Machine learning with py torch
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The document provides a comprehensive introduction to PyTorch, a Python machine learning library focused on research and dynamic computation, showcasing its features, installation, handling datasets, and building neural networks. It covers basic concepts such as tensors, autograd for automatic differentiation, and hands-on examples like creating a feedforward neural network to classify the iris dataset. Additionally, it discusses loss functions and optimization techniques for training models in PyTorch.
![Tensors
Vector
Rank: 1
Dimension: (3,)
[3, 4, 8]Vector](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-15-320.jpg)
![Tensors
Vector
import torch
vector = torch.Tensor(3,)
print(vector)
# tensor([ 0.0000e+00, 3.6893e+19, -7.6570e-25])](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-16-320.jpg)
![Tensors
Matrix
Rank: 2
Dimension: (2, 3)
[[1, 2, 3],
[4, 5, 6]]Matrix](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-17-320.jpg)
![Tensors
Matrix
import torch
matrix = torch.Tensor(2, 3)
print(matrix)
# tensor([[0.0000e+00, 1.5846e+29, 2.8179e+26],
# [1.0845e-19, 4.2981e+21, 6.3828e+28]])](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-18-320.jpg)
![Tensors
Tensor
Rank: 3
Dimension: (2, 2, 3)
[[[1, 2, 3],
[4, 5, 6]], [[7, 8, 9],
[10, 11, 12]]]Tensor](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-19-320.jpg)
![Tensors
Tensor
import torch
tensor = torch.Tensor(2, 2, 3)
print(tensor)
# tensor([[[ 0.0000e+00, 3.6893e+19, 0.0000e+00],
# [ 3.6893e+19, 4.2039e-45, 3.6893e+19]],
# [[ 1.6986e+06, -2.8643e-42, 4.2981e+21],
# [ 6.3828e+28, 3.8016e-39, 2.7551e-40]]])](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-20-320.jpg)
![Working With
import torch
a = torch.ones(5)
print(a) # tensor([1., 1., 1., 1., 1.])
b = a.numpy()
print(b) # [1. 1. 1. 1. 1.]
import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a) "#[2. 2. 2. 2. 2.]
print(b)
#tensor([2., 2., 2., 2., 2.], dtype=torch.float64)](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-22-320.jpg)
![Differen7a7on
Refresher
y = f(x) = 2xIF THEN
dy
dx
= 2
IF THENy = f(x1, x2,…,xn) [
dy
dx1
,
dy
dx2
, . . . ,
dy
dxn
]
Is the gradient of y w.r.t [x1, x2, …, xn]](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-26-320.jpg)
![Variable
import torch
from torch.autograd import Variable
x = Variable(torch.FloatTensor([11.2]),
requires_grad=True)
y = 2 * x
print(x)
# tensor([11.2000], requires_grad=True)
print(y)
# tensor([22.4000], grad_fn=<MulBackward>)
print(x.data) # tensor([11.2000])
print(y.data) # tensor([22.4000])
print(x.grad_fn) # None
print(y.grad_fn)
# <MulBackward object at 0x10ae58e48>
y.backward() # Calculates the gradients
print(x.grad) # tensor([2.])](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-29-320.jpg)
![Access Dataset
Transform the data
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-38-320.jpg)
![Access Dataset
Prepare train data
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
print(len(trainset.train_data)) # 5000
print(trainset.train_labels[1]) # 9 = Truck image](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-39-320.jpg)
![Access Dataset
Iterate data and train the model
for i, data in enumerate(trainloader):
data, labels = data
print(type(data)) # <class 'torch.Tensor'>
print(data.size()) # torch.Size([10, 3, 32, 32])
print(type(labels)) # <class 'torch.Tensor'>
print(labels.size()) # torch.Size([10])
# Model training happens here""...](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-41-320.jpg)
![Back to Iris
Let’s Train The Neural Network
net.train() # Training mode
optimizer.zero_grad() # Reset gradients from past operation
outputs = net(items) # Forward pass
loss = criterion(outputs, classes) # Calculate the loss
loss.backward() # Calculate the gradient
optimizer.step() # Adjust weight based on gradients
train_total += classes.size(0)
_, predicted = torch.max(outputs.data, 1)
train_correct += (predicted "== classes.data).sum()
print('Epoch %d/%d, Iteration %d/%d, Loss: %.4f'
%(epoch+1, num_epochs, i+1,
len(train_ds)"//batch_size, loss.data[0]))](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-65-320.jpg)
![Back to Iris
Let’s Train The Neural Network
net.eval() # Put the network into evaluation mode
train_loss.append(loss.data[0])
train_accuracy.append((100 * train_correct / train_total))
# Record the testing loss
test_items = torch.FloatTensor(test_ds.data.values[:, 0:4])
test_classes = torch.LongTensor(test_ds.data.values[:, 4])
outputs = net(Variable(test_items))
loss = criterion(outputs, Variable(test_classes))
test_loss.append(loss.data[0])
# Record the testing accuracy
_, predicted = torch.max(outputs.data, 1)
total = test_classes.size(0)
correct = (predicted "== test_classes).sum()
test_accuracy.append((100 * correct / total))](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-66-320.jpg)
![Back to Iris
Let’s Train The Neural Network
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from torch.autograd import Variable
from data import iris
# Create the module
class IrisNet(nn.Module):
def "__init"__(self, input_size, hidden1_size, hidden2_size, num_classes):
super(IrisNet, self)."__init"__()
self.layer1 = nn.Linear(input_size, hidden1_size)
self.act1 = nn.ReLU()
self.layer2 = nn.Linear(hidden1_size, hidden2_size)
self.act2 = nn.ReLU()
self.layer3 = nn.Linear(hidden2_size, num_classes)
def forward(self, x):
out = self.layer1(x)
out = self.act1(out)
out = self.layer2(out)
out = self.act2(out)
out = self.layer3(out)
return out
# Create a model instance
model = IrisNet(4, 100, 50, 3)
print(model)
# Create the DataLoader
batch_size = 60
iris_data_file = 'data/iris.data.txt'
train_ds, test_ds = iris.get_datasets(iris_data_file)
print('# instances in training set: ', len(train_ds))
print('# instances in testing/validation set: ', len(test_ds))
train_loader = torch.utils.data.DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_ds, batch_size=batch_size, shuffle=True)
# Model
net = IrisNet(4, 100, 50, 3)
# Loss Function
criterion = nn.CrossEntropyLoss()
# Optimizer
learning_rate = 0.001
optimizer = torch.optim.SGD(net.parameters(),
lr=learning_rate,
nesterov=True,
momentum=0.9,
dampening=0)
# Training iteration
num_epochs = 500
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
for epoch in range(num_epochs):
train_correct = 0
train_total = 0
for i, (items, classes) in enumerate(train_loader):
# Convert torch tensor to Variable
items = Variable(items)
classes = Variable(classes)
net.train() # Training mode
optimizer.zero_grad() # Reset gradients from past operation
outputs = net(items) # Forward pass
loss = criterion(outputs, classes) # Calculate the loss
loss.backward() # Calculate the gradient
optimizer.step() # Adjust weight/parameter based on gradients
train_total += classes.size(0)
_, predicted = torch.max(outputs.data, 1)
train_correct += (predicted "== classes.data).sum()
print('Epoch %d/%d, Iteration %d/%d, Loss: %.4f'
%(epoch+1, num_epochs, i+1, len(train_ds)"//batch_size, loss.data[0]))
net.eval() # Put the network into evaluation mode
train_loss.append(loss.data[0])
train_accuracy.append((100 * train_correct / train_total))
# Record the testing loss
test_items = torch.FloatTensor(test_ds.data.values[:, 0:4])
test_classes = torch.LongTensor(test_ds.data.values[:, 4])
outputs = net(Variable(test_items))
loss = criterion(outputs, Variable(test_classes))
test_loss.append(loss.data[0])
# Record the testing accuracy
_, predicted = torch.max(outputs.data, 1)
total = test_classes.size(0)
correct = (predicted "== test_classes).sum()
test_accuracy.append((100 * correct / total))](https://image.slidesharecdn.com/machinelearningwithpytorch-181012233426/85/Machine-learning-with-py-torch-67-320.jpg)
Machine learning with py torch
- 2. Hi, I’m Riza
- 3. Agenda • Introduction PyTorch • Getting Started with PyTorch • Handling Datasets • Build Simple Neural network with PyTorch
- 4. Introduction
- 5. Scientific computing package to replace NumPy to use the power of GPU. A deep learning research platform that provide maximum flexibility and speed
- 6. A complete Python rewrite of Machine Learning library called Torch, written in Lua Chainer — Deep learning library, huge in NLP community, big inspiration to PyTorch Team HIPS Autograd - Automatic differentiation library, become on of big feature of PyTorch In need of dynamic execution
- 7. January 2017 PyTorch was born 🍼 July 2017 Kaggle Data Science Bowl won using PyTorch 🎉 August 2017 PyTorch 0.2 🚢 September 2017 fast.ai switch to PyTorch 🚀 October 2017 SalesForce releases QRNN 🖖 November 2017 Uber releases Pyro 🚗 December 2017 PyTorch 0.3 release! 🛳 2017 in review
- 9. Killer Features Just Python On Steroid Dynamic computation allows flexibility of input Best suited for research and prototyping
- 10. Summary • PyTorch is Python machine learning library focus on research purposes • Released on January 2017, used by tech companies and universities • Dynamic and pythonic way to do machine learning
- 11. Getting Started
- 12. Installa7on $ pip install pytorch torchvision -c pytorch # macos $ pip install pytorch-cpu torchvision-cpu -c pytorch # linux More: https://pytorch.org/get-started/locally/
- 14. Tensors Scalar import torch pi = torch.tensor(3.14) print(x) # tensor(3.1400)
- 15. Tensors Vector Rank: 1 Dimension: (3,) [3, 4, 8]Vector
- 16. Tensors Vector import torch vector = torch.Tensor(3,) print(vector) # tensor([ 0.0000e+00, 3.6893e+19, -7.6570e-25])
- 17. Tensors Matrix Rank: 2 Dimension: (2, 3) [[1, 2, 3], [4, 5, 6]]Matrix
- 18. Tensors Matrix import torch matrix = torch.Tensor(2, 3) print(matrix) # tensor([[0.0000e+00, 1.5846e+29, 2.8179e+26], # [1.0845e-19, 4.2981e+21, 6.3828e+28]])
- 19. Tensors Tensor Rank: 3 Dimension: (2, 2, 3) [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]Tensor
- 20. Tensors Tensor import torch tensor = torch.Tensor(2, 2, 3) print(tensor) # tensor([[[ 0.0000e+00, 3.6893e+19, 0.0000e+00], # [ 3.6893e+19, 4.2039e-45, 3.6893e+19]], # [[ 1.6986e+06, -2.8643e-42, 4.2981e+21], # [ 6.3828e+28, 3.8016e-39, 2.7551e-40]]])
- 21. Operators import torch x = torch.Tensor(5, 3) # Randomize Tensor y = torch.rand(5, 3) # Add print(x + y) # or print(torch.add(x, y)) # Matrix Multiplication a = torch.randn(2, 3) b = torch.randn(3, 3) print(torch.mm(a, b)) https://pytorch.org/docs/stable/tensors.html
- 22. Working With import torch a = torch.ones(5) print(a) # tensor([1., 1., 1., 1., 1.]) b = a.numpy() print(b) # [1. 1. 1. 1. 1.] import numpy as np a = np.ones(5) b = torch.from_numpy(a) np.add(a, 1, out=a) print(a) "#[2. 2. 2. 2. 2.] print(b) #tensor([2., 2., 2., 2., 2.], dtype=torch.float64)
- 23. Working With GPU import torch x = torch.Tensor(5, 3) y = torch.rand(5, 3) if torch.cuda.is_available(): x = x.cuda() y = y.cuda() x + y
- 24. Summary • Tensor is like rubiks or multidimentional array • Scalar, Vector, Matrix and Tensor is the same with different dimension • We can use torch.Tensor() to create a tensor.
- 25. Autograd
- 26. Differen7a7on Refresher y = f(x) = 2xIF THEN dy dx = 2 IF THENy = f(x1, x2,…,xn) [ dy dx1 , dy dx2 , . . . , dy dxn ] Is the gradient of y w.r.t [x1, x2, …, xn]
- 27. Autograd • Calculus chain rule on steroid • Derivative of function within a function • Complex functions can be written as many compositions of simple functions • Provides auto differentiation on all tensor operations • In torch.autograd module
- 28. Variable • Crucial data structure, needed for automatic differentiation • Wrapper around Tensor • Records reference to the creator function
- 29. Variable import torch from torch.autograd import Variable x = Variable(torch.FloatTensor([11.2]), requires_grad=True) y = 2 * x print(x) # tensor([11.2000], requires_grad=True) print(y) # tensor([22.4000], grad_fn=<MulBackward>) print(x.data) # tensor([11.2000]) print(y.data) # tensor([22.4000]) print(x.grad_fn) # None print(y.grad_fn) # <MulBackward object at 0x10ae58e48> y.backward() # Calculates the gradients print(x.grad) # tensor([2.])
- 30. Summary • Autograd provides auto differentiation on all tensor operations, inside torch.autograd module • Variable is wrapper around Tensor that will records reference to the creator function
- 32. Dataset Collection of training examples
- 33. Epochs One pass of the entire dataset through your model
- 34. Batch A subset of training examples passed through your model at a time
- 35. Itera7on A single pass of a batch
- 36. Example 1,000 images of dataset 1 epochs Batch size of 50 Leads to 20 iterations
- 37. Access Dataset CIFAR10 dataset via torchvision import torch import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt
- 38. Access Dataset Transform the data transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
- 39. Access Dataset Prepare train data trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) print(len(trainset.train_data)) # 5000 print(trainset.train_labels[1]) # 9 = Truck image
- 40. Access Dataset Prepare train loader trainloader = torch.utils.data.DataLoader( trainset, batch_size=10, shuffle=True, num_workers=2)
- 41. Access Dataset Iterate data and train the model for i, data in enumerate(trainloader): data, labels = data print(type(data)) # <class 'torch.Tensor'> print(data.size()) # torch.Size([10, 3, 32, 32]) print(type(labels)) # <class 'torch.Tensor'> print(labels.size()) # torch.Size([10]) # Model training happens here""...
- 42. Datasets • COCO — large-scale object detection, segmentation, and captioning dataset. • MNIST — handwritten digit database • Fashion-MNIST — fashion product database • LSUN — Large-scale Image Dataset • Much more… Others
- 43. Summary • We can use existing dataset provided by torch and torchvision such as CIFAR10 • Dataset is training example, epochs is on pass througout the model, batch is subset of training model and iteration is a single pass of one batch
- 44. Neural Network
- 45. Neural Network Input Hidden 1 Hidden 2 Output Layers
- 46. Neural Network A Neuron Output ip1 ip2 ip3 w1 w2 w3 ip1*w1 ip2*w2 ip3*w3 bias fn() Output = fn(w1 * ip1 + w2 * ip2 + w3* ip3 + bias) Input Vector Weights Vector Activation Function
- 47. Ac7va7on Func7ons Sigmoid f(x) = 1 1 + e−x
- 48. Ac7va7on Func7ons Tanh f(x) = ex − e−x ex + e−x
- 49. Ac7va7on Func7ons Rectified Linear Unit f(x) = max(x,0)
- 50. Summary • Neural net is a collection of neurons that related to each other consist of input, weight, bias and output. • To generate an output we need to activate it using activation function such as Sigmoid, tanh or ReLU.
- 51. Our First Neural Network
- 52. Feed Forward NN The Iris Classify flower based on it’s structure
- 53. Feed Forward NN The Model
- 54. Feed Forward NN The Dataset Table 1 sepal_length_cm sepal_width_cm petal_length_cm petal_width_cm class 5.1 3.5 1.4 0.2 Iris-setosa 4.9 3.0 1.4 0.2 Iris-setosa 7.0 3.2 4.7 1.4 Iris-versicolor 6.4 3.2 4.5 1.5 Iris-versicolor 6.9 3.1 4.9 1.5 Iris-versicolor 6.4 2.8 5.6 2.2 Iris-virginica 6.3 2.8 5.1 1.5 Iris-virginica 6.1 2.6 5.6 1.4 Iris-virginica
- 55. The Iris import torch import torch.nn as nn import matplotlib.pyplot as plt from torch.autograd import Variable from data import iris Import things
- 56. The Iris class IrisNet(nn.Module): def "__init"__(self, input_size, hidden1_size, hidden2_size, num_classes): super(IrisNet, self)."__init"__() self.layer1 = nn.Linear(input_size, hidden1_size) self.act1 = nn.ReLU() self.layer2 = nn.Linear(hidden1_size, hidden2_size) self.act2 = nn.ReLU() self.layer3 = nn.Linear(hidden2_size, num_classes) def forward(self, x): out = self.layer1(x) out = self.act1(out) out = self.layer2(out) out = self.act2(out) out = self.layer3(out) return out model = IrisNet(4, 100, 50, 3) print(model) Create Module and Instance
- 57. The Iris batch_size = 60 iris_data_file = 'data/iris.data.txt' train_ds, test_ds = iris.get_datasets(iris_data_file) train_loader = torch.utils.data.DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_ds, batch_size=batch_size, shuffle=True) DataLoader
- 58. Loss Func7on Output/Prediction Actual Result Loss Function Loss Score Input
- 59. Loss Func7on In PyTorch • L1Loss • MSELoss • CrossEntropyLoss • BCELoss • SoftMarginLoss • More: https://pytorch.org/docs/stable/nn.html?#loss-functions
- 60. Loss Func7on CrossEntropyLoss Measures the performance of a classification model whose output is a probability value between 0 and 1. 🍎 🍌 🍍 Prediction 0.02 0.88 0.1 Actual 🍌 Loss Score 0.98 0.12 0.9
- 61. Op7mizer Func7on Output/Prediction Actual Result Loss Function Loss Score Input Calculate Gradients Optimizer Iteration
- 62. Op7mizer Func7on In PyTorch • Socastic Gradient Descend (SGD) • Adam • Adadelta • RMSprop
- 63. Back to Iris Let’s Train The Neural Network net = IrisNet(4, 100, 50, 3) # Loss Function criterion = nn.CrossEntropyLoss() # Optimizer learning_rate = 0.001 optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, nesterov=True, momentum=0.9, dampening=0)
- 64. Back to Iris Let’s Train The Neural Network num_epochs = 500 for epoch in range(num_epochs): train_correct = 0 train_total = 0 for i, (items, classes) in enumerate(train_loader): # Convert torch tensor to Variable items = Variable(items) classes = Variable(classes)
- 65. Back to Iris Let’s Train The Neural Network net.train() # Training mode optimizer.zero_grad() # Reset gradients from past operation outputs = net(items) # Forward pass loss = criterion(outputs, classes) # Calculate the loss loss.backward() # Calculate the gradient optimizer.step() # Adjust weight based on gradients train_total += classes.size(0) _, predicted = torch.max(outputs.data, 1) train_correct += (predicted "== classes.data).sum() print('Epoch %d/%d, Iteration %d/%d, Loss: %.4f' %(epoch+1, num_epochs, i+1, len(train_ds)"//batch_size, loss.data[0]))
- 66. Back to Iris Let’s Train The Neural Network net.eval() # Put the network into evaluation mode train_loss.append(loss.data[0]) train_accuracy.append((100 * train_correct / train_total)) # Record the testing loss test_items = torch.FloatTensor(test_ds.data.values[:, 0:4]) test_classes = torch.LongTensor(test_ds.data.values[:, 4]) outputs = net(Variable(test_items)) loss = criterion(outputs, Variable(test_classes)) test_loss.append(loss.data[0]) # Record the testing accuracy _, predicted = torch.max(outputs.data, 1) total = test_classes.size(0) correct = (predicted "== test_classes).sum() test_accuracy.append((100 * correct / total))
- 67. Back to Iris Let’s Train The Neural Network import torch import torch.nn as nn import matplotlib.pyplot as plt from torch.autograd import Variable from data import iris # Create the module class IrisNet(nn.Module): def "__init"__(self, input_size, hidden1_size, hidden2_size, num_classes): super(IrisNet, self)."__init"__() self.layer1 = nn.Linear(input_size, hidden1_size) self.act1 = nn.ReLU() self.layer2 = nn.Linear(hidden1_size, hidden2_size) self.act2 = nn.ReLU() self.layer3 = nn.Linear(hidden2_size, num_classes) def forward(self, x): out = self.layer1(x) out = self.act1(out) out = self.layer2(out) out = self.act2(out) out = self.layer3(out) return out # Create a model instance model = IrisNet(4, 100, 50, 3) print(model) # Create the DataLoader batch_size = 60 iris_data_file = 'data/iris.data.txt' train_ds, test_ds = iris.get_datasets(iris_data_file) print('# instances in training set: ', len(train_ds)) print('# instances in testing/validation set: ', len(test_ds)) train_loader = torch.utils.data.DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_ds, batch_size=batch_size, shuffle=True) # Model net = IrisNet(4, 100, 50, 3) # Loss Function criterion = nn.CrossEntropyLoss() # Optimizer learning_rate = 0.001 optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, nesterov=True, momentum=0.9, dampening=0) # Training iteration num_epochs = 500 train_loss = [] test_loss = [] train_accuracy = [] test_accuracy = [] for epoch in range(num_epochs): train_correct = 0 train_total = 0 for i, (items, classes) in enumerate(train_loader): # Convert torch tensor to Variable items = Variable(items) classes = Variable(classes) net.train() # Training mode optimizer.zero_grad() # Reset gradients from past operation outputs = net(items) # Forward pass loss = criterion(outputs, classes) # Calculate the loss loss.backward() # Calculate the gradient optimizer.step() # Adjust weight/parameter based on gradients train_total += classes.size(0) _, predicted = torch.max(outputs.data, 1) train_correct += (predicted "== classes.data).sum() print('Epoch %d/%d, Iteration %d/%d, Loss: %.4f' %(epoch+1, num_epochs, i+1, len(train_ds)"//batch_size, loss.data[0])) net.eval() # Put the network into evaluation mode train_loss.append(loss.data[0]) train_accuracy.append((100 * train_correct / train_total)) # Record the testing loss test_items = torch.FloatTensor(test_ds.data.values[:, 0:4]) test_classes = torch.LongTensor(test_ds.data.values[:, 4]) outputs = net(Variable(test_items)) loss = criterion(outputs, Variable(test_classes)) test_loss.append(loss.data[0]) # Record the testing accuracy _, predicted = torch.max(outputs.data, 1) total = test_classes.size(0) correct = (predicted "== test_classes).sum() test_accuracy.append((100 * correct / total))
- 69. Summary • Created feed forward neural network to predict a type of flower • Start from read the dataset and dataloader • Choose a loss function and optimizer • Train and evaluation
- 70. What’s Next?! • pytorch.org/tutorials • course.fast.ai • coursera.org/learn/machine-learning • kaggle.com/datasets
- 71. That’s All From me github.com/rizafahmi slideshare.net/rizafahmi [email protected] twi8er.com/rizafahmi22 facebook.com/rizafahmi