This article is focused on showing how a point on the loss surface is equivalent to a line between X and Y. The example that I have taken here is of a simple linear regression model between 2 variables sunshine (in hours) and attendance (in thousands).

```
# Import libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('sunshine.csv')
# Check the data
dataset.head()
# Check correlation between dependent and independent variables
dataset.corr()
# Assign columns to X and y
X = dataset.iloc[:, [0]].values
y = dataset.iloc[:, 1].values
print(X.shape)
print(y.shape)
# Check the scatter plot
plt.scatter(X, y)
plt.xlabel("Sunshine in hrs")
plt.ylabel("Attendance in '000s")
plt.title("Sunshine vs Attendance")
plt.show()
# Create LinearRegression model
from sklearn.linear_model import LinearRegression
# Create linear regression object
model = LinearRegression()
model.fit(X, y)
print(model.coef_)
print(model.intercept_)
# Draw the predicted line
plt.scatter(X, y)
plt.plot(X, model.predict(X))
plt.xlabel("Sunshine in hrs")
plt.ylabel("Attendance in '000s")
plt.title("Sunshine vs Attendance")
plt.show()
```

Now the best-fit line has a loss which is defined as the Least Sum of Squared Errors i.e. L2 loss which has the formula.

Min of** ****Σ**(Actual y – Predicted y)2

So for coefficient 5.45 the loss is

Let’s plot this loss against the coefficient and our regression line side-by-side

```
loss = sum((y - ypred)**2)
plt.scatter(model.coef_, loss)
plt.xlabel('w')
plt.ylabel('loss')
plt.show()
```

Now, let's change the coefficient range from 2.5 to 9 and plot the different lines that we get.

So for each coefficient, you get a line and a corresponding loss. So each loss point on the LHS figure is actually a regression line on the RHS figure. We have ignored the bias/intercept so far in this visualization.

## Plotting L2 loss

Suppose we plot for the bias, we will follow the curve. The L2 loss function is quadratic in nature hence we get a shaped curve.

```
slope = np.arange(2.5, 7.5, 0.5)
bias = np.arange(13.2, 18, 0.5)
w0, w1 = np.meshgrid(slope, bias)
ypred = w0 * X + w1
loss = np.power((y - ypred), 2)
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(w0,
w1,
loss,
label="Loss surface",
cmap='viridis',
edgecolor='none')
surf._facecolors2d = surf._facecolors3d
surf._edgecolors2d = surf._edgecolors3d
ax.set_xlabel('Slope')
ax.set_ylabel('Bias')
ax.legend()
```

Geometrically loss function is a convex function as shown above.

## Plotting L1 Loss

Similarly, you can plot the L1 loss which is abs(y-ypred). Here there is no quadratic term. So how does the geometry of this loss function look? It looks V-shaped.

You can visualize the other loss functions in the same way.

I have made a video on this topic and uploaded it here.

The code is also uploaded.