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HenryAI
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KerasCodeExamples.txt

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for w, b in zip(self.block_weights, self.biases):
activations = tnp.matmul(activations, w) + b
# ReLu activation function
activations = tnp.maximum(activations, 0.0)
return tnp.matmul(activations, self.linear_layer)
Just like with any other Keras model we can utilize any supported optimizer, loss, metrics or callbacks that we want.
Let's see how the model performs!
model = TNPForwardFeedRegressionNetwork(blocks=[3, 3])
model.compile(
optimizer=\"adam\",
loss=\"mean_squared_error\",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
evaluate_model(model)
Mean absolute percent error before training: 422.45343017578125
Mean absolute percent error after training: 97.24715423583984
Great! Our model seems to be effectively learning to solve the problem at hand.
We can also write our own custom loss function using TNP.
def tnp_mse(y_true, y_pred):
return tnp.mean(tnp.square(y_true - y_pred), axis=0)
keras.backend.clear_session()
model = TNPForwardFeedRegressionNetwork(blocks=[3, 3])
model.compile(
optimizer=\"adam\",
loss=tnp_mse,
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
evaluate_model(model)
Mean absolute percent error before training: 79.84039306640625
Mean absolute percent error after training: 28.658035278320312
Implementing a Keras Layer Based Model with TNP
If desired, TNP can also be used in layer oriented Keras code structure. Let's implement the same model, but using a layered approach!
def tnp_relu(x):
return tnp.maximum(x, 0)
class TNPDense(keras.layers.Layer):
def __init__(self, units, activation=None):
super().__init__()
self.units = units
self.activation = activation
def build(self, input_shape):
self.w = self.add_weight(
name=\"weights\",
shape=(input_shape[1], self.units),
initializer=\"random_normal\",
trainable=True,
)
self.bias = self.add_weight(
name=\"bias\",
shape=(self.units,),
initializer=\"random_normal\",
trainable=True,
)
def call(self, inputs):
outputs = tnp.matmul(inputs, self.w) + self.bias
if self.activation:
return self.activation(outputs)
return outputs
def create_layered_tnp_model():
return keras.Sequential(
[
TNPDense(3, activation=tnp_relu),
TNPDense(3, activation=tnp_relu),
TNPDense(1),
]
)
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)
Model: \"sequential\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
tnp_dense (TNPDense) (None, 3) 42
_________________________________________________________________
tnp_dense_1 (TNPDense) (None, 3) 12
_________________________________________________________________
tnp_dense_2 (TNPDense) (None, 1) 4