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7057729 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | """FeedForwardNetwork definition required for deserializing model.keras.
This must be imported before tf.keras.models.load_model() is called so
that keras can resolve the registered custom class.
"""
import keras
import tensorflow as tf
@keras.saving.register_keras_serializable()
class FeedForwardNetwork(tf.keras.Model):
"""Fully-connected feedforward network for 6-class HAR classification.
Architecture: Dense(512) → BN → ReLU → Dropout
Dense(256) → BN → ReLU → Dropout
Dense(128) → BN → ReLU → Dropout
Dense(6, softmax)
"""
def __init__(
self,
num_features,
num_classes,
hidden_units=(512, 256, 128),
dropout_rate=0.3,
**kwargs,
):
super().__init__(**kwargs)
self._num_features = num_features
self._num_classes = num_classes
self._hidden_units = tuple(hidden_units)
self._dropout_rate = dropout_rate
self.hidden_blocks = []
for units in hidden_units:
self.hidden_blocks.append([
tf.keras.layers.Dense(units, use_bias=False),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Dropout(dropout_rate),
])
self.output_layer = tf.keras.layers.Dense(num_classes, activation="softmax")
def call(self, inputs, training=False):
x = inputs
for block in self.hidden_blocks:
for layer in block:
if isinstance(layer, (tf.keras.layers.BatchNormalization,
tf.keras.layers.Dropout)):
x = layer(x, training=training)
else:
x = layer(x)
return self.output_layer(x)
def get_config(self):
config = super().get_config()
config.update({
"num_features": self._num_features,
"num_classes": self._num_classes,
"hidden_units": self._hidden_units,
"dropout_rate": self._dropout_rate,
})
return config
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