In this lab, you will train a model with PyTorch by using data that you created. The model only has one parameter: the slope.
Estimated Time Needed: 20 min
The following are the libraries we are going to use for this lab.
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# These are the libraries will be used for this lab.
import numpy as np
import matplotlib.pyplot as plt
The class plot_diagram
helps us to visualize the data space and the parameter space during training and has nothing to do with PyTorch.
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# The class for plotting
class plot_diagram():
# Constructor
def __init__(self, X, Y, w, stop, go = False):
start = w.data
self.error = []
self.parameter = []
self.X = X.numpy()
self.Y = Y.numpy()
self.parameter_values = torch.arange(start, stop)
self.Loss_function = [criterion(forward(X), Y) for w.data in self.parameter_values]
w.data = start
# Executor
def __call__(self, Yhat, w, error, n):
self.error.append(error)
self.parameter.append(w.data)
plt.subplot(212)
plt.plot(self.X, Yhat.detach().numpy())
plt.plot(self.X, self.Y,'ro')
plt.xlabel("A")
plt.ylim(-20, 20)
plt.subplot(211)
plt.title("Data Space (top) Estimated Line (bottom) Iteration " + str(n))
plt.plot(self.parameter_values.numpy(), self.Loss_function)
plt.plot(self.parameter, self.error, 'ro')
plt.xlabel("B")
plt.figure()
# Destructor
def __del__(self):
plt.close('all')
Import PyTorch library:
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# Import the library PyTorch
import torch
Generate values from -3 to 3 that create a line with a slope of -3. This is the line you will estimate.
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# Create the f(X) with a slope of -3
X = torch.arange(-3, 3, 0.1).view(-1, 1)
f = -3 * X
Let us plot the line.
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# Plot the line with blue
plt.plot(X.numpy(), f.numpy(), label = 'f')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
Let us add some noise to the data in order to simulate the real data. Use torch.randn(X.size())
to generate Gaussian noise that is the same size as X
and has a standard deviation opf 0.1.
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# Add some noise to f(X) and save it in Y
Y = f + 0.1 * torch.randn(X.size())
Plot the Y
:
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# Plot the data points
plt.plot(X.numpy(), Y.numpy(), 'rx', label = 'Y')
plt.plot(X.numpy(), f.numpy(), label = 'f')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
In this section, let us create the model and the cost function (total loss) we are going to use to train the model and evaluate the result.
First, define the forward
function $y=w*x$. (We will add the bias in the next lab.)
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# Create forward function for prediction
def forward(x):
return w * x
Define the cost or criterion function using MSE (Mean Square Error):
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# Create the MSE function for evaluate the result.
def criterion(yhat, y):
return torch.mean((yhat - y) ** 2)
Define the learning rate lr
and an empty list LOSS
to record the loss for each iteration:
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# Create Learning Rate and an empty list to record the loss for each iteration
lr = 0.1
LOSS = []
Now, we create a model parameter by setting the argument requires_grad
to True
because the system must learn it.
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w = torch.tensor(-10.0, requires_grad = True)
Create a plot_diagram
object to visualize the data space and the parameter space for each iteration during training:
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gradient_plot = plot_diagram(X, Y, w, stop = 5)
Let us define a function for training the model. The steps will be described in the comments.
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# Define a function for train the model
def train_model(iter):
for epoch in range (iter):
# make the prediction as we learned in the last lab
Yhat = forward(X)
# calculate the iteration
loss = criterion(Yhat,Y)
# plot the diagram for us to have a better idea
gradient_plot(Yhat, w, loss.item(), epoch)
# store the loss into list
LOSS.append(loss)
# backward pass: compute gradient of the loss with respect to all the learnable parameters
loss.backward()
# updata parameters
w.data = w.data - lr * w.grad.data
# zero the gradients before running the backward pass
w.grad.data.zero_()
Let us try to run 4 iterations of gradient descent:
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# Give 4 iterations for training the model here.
train_model(4)
Plot the cost for each iteration:
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# Plot the loss for each iteration
plt.plot(LOSS)
plt.tight_layout()
plt.xlabel("Epoch/Iterations")
plt.ylabel("Cost")
Create a new learnable parameter w
with an initial value of -15.0.
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# Practice: Create w with the inital value of -15.0
# Type your code here
Double-click here for the solution.
Create an empty list LOSS2
:
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# Practice: Create LOSS2 list
# Type your code here
Double-click here for the solution.
Write your own my_train_model
function with loss list LOSS2
. And run it with 4 iterations.
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# Practice: Create your own my_train_model
gradient_plot1 = plot_diagram(X, Y, w, stop = 15)
Double-click here for the solution.
Plot an overlay of the list LOSS2
and LOSS
.
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# Practice: Plot the list LOSS2 and LOSS
# Type your code here
Double-click here for the solution.
What does this tell you about the parameter value?
Double-click here for the solution.
Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Other contributors: Michelle Carey, Mavis Zhou
Copyright © 2018 cognitiveclass.ai. This notebook and its source code are released under the terms of the MIT License.