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KerasCodeExamples.txt

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=================================================================
Total params: 58
Trainable params: 58
Non-trainable params: 0
_________________________________________________________________
Mean absolute percent error before training: 101.17143249511719
Mean absolute percent error after training: 23.479856491088867
You can also seamlessly switch between TNP layers and native Keras layers!
def create_mixed_model():
return keras.Sequential(
[
TNPDense(3, activation=tnp_relu),
# The model will have no issue using a normal Dense layer
layers.Dense(3, activation=\"relu\"),
# ... or switching back to tnp layers!
TNPDense(1),
]
)
model = create_mixed_model()
model.compile(
optimizer=\"adam\",
loss=\"mean_squared_error\",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
model.build((None, 13,))
model.summary()
evaluate_model(model)
Model: \"sequential_1\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
tnp_dense_3 (TNPDense) (None, 3) 42
_________________________________________________________________
dense (Dense) (None, 3) 12
_________________________________________________________________
tnp_dense_4 (TNPDense) (None, 1) 4
=================================================================
Total params: 58
Trainable params: 58
Non-trainable params: 0
_________________________________________________________________
Mean absolute percent error before training: 104.59967041015625
Mean absolute percent error after training: 27.712949752807617
The Keras API offers a wide variety of layers. The ability to use them alongside NumPy code can be a huge time saver in projects.
Distribution Strategy
TensorFlow NumPy and Keras integrate with TensorFlow Distribution Strategies. This makes it simple to perform distributed training across multiple GPUs, or even an entire TPU Pod.
gpus = tf.config.list_logical_devices(\"GPU\")
if gpus:
strategy = tf.distribute.MirroredStrategy(gpus)
else:
# We can fallback to a no-op CPU strategy.
strategy = tf.distribute.get_strategy()
print(\"Running with strategy:\", str(strategy.__class__.__name__))
with strategy.scope():
model = create_layered_tnp_model()
model.compile(
optimizer=\"adam\",
loss=\"mean_squared_error\",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
model.build((None, 13,))
model.summary()
evaluate_model(model)
Running with strategy: _DefaultDistributionStrategy
Model: \"sequential_2\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
tnp_dense_5 (TNPDense) (None, 3) 42
_________________________________________________________________
tnp_dense_6 (TNPDense) (None, 3) 12
_________________________________________________________________
tnp_dense_7 (TNPDense) (None, 1) 4
=================================================================
Total params: 58
Trainable params: 58
Non-trainable params: 0
_________________________________________________________________
Mean absolute percent error before training: 100.5331039428711
Mean absolute percent error after training: 20.71842384338379
TensorBoard Integration
One of the many benefits of using the Keras API is the ability to monitor training through TensorBoard. Using the TensorFlow NumPy API alongside Keras allows you to easily leverage TensorBoard.
keras.backend.clear_session()
To load the TensorBoard from a Jupyter notebook, you can run the following magic:
%load_ext tensorboard
models = [
(TNPForwardFeedRegressionNetwork(blocks=[3, 3]), \"TNPForwardFeedRegressionNetwork\"),
(create_layered_tnp_model(), \"layered_tnp_model\"),
(create_mixed_model(), \"mixed_model\"),
]
for model, model_name in models: