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05_pytorch_going_modular.md #1067

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6 changes: 3 additions & 3 deletions 05_pytorch_going_modular.md
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Expand Up @@ -32,7 +32,7 @@ Though this is a debated topic, as companies like [Netflix have shown how they u

For example, if you have an app running online that other people can access and use, the code running that app is considered **production code**.

And libraries like fast.ai's [`nb-dev`](https://github.com/fastai/nbdev) (short for notebook development) enable you to write whole Python libraries (including documentation) with Jupyter Notebooks.
And libraries like fast.ai's [`nb-dev`](https://github.com/fastai/nbdev) (short for notebook development) enables you to write whole Python libraries (including documentation) with Jupyter Notebooks.

### Pros and cons of notebooks vs Python scripts

Expand Down Expand Up @@ -384,7 +384,7 @@ class TinyVGG(nn.Module):
self.classifier = nn.Sequential(
nn.Flatten(),
# Where did this in_features shape come from?
# It's because each layer of our network compresses and changes the shape of our inputs data.
# It's because each layer of our network compresses and changes the shape of our input data.
nn.Linear(in_features=hidden_units*13*13,
out_features=output_shape)
)
Expand Down Expand Up @@ -483,7 +483,7 @@ def train_step(model: torch.nn.Module,
# 5. Optimizer step
optimizer.step()

# Calculate and accumulate accuracy metric across all batches
# Calculate and accumulate accuracy metrics across all batches
y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)
train_acc += (y_pred_class == y).sum().item()/len(y_pred)

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