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b87f5fe 7057729 b87f5fe 7057729 b87f5fe | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | """Model classes required for deserializing .keras files.
Must be imported before tf.keras.models.load_model() so that keras
can resolve the registered custom classes.
"""
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
@keras.saving.register_keras_serializable()
class Conv1DNetwork(tf.keras.Model):
"""1D-CNN for classification on pre-computed feature vectors.
Architecture: Reshape(561, 1)
→ Conv1D(64, k=5) → BN → ReLU → MaxPool(2) → Dropout(0.3)
→ Conv1D(128, k=5) → BN → ReLU → MaxPool(2) → Dropout(0.3)
→ Conv1D(256, k=3) → BN → ReLU → GlobalAvgPool1D
→ Dense(128) → BN → ReLU → Dropout(0.5)
→ Dense(6, softmax)
"""
def __init__(
self,
num_features,
num_classes,
dropout_rate=0.3,
**kwargs,
):
super().__init__(**kwargs)
self._num_features = num_features
self._num_classes = num_classes
self._dropout_rate = dropout_rate
self.reshape = tf.keras.layers.Reshape((num_features, 1))
self.conv1 = tf.keras.layers.Conv1D(64, kernel_size=5, padding="same", use_bias=False)
self.bn1 = tf.keras.layers.BatchNormalization()
self.relu1 = tf.keras.layers.ReLU()
self.pool1 = tf.keras.layers.MaxPooling1D(pool_size=2)
self.drop1 = tf.keras.layers.Dropout(dropout_rate)
self.conv2 = tf.keras.layers.Conv1D(128, kernel_size=5, padding="same", use_bias=False)
self.bn2 = tf.keras.layers.BatchNormalization()
self.relu2 = tf.keras.layers.ReLU()
self.pool2 = tf.keras.layers.MaxPooling1D(pool_size=2)
self.drop2 = tf.keras.layers.Dropout(dropout_rate)
self.conv3 = tf.keras.layers.Conv1D(256, kernel_size=3, padding="same", use_bias=False)
self.bn3 = tf.keras.layers.BatchNormalization()
self.relu3 = tf.keras.layers.ReLU()
self.gap = tf.keras.layers.GlobalAveragePooling1D()
self.dense1 = tf.keras.layers.Dense(128, use_bias=False)
self.bn_fc = tf.keras.layers.BatchNormalization()
self.relu_fc = tf.keras.layers.ReLU()
self.drop_fc = tf.keras.layers.Dropout(0.5)
self.output_layer = tf.keras.layers.Dense(num_classes, activation="softmax")
def call(self, inputs, training=False):
x = self.reshape(inputs)
x = self.conv1(x)
x = self.bn1(x, training=training)
x = self.relu1(x)
x = self.pool1(x)
x = self.drop1(x, training=training)
x = self.conv2(x)
x = self.bn2(x, training=training)
x = self.relu2(x)
x = self.pool2(x)
x = self.drop2(x, training=training)
x = self.conv3(x)
x = self.bn3(x, training=training)
x = self.relu3(x)
x = self.gap(x)
x = self.dense1(x)
x = self.bn_fc(x, training=training)
x = self.relu_fc(x)
x = self.drop_fc(x, training=training)
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,
"dropout_rate": self._dropout_rate,
})
return config
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