Deep-Learning-with-PyTorch

122 CHAPTER 5 The mechanics of learning
5.4.5 Visualizing (again)
Let’s revisit something we did right at the start: plotting our data. Seriously, this is the
first thing anyone doing data science should do. Always plot the heck out of the data:
# In[22]:
%matplotlib inline
from matplotlib import pyplot as plt
t_p = model(t_un, *params)
Remember that we’re training on the
normalized unknown units. We also
use argument unpacking.
fig = plt.figure(dpi=600)
plt.xlabel("Temperature (°Fahrenheit)")
plt.ylabel("Temperature (°Celsius)")
plt.plot(t_u.numpy(), t_p.detach().numpy())
plt.plot(t_u.numpy(), t_c.numpy(), 'o')
But we’re plotting the
raw unknown values.
We are using a Python trick called argument unpacking here: *params means to pass the
elements of params as individual arguments. In Python, this is usually done with lists
or tuples, but we can also use argument unpacking with PyTorch tensors, which are
split along the leading dimension. So here, model(t_un, *params) is equivalent to
model(t_un, params[0], params[1]).
This code produces figure 5.9. Our linear model is a good model for the data, it
seems. It also seems our measurements are somewhat erratic. We should either call
our optometrist for a new pair of glasses or think about returning our fancy thermometer.
25
temperature (°CELSIUS)
20
15
10
5
0
-5
20 30 40 50 60 70 80
temperature (°fahrenheit)
Figure 5.9
The plot of our linear-fit model (solid line) versus our input data (circles)