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Enhance docstrings + easy to understand examples (nn.py) #758

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Aug 21, 2023
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Enhance docstrings + easy to understand examples (nn.py) #758

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Enhance docstrings + easy to understand examples
Faisal-Alsrheed Aug 21, 2023
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Enhance docstrings + easy to understand examplesv2
Faisal-Alsrheed Aug 21, 2023
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67 changes: 33 additions & 34 deletions keras_core/ops/nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,12 +32,14 @@ def relu(x):
Returns:
A tensor with the same shape as `x`.

Example:
Examples:

>>> x = np.array([-1., 0., 1.])
>>> x_relu = keras_core.ops.relu(x)
>>> print(x_relu)
array([0., 0., 1.], shape=(3,), dtype=float64)
>>> x1 = keras_core.ops.convert_to_tensor([-1.0, 0.0, 1.0])
>>> keras_core.ops.relu(x1)
array([0.0, 0.0, 1.0], dtype=float32)
>>> x2 = keras_core.ops.convert_to_tensor([-100.0, 0.0, 100.0])
>>> keras_core.ops.relu(x2)
array([0.0, 0.0, 100.0], dtype=float32)

"""
if any_symbolic_tensors((x,)):
Expand Down Expand Up @@ -67,10 +69,9 @@ def relu6(x):

Example:

>>> x = np.array([-1., 0., 1., 6., 7.])
>>> x_relu6 = keras_core.ops.relu6(x)
>>> print(x_relu6)
array([0., 0., 1., 6., 6.], shape=(5,), dtype=float64)
>>> x = keras_core.ops.convert_to_tensor([-66.0, -6.0, 0.0, 6.0, 66.0])
>>> keras_core.ops.relu6(x)
array([0.0, 0.0, 0.0, 6.0, 6.0], dtype=float32)

"""
if any_symbolic_tensors((x,)):
Expand Down Expand Up @@ -100,10 +101,9 @@ def sigmoid(x):

Example:

>>> x = np.array([-1., 0., 1.])
>>> x_sigmoid = keras_core.ops.sigmoid(x)
>>> print(x_sigmoid)
array([0.26894143, 0.5, 0.7310586 ], dtype=float64)
>>> x = keras_core.ops.convert_to_tensor([-6.0, 1.0, 0.0, 1.0, 6.0])
>>> keras_core.ops.sigmoid(x)
array([0.00247262, 0.7310586, 0.5, 0.7310586, 0.9975274], dtype=float32)

"""
if any_symbolic_tensors((x,)):
Expand Down Expand Up @@ -134,10 +134,9 @@ def softplus(x):

Example:

>>> x = np.array([-1., 0., 1.])
>>> x_softplus = keras_core.ops.softplus(x)
>>> print(x_softplus)
array([0.31326166, 0.6931472 , 1.3132616 ], shape=(3,), dtype=float64)
>>> x = keras_core.ops.convert_to_tensor([-0.555, 0.0, 0.555])
>>> keras_core.ops.softplus(x)
array([0.45366603, 0.6931472, 1.008666], dtype=float32)

"""
if any_symbolic_tensors((x,)):
Expand Down Expand Up @@ -167,10 +166,9 @@ def softsign(x):

Example:

>>> x = np.array([-1., 0., 1.])
>>> x_softsign = keras_core.ops.softsign(x)
>>> print(x_softsign)
array([-0.5, 0., 0.5], shape=(3,), dtype=float64)
>>> x = keras_core.ops.convert_to_tensor([-0.100, -10.0, 1.0, 0.0, 100.0])
>>> keras_core.ops.softsign(x)
Array([-0.09090909, -0.90909094, 0.5, 0.0, 0.990099], dtype=float32)

"""
if any_symbolic_tensors((x,)):
Expand All @@ -195,9 +193,10 @@ def compute_output_spec(self, x):
]
)
def silu(x):
"""Sigmoid-weighted linear unit activation function.
"""Sigmoid Linear Unit (SiLU) activation function, also known as Swish.

It is defined as `f(x) = x * sigmoid(x)`.
The SiLU activation function is computed by the sigmoid function multiplied
by its input. It is defined as `f(x) = x * sigmoid(x)`.

Args:
x: Input tensor.
Expand All @@ -207,10 +206,11 @@ def silu(x):

Example:

>>> x = np.array([-1., 0., 1.])
>>> x_silu = keras_core.ops.silu(x)
>>> print(x_silu)
array([-0.26894143, 0., 0.7310586], shape=(3,), dtype=float64)
>>> x = keras_core.ops.convert_to_tensor([-6.0, 1.0, 0.0, 1.0, 6.0])
>>> keras_core.ops.sigmoid(x)
array([0.00247262, 0.7310586, 0.5, 0.7310586, 0.9975274], dtype=float32)
>>> keras_core.ops.silu(x)
array([-0.0148357, 0.7310586, 0.0, 0.7310586, 5.9851646], dtype=float32)

"""
if any_symbolic_tensors((x,)):
Expand Down Expand Up @@ -245,10 +245,9 @@ def log_sigmoid(x):

Example:

>>> x = np.array([-1., 0., 1.])
>>> x_log_sigmoid = keras_core.ops.log_sigmoid(x)
>>> print(x_log_sigmoid)
array([-1.3132616, -0.6931472, -0.3132616], shape=(3,), dtype=float64)
>>> x = keras_core.ops.convert_to_tensor([-0.541391, 0.0, 0.50, 5.0])
>>> keras_core.ops.log_sigmoid(x)
array([-1.0000418, -0.6931472, -0.474077, -0.00671535], dtype=float32)

"""
if any_symbolic_tensors((x,)):
Expand All @@ -272,7 +271,7 @@ def compute_output_spec(self, x):
["keras_core.ops.leaky_relu", "keras_core.ops.nn.leaky_relu"]
)
def leaky_relu(x, negative_slope=0.2):
"""Leaky version of a Rectified Linear Unit.
"""Leaky version of a Rectified Linear Unit activation function.

It allows a small gradient when the unit is not active, it is defined as:

Expand Down Expand Up @@ -353,7 +352,7 @@ def compute_output_spec(self, x):

@keras_core_export(["keras_core.ops.elu", "keras_core.ops.nn.elu"])
def elu(x, alpha=1.0):
"""Exponential Linear Unit.
"""Exponential Linear Unit activation function.

It is defined as:

Expand Down Expand Up @@ -389,7 +388,7 @@ def compute_output_spec(self, x):

@keras_core_export(["keras_core.ops.selu", "keras_core.ops.nn.selu"])
def selu(x):
"""Scaled Exponential Linear Unit (SELU).
"""Scaled Exponential Linear Unit (SELU) activation function.

It is defined as:

Expand Down