Category: pytorch

PyTorch – Loading Data ”; Previous Next PyTorch includes a package called torchvision which is used to load and prepare the dataset. It includes two basic functions namely Dataset and DataLoader which helps in transformation and loading of dataset. Dataset Dataset is used to read and transform a datapoint from the given dataset. The basic syntax to implement is mentioned below − trainset = torchvision.datasets.CIFAR10(root = ”./data”, train = True, download = True, transform = transform) DataLoader is used to shuffle and batch data. It can be used to load the data in parallel with multiprocessing workers. trainloader = torch.utils.data.DataLoader(trainset, batch_size = 4, shuffle = True, num_workers = 2) Example: Loading CSV File We use the Python package Panda to load the csv file. The original file has the following format: (image name, 68 landmarks – each landmark has a x, y coordinates). landmarks_frame = pd.read_csv(”faces/face_landmarks.csv”) n = 65 img_name = landmarks_frame.iloc[n, 0] landmarks = landmarks_frame.iloc[n, 1:].as_matrix() landmarks = landmarks.astype(”float”).reshape(-1, 2) Print Page Previous Next Advertisements ”;

PyTorch – Installation ”; Previous Next PyTorch is a popular deep learning framework. In this tutorial, we consider “Windows 10” as our operating system. The steps for a successful environmental setup are as follows − Step 1 The following link includes a list of packages which includes suitable packages for PyTorch. https://drive.google.com/drive/folders/0B-X0-FlSGfCYdTNldW02UGl4MXM All you need to do is download the respective packages and install it as shown in the following screenshots − Step 2 It involves verifying the installation of PyTorch framework using Anaconda Framework. Following command is used to verify the same − conda list “Conda list” shows the list of frameworks which is installed. The highlighted part shows that PyTorch has been successfully installed in our system. Print Page Previous Next Advertisements ”;

PyTorch Tutorial PDF Version Quick Guide Resources Job Search Discussion PyTorch is an open source machine learning library for Python and is completely based on Torch. It is primarily used for applications such as natural language processing. PyTorch is developed by Facebook”s artificial-intelligence research group along with Uber”s “Pyro” software for the concept of in-built probabilistic programming. Audience This tutorial has been prepared for python developers who focus on research and development with machinelearning algorithms along with natural language processing system. The aim of this tutorial is to completely describe all concepts of PyTorch and realworld examples of the same. Prerequisites Before proceeding with this tutorial, you need knowledge of Python and Anaconda framework (commands used in Anaconda). Having knowledge of artificial intelligence concepts will be an added advantage. Print Page Previous Next Advertisements ”;

Universal Workflow of Machine Learning ”; Previous Next Artificial Intelligence is trending nowadays to a greater extent. Machine learning and deep learning constitutes artificial intelligence. The Venn diagram mentioned below explains the relationship of machine learning and deep learning. Machine Learning Machine learning is the art of science which allows computers to act as per the designed and programmed algorithms. Many researchers think machine learning is the best way to make progress towards human-level AI. It includes various types of patterns like − Supervised Learning Pattern Unsupervised Learning Pattern Deep Learning Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structure and function of the brain called Artificial Neural Networks. Deep learning has gained much importance through supervised learning or learning from labelled data and algorithms. Each algorithm in deep learning goes through same process. It includes hierarchy of nonlinear transformation of input and uses to create a statistical model as output. Machine learning process is defined using following steps − Identifies relevant data sets and prepares them for analysis. Chooses the type of algorithm to use. Builds an analytical model based on the algorithm used. Trains the model on test data sets, revising it as needed. Runs the model to generate test scores. Print Page Previous Next Advertisements ”;

PyTorch – Linear Regression ”; Previous Next In this chapter, we will be focusing on basic example of linear regression implementation using TensorFlow. Logistic regression or linear regression is a supervised machine learning approach for the classification of order discrete categories. Our goal in this chapter is to build a model by which a user can predict the relationship between predictor variables and one or more independent variables. The relationship between these two variables is considered linear i.e., if y is the dependent variable and x is considered as the independent variable, then the linear regression relationship of two variables will look like the equation which is mentioned as below − Y = Ax+b Next, we shall design an algorithm for linear regression which allows us to understand two important concepts given below − Cost Function Gradient Descent Algorithms The schematic representation of linear regression is mentioned below Interpreting the result $$Y=ax+b$$ The value of a is the slope. The value of b is the y − intercept. r is the correlation coefficient. r2 is the correlation coefficient. The graphical view of the equation of linear regression is mentioned below − Following steps are used for implementing linear regression using PyTorch − Step 1 Import the necessary packages for creating a linear regression in PyTorch using the below code − import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation import seaborn as sns import pandas as pd %matplotlib inline sns.set_style(style = ”whitegrid”) plt.rcParams[“patch.force_edgecolor”] = True Step 2 Create a single training set with the available data set as shown below − m = 2 # slope c = 3 # interceptm = 2 # slope c = 3 # intercept x = np.random.rand(256) noise = np.random.randn(256) / 4 y = x * m + c + noise df = pd.DataFrame() df[”x”] = x df[”y”] = y sns.lmplot(x =”x”, y =”y”, data = df) Step 3 Implement linear regression with PyTorch libraries as mentioned below − import torch import torch.nn as nn from torch.autograd import Variable x_train = x.reshape(-1, 1).astype(”float32”) y_train = y.reshape(-1, 1).astype(”float32”) class LinearRegressionModel(nn.Module): def __init__(self, input_dim, output_dim): super(LinearRegressionModel, self).__init__() self.linear = nn.Linear(input_dim, output_dim) def forward(self, x): out = self.linear(x) return out input_dim = x_train.shape[1] output_dim = y_train.shape[1] input_dim, output_dim(1, 1) model = LinearRegressionModel(input_dim, output_dim) criterion = nn.MSELoss() [w, b] = model.parameters() def get_param_values(): return w.data[0][0], b.data[0] def plot_current_fit(title = “”): plt.figure(figsize = (12,4)) plt.title(title) plt.scatter(x, y, s = 8) w1 = w.data[0][0] b1 = b.data[0] x1 = np.array([0., 1.]) y1 = x1 * w1 + b1 plt.plot(x1, y1, ”r”, label = ”Current Fit ({:.3f}, {:.3f})”.format(w1, b1)) plt.xlabel(”x (input)”) plt.ylabel(”y (target)”) plt.legend() plt.show() plot_current_fit(”Before training”) The plot generated is as follows − Print Page Previous Next Advertisements ”;