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doc fix on keras models #3585

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merged 2 commits into from
Oct 16, 2023
Merged

doc fix on keras models #3585

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0239e46
doc fix on keras models
KitVB Sep 29, 2023
4057170
doc fixes
KitVB Oct 4, 2023
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27 changes: 18 additions & 9 deletions deepchem/models/keras_model.py
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Expand Up @@ -353,7 +353,8 @@ def fit(self,

Returns
-------
The average loss over the most recent checkpoint interval
float
The average loss over the most recent checkpoint interval
"""
return self.fit_generator(
self.default_generator(dataset,
Expand Down Expand Up @@ -403,7 +404,8 @@ def fit_generator(self,

Returns
-------
The average loss over the most recent checkpoint interval
float
The average loss over the most recent checkpoint interval
"""
if not isinstance(callbacks, SequenceCollection):
callbacks = [callbacks]
Expand Down Expand Up @@ -557,7 +559,8 @@ def fit_on_batch(self,

Returns
-------
the loss on the batch
float
the loss on the batch
"""
self._ensure_built()
dataset = NumpyDataset(X, y, w)
Expand Down Expand Up @@ -738,7 +741,10 @@ def predict_on_generator(
If specified, all outputs of this type will be retrieved
from the model. If output_types is specified, outputs must
be None.
Returns:

Returns
-------
OneOrMany[np.ndarray]
a NumPy array of the model produces a single output, or a list of arrays
if it produces multiple outputs
"""
Expand Down Expand Up @@ -768,8 +774,9 @@ def predict_on_batch(

Returns
-------
a NumPy array of the model produces a single output, or a list of arrays
if it produces multiple outputs
OneOrMany[np.ndarray]
a NumPy array of the model produces a single output, or a list of arrays
if it produces multiple outputs
"""
dataset = NumpyDataset(X=X, y=None)
return self.predict(dataset, transformers, outputs)
Expand Down Expand Up @@ -797,9 +804,11 @@ def predict_uncertainty_on_batch(

Returns
-------
for each output, a tuple (y_pred, y_std) where y_pred is the predicted
value of the output, and each element of y_std estimates the standard
deviation of the corresponding element of y_pred
OneOrMany[Tuple[y_pred, y_std]]
y_pred: np.ndarray
predicted value of the output
y_std: np.ndarray
standard deviation of the corresponding element of y_pred
"""
dataset = NumpyDataset(X=X, y=None)
return self.predict_uncertainty(dataset, masks)
Expand Down