Machine learning with py torch
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Introducing PyTorch and build Feed Forward Neural Network at Facebook Developer Circles Jakarta meetup
![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)
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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