In the previous article, we defined the forward pass for our neural network.

Now, we will provide inputs to the network so that we can test whether it works correctly.

Creating Input Values

To create a sequence of input values, we can define them like this:

input_doses = torch.linspace(start=0, end=1, steps=11)

Here, we use the PyTorch function linspace() to create a tensor containing 11 evenly spaced values between 0 and 1, including both endpoints.

The resulting tensor is stored in a variable called input_doses.

We can print input_doses to see what it looks like:

tensor([0.0000, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000,
        0.9000, 1.0000])

Creating the Neural Network

Now, we need to create an instance of our neural network.

model = MyBasicNN()

Passing Inputs Through the Neural Network

Next, we pass the input values through the model.

output_values = model(input_doses)

By default, PyTorch automatically calls the forward() method that we defined earlier.

The outputs from the neural network are stored in the variable output_values.

Plotting the Results

Now, let us visualize the outputs using a graph.

For this, we will use Seaborn and Matplotlib.

import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="whitegrid")

sns.lineplot(
    x=input_doses,
    y=output_values,
    color="green",
    linewidth=2.5
)

Next, we set labels for the graph:

plt.ylabel("Effectiveness")
plt.xlabel("Dose")

This gives us the following result:

You can try out the code yourself by checking out this colab notebook

In the next article, we will explore a scenario where the final bias is unknown.

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