https://github.com/franneck94/tensorcross
Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
https://github.com/franneck94/tensorcross
cross-validation dataset grid-search gridsearch gridsearchcv python random-search tensorflow tensorflow-datasets tensorflow2 validation
Last synced: 6 months ago
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Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
- Host: GitHub
- URL: https://github.com/franneck94/tensorcross
- Owner: franneck94
- License: mit
- Created: 2020-11-16T18:23:35.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-05-20T18:54:02.000Z (over 1 year ago)
- Last Synced: 2025-03-27T03:22:31.739Z (7 months ago)
- Topics: cross-validation, dataset, grid-search, gridsearch, gridsearchcv, python, random-search, tensorflow, tensorflow-datasets, tensorflow2, validation
- Language: Python
- Homepage:
- Size: 734 KB
- Stars: 15
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# TensorCross
[](https://github.com/franneck94/TensorCross/blob/main/LICENSE)
[](https://github.com/franneck94/TensorCross/actions?query=workflow%3A%22Test+and+Coverage%22)
[](https://codecov.io/gh/franneck94/TensorCross)
[](https://franneck94.github.io/TensorCross/index.html)
```bash
pip install tensorcross
```
Grid Search and Random Search with optionally CrossValidation for tf.data.Datasets in TensorFlow (Keras) 2.8+ and Python 3.9+.
## Motivation
There was the tf.keras.wrapper.KerasClassifier/KerasRegressor class,
which can be used to transform your tf.keras model into a sklearn estimator.
However, this approach is only applicable if your dataset is a numpy.ndarray
for your x and y data and it was also removed from newer versions.
If you want to use the new tf.data.Dataset class, you cannot use the sklearn
wrappers.
This python package aims to help with this use-case.
## API
- [GridSearch](#GridSearch-Example)
- [GridSearchCV](#GridSearchCV-Example)
- For more examples see: [here](examples/)
### Dataset and TensorFlow Model for the Examples
```python
import tensorflow as tf
dataset = tf.data.Dataset.from_tensor_slices(
(np.array([1, 2, 3]).reshape(-1, 1), # x
np.array([-1, -2, -3]).reshape(-1, 1)) # y
)
def build_model(
optimizer: tf.keras.optimizers.Optimizer,
learning_rate: float
) -> tf.keras.models.Model:
x_input = tf.keras.layers.Input(shape=2)
y_pred = tf.keras.layers.Dense(units=1)(x_input)
model = tf.keras.models.Model(inputs=[x_input], outputs=[y_pred])
opt = optimizer(learning_rate=learning_rate)
model.compile(
loss="mse", optimizer=opt, metrics=["mse"]
)
return model
```
The dataset must be a tf.data.Dataset object and you have to define a
function/callable that returns a compiled tf.keras.models.Model object.
This object will then be trained in e.g. the GridSearch.
### GridSearch Example
Assuming you have a tf.data.Dataset object and a build_model function,
defined as above. You can run a GridSearch as below:
``` python
from tensorcross.model_selection import GridSearch
train_dataset, val_dataset = dataset_split(
dataset=dataset,
split_fraction=(1 / 3)
)
param_grid = {
"optimizer": [
tf.keras.optimizers.Adam,
tf.keras.optimizers.RMSprop
],
"learning_rate": [0.001, 0.0001]
}
grid_search = GridSearch(
model_fn=build_model,
param_grid=param_grid,
verbose=1,
)
grid_search.fit(
train_dataset=train_dataset,
val_dataset=val_dataset,
epochs=1,
verbose=1
)
grid_search.summary()
```
This would result in the following console output:
```console
--------------------------------------------------
Best score: 1.1800532341003418 using params: {
'learning_rate': 0.001, 'optimizer': 'RMSprop'
}
--------------------------------------------------
Idx: 0 - Score: 0.2754371166229248 with param: {
'learning_rate': 0.001, 'optimizer': 'Adam'
}
Idx: 1 - Score: 1.1800532341003418 with param: {
'learning_rate': 0.001, 'optimizer': 'RMSprop'
}
Idx: 2 - Score: 0.055416107177734375 with param: {
learning_rate': 0.0001, 'optimizer': 'Adam'
}
Idx: 3 - Score: 0.12417340278625488 with param: {
'learning_rate': 0.0001, 'optimizer': 'RMSprop'
}
--------------------------------------------------
```
### GridSearchCV Example
Assuming you have a tf.data.Dataset object and a build_model function,
defined as above. You can run a GridSearchCV as below:
```python
from tensorcross.model_selection import GridSearchCV
param_grid = {
"optimizer": [
tf.keras.optimizers.Adam,
tf.keras.optimizers.RMSprop
],
"learning_rate": [0.001, 0.0001]
}
grid_search_cv = GridSearchCV(
model_fn=build_model,
param_grid=param_grid,
n_folds=2,
verbose=1,
)
grid_search_cv.fit(
dataset=dataset,
epochs=1,
verbose=1
)
grid_search_cv.summary()
```
This would result in the following console output:
```console
--------------------------------------------------
Best score: 1.1800532341003418 using params: {
'learning_rate': 0.001, 'optimizer': 'RMSprop'
}
--------------------------------------------------
Idx: 0 - Score: 0.2754371166229248 with param: {
'learning_rate': 0.001, 'optimizer': 'Adam'
}
Idx: 1 - Score: 1.1800532341003418 with param: {
'learning_rate': 0.001, 'optimizer': 'RMSprop'
}
Idx: 2 - Score: 0.055416107177734375 with param: {
learning_rate': 0.0001, 'optimizer': 'Adam'
}
Idx: 3 - Score: 0.12417340278625488 with param: {
'learning_rate': 0.0001, 'optimizer': 'RMSprop'
}
--------------------------------------------------
```
### Callabcks
Adding Callbacks like TensorBoard is easy.
Just pass the callbacks list to the .fit mehtod like you would do with a normal keras Model.