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Update getting_started.ipynb and getting_started.md #2563

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Oct 31, 2022
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Update getting_started.ipynb and getting_started.md #2563

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8e7e190
Update getting_started.ipynb
chiamp Oct 28, 2022
ed53891
Update getting_started.md
chiamp Oct 28, 2022
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8 changes: 4 additions & 4 deletions docs/getting_started.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -276,8 +276,8 @@
" [Module.apply](https://flax.readthedocs.io/en/latest/flax.linen.html#flax.linen.Module.apply)\n",
" method.\n",
"- Computes the `cross_entropy_loss` loss function.\n",
"- Evaluates the loss function and its gradient using\n",
" [jax.value_and_grad](https://jax.readthedocs.io/en/latest/jax.html#jax.value_and_grad).\n",
"- Evaluates the gradient of the loss function using\n",
" [jax.grad](https://jax.readthedocs.io/en/latest/jax.html#jax.grad).\n",
"- Applies a\n",
" [pytree](https://jax.readthedocs.io/en/latest/pytrees.html#pytrees-and-jax-functions)\n",
" of gradients to the optimizer to update the model's parameters.\n",
Expand All @@ -304,8 +304,8 @@
" logits = CNN().apply({'params': params}, batch['image'])\n",
" loss = cross_entropy_loss(logits=logits, labels=batch['label'])\n",
" return loss, logits\n",
" grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n",
" (_, logits), grads = grad_fn(state.params)\n",
" grad_fn = jax.grad(loss_fn, has_aux=True)\n",
" grads, logits = grad_fn(state.params)\n",
" state = state.apply_gradients(grads=grads)\n",
" metrics = compute_metrics(logits=logits, labels=batch['label'])\n",
" return state, metrics"
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8 changes: 4 additions & 4 deletions docs/getting_started.md
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Original file line number Diff line number Diff line change
Expand Up @@ -198,8 +198,8 @@ A function that:
[Module.apply](https://flax.readthedocs.io/en/latest/flax.linen.html#flax.linen.Module.apply)
method.
- Computes the `cross_entropy_loss` loss function.
- Evaluates the loss function and its gradient using
[jax.value_and_grad](https://jax.readthedocs.io/en/latest/jax.html#jax.value_and_grad).
- Evaluates the gradient of the loss function using
[jax.grad](https://jax.readthedocs.io/en/latest/jax.html#jax.grad).
- Applies a
[pytree](https://jax.readthedocs.io/en/latest/pytrees.html#pytrees-and-jax-functions)
of gradients to the optimizer to update the model's parameters.
Expand All @@ -220,8 +220,8 @@ def train_step(state, batch):
logits = CNN().apply({'params': params}, batch['image'])
loss = cross_entropy_loss(logits=logits, labels=batch['label'])
return loss, logits
grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
(_, logits), grads = grad_fn(state.params)
grad_fn = jax.grad(loss_fn, has_aux=True)
grads, logits = grad_fn(state.params)
state = state.apply_gradients(grads=grads)
metrics = compute_metrics(logits=logits, labels=batch['label'])
return state, metrics
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