss function is set as mean_squared_error
optimizer is set as sgd
metrics is set as metrics.categorical_accuracy

Model Training

Models are trained by NumPy arrays using fit(). The main purpose of this fit function is used to evaluate your model on training. This can be also used for graphing model performance. It has the following syntax −

model.fit(X, y, epochs = , batch_size = )

Here,

X, y − It is a tuple to evaluate your data.
epochs − no of times the model is needed to be evaluated during training.
batch_size − training instances.

Let us take a simple example of numpy random data to use this concept.

Create data
Let us create a random data using numpy for x and y with the help of below mentioned command −
import numpy as np
x_train = np.random.random((100,4,8))
y_train = np.random.random((100,10))
Now, create random validation data,
x_val = np.random.random((100,4,8))
y_val = np.random.random((100,10))
Create model
Let us create simple sequential model −
from keras.models import Sequential model = Sequential()
Add layers
Create layers to add model −
from keras.layers import LSTM, Dense
# add a sequence of vectors of dimension 16
model.add(LSTM(16, return_sequences = True))
model.add(Dense(10, activation = 'softmax'))
compile model
Now model is defined. You can compile using the below command −
model.compile(
loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy']
)
Apply fit()
Now we apply fit() function to train our data −
model.fit(x_train, y_train, batch_size = 32, epochs = 5, validation_data = (x_val, y_val))

Create a Multi-Layer Perceptron ANN

We have learned to create, compile and train the Keras models.

Let us apply our learning and create a simple MPL based ANN.

Dataset module

Before creating a model, we need to choose a problem, need to collect the required data and convert the data to NumPy array. Once data is collected, we can prepare the model and train it by using the collected data. Data collection is one of the most difficult phase of machine learning. Keras provides a special module, datasets to download the online machine learning data for training purposes. It fetches the data from online server, process the data and return the data as training and test set. Let us check the data provided by Keras dataset module. The data available in the module are as follows,

CIFAR10 small image classification
CIFAR100 small image classification
IMDB Movie reviews sentiment classification
Reuters newswire topics classification
MNIST database of handwritten digits
Fashion-MNIST database of fashion articles
Boston housing price regression dataset

Let us use the MNIST database of handwritten digits (or minst) as our input. minst is a collection of 60,000, 28×28 grayscale images. It contains 10 digits. It also contains 10,000 test images.

Below code can be used to load the dataset −

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

where

Line 1 imports minst from the keras dataset module.
Line 3 calls the load_data function, which will fetch the data from online server and return the data as 2 tuples, First tuple, (x_train, y_train) represent the training data with shape, (number_sample, 28, 28) and its digit label with shape, (number_samples, ). Second tuple, (x_test, y_test) represent test data with same shape.

Other dataset can also be fetched using similar API and every API returns similar data as well except the shape of the data. The shape of the data depends on the type of data.

Create a model

Let us choose a simple multi-layer perceptron (MLP) as represented below and try to create the model using Keras.

keras 7 1

The core features of the model are as follows −

Input layer consists of 784 values (28 x 28 = 784).
First hidden layer, Dense consists of 512 neurons and ‘relu’ activation function.
Second hidden layer, Dropout has 0.2 as its value.
Third hidden layer, again Dense consists of 512 neurons and ‘relu’ activation function.
Fourth hidden layer, Dropout has 0.2 as its value.
Fifth and final layer consists of 10 neurons and ‘softmax’ activation function.
Use categorical_crossentropy as loss function.
Use RMSprop() as Optimizer.
Use accuracy as metrics.
Use 128 as batch size.
Use 20 as epochs.

Step 1 − Import the modules
Let us import the necessary modules.
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import numpy as np
Step 2 − Load data
Let us import the mnist dataset.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
Step 3 − Process the data
Let us change the dataset according to our model, so that it can be feed into our model.
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
Where
reshape is used to reshape the input from (28, 28) tuple to (784, )
to_categorical is used to convert vector to binary matrix
Step 4 − Create the model
Let us create the actual model.
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation = 'softmax'))
Step 5 − Compile the model
Let us compile the model using selected loss function, optimizer and metrics.
model.compile(loss = 'categorical_crossentropy',
optimizer = RMSprop(),
metrics = ['accuracy'])
Step 6 − Train the model
Let us train the model using fit() method.
history = model.fit(
x_train, y_train,
batch_size = 128,
epochs = 20,
verbose = 1,
validation_data = (x_test, y_test)
)

Final thoughts

We have created the model, loaded the data and also trained the data to the model. We still need to evaluate the model and predict output for unknown input.

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import numpy as np
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Dense(512, activation='relu', input_shape = (784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation = 'relu')) model.add(Dropout(0.2))
model.add(Dense(10, activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy',
optimizer = RMSprop(),
metrics = ['accuracy'])
history = model.fit(x_train, y_train,
batch_size = 128, epochs = 20, verbose = 1, validation_data = (x_test, y_test))
Executing the application will give the below content as output −
Train on 60000 samples, validate on 10000 samples Epoch 1/20
60000/60000 [==============================] - 7s 118us/step - loss: 0.2453
- acc: 0.9236 - val_loss: 0.1004 - val_acc: 0.9675 Epoch 2/20
60000/60000 [==============================] - 7s 110us/step - loss: 0.1023
- acc: 0.9693 - val_loss: 0.0797 - val_acc: 0.9761 Epoch 3/20
60000/60000 [==============================] - 7s 110us/step - loss: 0.0744
- acc: 0.9770 - val_loss: 0.0727 - val_acc: 0.9791 Epoch 4/20
60000/60000 [==============================] - 7s 110us/step - loss: 0.0599
- acc: 0.9823 - val_loss: 0.0704 - val_acc: 0.9801 Epoch 5/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0504
- acc: 0.9853 - val_loss: 0.0714 - val_acc: 0.9817 Epoch 6/20
60000/60000 [==============================] - 7s 111us/step - loss: 0.0438
- acc: 0.9868 - val_loss: 0.0845 - val_acc: 0.9809 Epoch 7/20
60000/60000 [==============================] - 7s 114us/step - loss: 0.0391
- acc: 0.9887 - val_loss: 0.0823 - val_acc: 0.9802 Epoch 8/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0364
- acc: 0.9892 - val_loss: 0.0818 - val_acc: 0.9830 Epoch 9/20
60000/60000 [==============================] - 7s 113us/step - loss: 0.0308
- acc: 0.9905 - val_loss: 0.0833 - val_acc: 0.9829 Epoch 10/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0289
- acc: 0.9917 - val_loss: 0.0947 - val_acc: 0.9815 Epoch 11/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0279
- acc: 0.9921 - val_loss: 0.0818 - val_acc: 0.9831 Epoch 12/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0260
- acc: 0.9927 - val_loss: 0.0945 - val_acc: 0.9819 Epoch 13/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0257
- acc: 0.9931 - val_loss: 0.0952 - val_acc: 0.9836 Epoch 14/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0229
- acc: 0.9937 - val_loss: 0.0924 - val_acc: 0.9832 Epoch 15/20
60000/60000 [==============================] - 7s 115us/step - loss: 0.0235
- acc: 0.9937 - val_loss: 0.1004 - val_acc: 0.9823 Epoch 16/20
60000/60000 [==============================] - 7s 113us/step - loss: 0.0214
- acc: 0.9941 - val_loss: 0.0991 - val_acc: 0.9847 Epoch 17/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0219
- acc: 0.9943 - val_loss: 0.1044 - val_acc: 0.9837 Epoch 18/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0190
- acc: 0.9952 - val_loss: 0.1129 - val_acc: 0.9836 Epoch 19/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0197
- acc: 0.9953 - val_loss: 0.0981 - val_acc: 0.9841 Epoch 20/20
60000/60000 [==============================] - 7s 112us/step - loss: 0.0198
- acc: 0.9950 - val_loss: 0.1215 - val_acc: 0.9828

So, this brings us to the end of blog. This Tecklearn ‘Detailed understanding of the Keras Model compilation process’ 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/

Artificial Intelligence and Deep Learning with TensorFlow Training

About the Course

Tecklearn’s Artificial Intelligence and Deep Learning with Tensor Flow course is curated by industry professionals as per the industry requirements & demands and aligned with the latest best practices. You’ll master convolutional neural networks (CNN), TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects. The trainee will learn AI by mastering natural language processing, deep neural networks, predictive analytics, reinforcement learning, and more programming languages needed to shine in this field.

Why Should you take Artificial Intelligence and Deep Learning with Tensor Flow Training?

According to Paysa.com, an Artificial Intelligence Engineer earns an average of $171,715, ranging from $124,542 at the 25th percentile to $201,853 at the 75th percentile, with top earners earning more than $257,530.
Worldwide Spending on Artificial Intelligence Systems Will Be Nearly $98 Billion in 2023, According to New IDC Spending Guide at a GACR of 28.5%.
IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle and almost all the leading companies are working on Artificial Intelligence to innovate future technologies.

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

Got a question for us? Please mention it in the comments section and we will get back to you.

616