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Fix nnx guide error #2183

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Merged
merged 3 commits into from
Sep 29, 2025
Merged

Fix nnx guide error #2183

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2d9cb36
Update keras_nnx_guide.py
divyashreepathihalli Sep 29, 2025
5ec12d4
Update keras_nnx_guide.ipynb
divyashreepathihalli Sep 29, 2025
fb8ea90
Update keras_nnx_guide.md
divyashreepathihalli Sep 29, 2025
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5 changes: 3 additions & 2 deletions guides/ipynb/keras_nnx_guide.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -342,7 +342,8 @@
" y_pred = model_(x)\n",
" return jnp.mean((y - y_pred) ** 2)\n",
"\n",
" grads = nnx.grad(loss_fn, wrt=trainable_var)(model)\n",
" diff_state = nnx.DiffState(0, trainable_var)\n",
" grads = nnx.grad(loss_fn, argnums=diff_state)(model)\n",
Comment on lines +345 to +346
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@gemini-code-assist gemini-code-assist bot Sep 29, 2025

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medium

For conciseness, you can inline the creation of nnx.DiffState directly into the nnx.grad call. This removes the need for a temporary variable and makes the code a single, expressive line.

Suggested change
" diff_state = nnx.DiffState(0, trainable_var)\n",
" grads = nnx.grad(loss_fn, argnums=diff_state)(model)\n",
" grads = nnx.grad(loss_fn, argnums=nnx.DiffState(0, trainable_var))(model)\n",

" optimizer.update(model, grads)\n",
"\n",
"\n",
Expand Down Expand Up @@ -506,4 +507,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
3 changes: 2 additions & 1 deletion guides/keras_nnx_guide.py
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Expand Up @@ -208,7 +208,8 @@ def loss_fn(model_):
y_pred = model_(x)
return jnp.mean((y - y_pred) ** 2)

grads = nnx.grad(loss_fn, wrt=trainable_var)(model)
diff_state = nnx.DiffState(0, trainable_var)
grads = nnx.grad(loss_fn, argnums=diff_state)(model)
Comment on lines +211 to +212
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@gemini-code-assist gemini-code-assist bot Sep 29, 2025

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medium

For conciseness, you can inline the creation of nnx.DiffState directly into the nnx.grad call. This removes the need for a temporary variable and makes the code a single, expressive line.

Suggested change
diff_state = nnx.DiffState(0, trainable_var)
grads = nnx.grad(loss_fn, argnums=diff_state)(model)
grads = nnx.grad(loss_fn, argnums=nnx.DiffState(0, trainable_var))(model)

optimizer.update(model, grads)


Expand Down
3 changes: 2 additions & 1 deletion guides/md/keras_nnx_guide.md
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Expand Up @@ -223,7 +223,8 @@ def train_step(model, optimizer, batch):
y_pred = model_(x)
return jnp.mean((y - y_pred) ** 2)

grads = nnx.grad(loss_fn, wrt=trainable_var)(model)
diff_state = nnx.DiffState(0, trainable_var)
grads = nnx.grad(loss_fn, argnums=diff_state)(model)
Comment on lines +226 to +227
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@gemini-code-assist gemini-code-assist bot Sep 29, 2025

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medium

For conciseness, you can inline the creation of nnx.DiffState directly into the nnx.grad call. This removes the need for a temporary variable and makes the code a single, expressive line. This can help keep code examples in the guide clean and focused.

Suggested change
diff_state = nnx.DiffState(0, trainable_var)
grads = nnx.grad(loss_fn, argnums=diff_state)(model)
grads = nnx.grad(loss_fn, argnums=nnx.DiffState(0, trainable_var))(model)

optimizer.update(model, grads)


Expand Down