Transparent Keras
Transparent Keras aims to provide a very simple way to look under the hood during training of Keras models by defining an extra set of outputs that will be returned by train_on_batch or test_on_batch.
The API is extremely simple all that is provided is a TransparentModel that accepts an extra constructor keyword argument observed_tensors. The created model should behave exactly like a Keras model except for the functions (train|test)_on_batch, which return the extra tensors as after their normal return values.
Example
from keras.layers import Activation, Dense, Dropout, Input
import numpy as np
from transparent_keras import TransparentModel
x0 = Input(shape=(10,))
x = Dense(10, activation="relu")(x0)
x = Dropout(0.5)(x)
y_extra = x = Dense(10)(x)
x = Activation("relu")(x)
x = Dropout(0.5)(x)
y = Dense(1)(x)
m = TransparentModel(inputs=[x0], outputs=[y], observed_tensors=[y_extra])
m.compile(optimizer="sgd", loss="mse")
x_random = np.random.rand(128, 10)
y_random = np.random.rand(128, 1)
loss, y_extra = m.train_on_batch(x_random, y_random)1 maintainer
Metadata
1 maintainer
Release files for transparent-keras 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| transparent-keras-0.3.1.tar.gz | 4.2 kB | Without provenance | Details for transparent-keras-0.3.1.tar.gz |
Release files / transparent-keras-0.3.1.tar.gz
| Download URL | transparent-keras-0.3.1.tar.gz |
|---|---|
| Size | 4.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c5f866c8f74b9ef4b634e5f44ddbb64963536fcc4bd7751a6651a5d90faf83d7
|
|
BLAKE2b-256 checksum How to use checksums |
cbe20a55ab3e97587c039e3996009e75c4f603b22fe380888c4379b9013a4391
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for transparent-keras 0.3.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
