keras_mixed_sequence.egg-info/PKG-INFO
Metadata-Version: 2.1
Name: keras-mixed-sequence
Version: 1.0.29
Summary: Lazily loading mixed sequences using Keras Sequence, focused on multi-task models.
Home-page: https://github.com/LucaCappelletti94/keras_mixed_sequence
Author: Luca Cappelletti
Author-email: cappelletti.luca94@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >3.5.2
Provides-Extra: test
License-File: LICENSE
keras_mixed_sequence
=========================================================================================
|pip| |downloads|
Lazily loading mixed sequences using Keras Sequence,
focused on multi-task models.
How do I install this package?
----------------------------------------------
As usual, just download it using pip:
.. code:: shell
pip install keras_mixed_sequence
Usage examples
----------------------------------------------
Example for traditional single-task models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First of all let's create a simple single-task model:
.. code:: python
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
model = Sequential([
Dense(1, activation="relu")
])
model.compile(
optimizer="nadam",
loss="relu"
)
Then we proceed to load or otherwise create the training data.
Here there will be listed, in the future, some custom
Sequence objects that have been created for the purpose
of being used alongside this library.
.. code:: python
X = either_a_numpy_array_or_sequence_for_input
y = either_a_numpy_array_or_sequence_for_output
Now we combine the training data using the MixedSequence
object.
.. code:: python
from keras_mixed_sequence import MixedSequence
sequence = MixedSequence(
X, y,
batch_size=batch_size
)
Finally, we can train the model:
.. code:: python
from multiprocessing import cpu_count
model.fit_generator(
sequence,
steps_per_epoch=sequence.steps_per_epoch,
epochs=2,
verbose=0,
use_multiprocessing=True,
workers=cpu_count(),
shuffle=True
)
Example for multi-task models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First of all let's create a simple multi-taks model:
.. code:: python
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Input
inputs = Input(shape=(10,))
output1 = Dense(
units=10,
activation="relu",
name="output1"
)(inputs)
output2 = Dense(
units=10,
activation="relu",
name="output2"
)(inputs)
model = Model(
inputs=inputs,
outputs=[output1, output2],
name="my_model"
)
model.compile(
optimizer="nadam",
loss="MSE"
)
Then we proceed to load or otherwise create the training data.
Here there will be listed, in the future, some custom
Sequence objects that have been created for the purpose
of being used alongside this library.
.. code:: python
X = either_a_numpy_array_or_sequence_for_input
y1 = either_a_numpy_array_or_sequence_for_output1
y2 = either_a_numpy_array_or_sequence_for_output2
Now we combine the training data using the MixedSequence
object.
.. code:: python
from keras_mixed_sequence import MixedSequence
sequence = MixedSequence(
x=X,
y={
"output1": y1,
"output2": y2
},
batch_size=batch_size
)
Finally, we can train the model:
.. code:: python
from multiprocessing import cpu_count
model.fit_generator(
sequence,
steps_per_epoch=sequence.steps_per_epoch,
epochs=2,
verbose=0,
use_multiprocessing=True,
workers=cpu_count(),
shuffle=True
)
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:alt: Pypi project
.. |downloads| image:: https://pepy.tech/badge/keras-mixed-sequence
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