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Fixes monospace formatting for layer subclassing docs #2060

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Fixes monospace formatting for layer subclassing docs #2060

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22 changes: 8 additions & 14 deletions guides/ipynb/making_new_layers_and_models_via_subclassing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -97,8 +97,7 @@
" self.b = self.add_weight(shape=(units,), initializer=\"zeros\", trainable=True)\n",
"\n",
" def call(self, inputs):\n",
" return ops.matmul(inputs, self.w) + self.b\n",
""
" return ops.matmul(inputs, self.w) + self.b\n"
]
},
{
Expand Down Expand Up @@ -246,8 +245,7 @@
" self.b = self.add_weight(shape=(units,), initializer=\"zeros\", trainable=True)\n",
"\n",
" def call(self, inputs):\n",
" return ops.matmul(inputs, self.w) + self.b\n",
""
" return ops.matmul(inputs, self.w) + self.b\n"
]
},
{
Expand Down Expand Up @@ -289,8 +287,7 @@
" )\n",
"\n",
" def call(self, inputs):\n",
" return ops.matmul(inputs, self.w) + self.b\n",
""
" return ops.matmul(inputs, self.w) + self.b\n"
]
},
{
Expand Down Expand Up @@ -390,7 +387,7 @@
"The `keras.ops` namespace gives you access to:\n",
"\n",
"- The NumPy API, e.g. `ops.matmul`, `ops.sum`, `ops.reshape`, `ops.stack`, etc.\n",
"- Neural networks-specific APIs such as `ops.softmax`, `ops`.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.\n",
"- Neural networks-specific APIs such as `ops.softmax`, `ops.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.\n",
"\n",
"You can also use backend-native APIs in your layers (such as `tf.nn` functions),\n",
"but if you do this, then your layer will only be usable with the backend in question.\n",
Expand Down Expand Up @@ -465,8 +462,7 @@
"\n",
" def call(self, inputs):\n",
" self.add_loss(self.rate * ops.mean(inputs))\n",
" return inputs\n",
""
" return inputs\n"
]
},
{
Expand Down Expand Up @@ -742,8 +738,7 @@
" return keras.random.dropout(\n",
" inputs, rate=self.rate, seed=self.seed_generator\n",
" )\n",
" return inputs\n",
""
" return inputs\n"
]
},
{
Expand Down Expand Up @@ -947,8 +942,7 @@
" z_log_var - ops.square(z_mean) - ops.exp(z_log_var) + 1\n",
" )\n",
" self.add_loss(kl_loss)\n",
" return reconstructed\n",
""
" return reconstructed\n"
]
},
{
Expand Down Expand Up @@ -1010,4 +1004,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
2 changes: 1 addition & 1 deletion guides/making_new_layers_and_models_via_subclassing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,7 +227,7 @@ def call(self, inputs):
The `keras.ops` namespace gives you access to:

- The NumPy API, e.g. `ops.matmul`, `ops.sum`, `ops.reshape`, `ops.stack`, etc.
- Neural networks-specific APIs such as `ops.softmax`, `ops`.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.
- Neural networks-specific APIs such as `ops.softmax`, `ops.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.

You can also use backend-native APIs in your layers (such as `tf.nn` functions),
but if you do this, then your layer will only be usable with the backend in question.
Expand Down
2 changes: 1 addition & 1 deletion guides/md/making_new_layers_and_models_via_subclassing.md
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Original file line number Diff line number Diff line change
Expand Up @@ -272,7 +272,7 @@ All layers you've seen so far in this guide work with all Keras backends.
The `keras.ops` namespace gives you access to:

- The NumPy API, e.g. `ops.matmul`, `ops.sum`, `ops.reshape`, `ops.stack`, etc.
- Neural networks-specific APIs such as `ops.softmax`, `ops`.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.
- Neural networks-specific APIs such as `ops.softmax`, `ops.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.

You can also use backend-native APIs in your layers (such as `tf.nn` functions),
but if you do this, then your layer will only be usable with the backend in question.
Expand Down