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import tensorflow as tf
from typing import Tuple
def _inception_module(
input_tensor,
stride=1,
activation="linear",
use_bottleneck=True,
kernel_size=40,
bottleneck_size=32,
nb_filters=32,
):
if use_bottleneck and int(input_tensor.shape[-1]) > 1:
input_inception = tf.keras.layers.Conv1D(
filters=bottleneck_size,
kernel_size=1,
padding="same",
activation=activation,
use_bias=False,
)(input_tensor)
else:
input_inception = input_tensor
# kernel_size_s = [3, 5, 8, 11, 17]
kernel_size_s = [kernel_size // (2**i) for i in range(3)]
conv_list = []
for i in range(len(kernel_size_s)):
conv_list.append(
tf.keras.layers.Conv1D(
filters=nb_filters,
kernel_size=kernel_size_s[i],
strides=stride,
padding="same",
activation=activation,
use_bias=False,
)(input_inception)
)
max_pool_1 = tf.keras.layers.MaxPool1D(pool_size=3, strides=stride, padding="same")(
input_tensor
)
conv_6 = tf.keras.layers.Conv1D(
filters=nb_filters,
kernel_size=1,
padding="same",
activation=activation,
use_bias=False,
)(max_pool_1)
conv_list.append(conv_6)
x = tf.keras.layers.Concatenate(axis=2)(conv_list)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation(activation="relu")(x)
return x
def _shortcut_layer(input_tensor, out_tensor):
shortcut_y = tf.keras.layers.Conv1D(
filters=int(out_tensor.shape[-1]), kernel_size=1, padding="same", use_bias=False
)(input_tensor)
shortcut_y = tf.keras.layers.BatchNormalization()(shortcut_y)
x = tf.keras.layers.Add()([shortcut_y, out_tensor])
x = tf.keras.layers.Activation("relu")(x)
return x
def build_model(
input_shape: Tuple[int, int],
nb_classes: int,
depth: int = 6,
use_residual: bool = True,
)-> tf.keras.models.Model:
"""
Model proposed by HI Fawas et al 2019 "Finding AlexNet for Time Series Classification - InceptionTime"
"""
input_layer = tf.keras.layers.Input(input_shape)
x = input_layer
input_res = input_layer
for d in range(depth):
x = _inception_module(x)
if use_residual and d % 3 == 2:
x = _shortcut_layer(input_res, x)
input_res = x
gap_layer = tf.keras.layers.GlobalAveragePooling1D()(x)
output_layer = tf.keras.layers.Dense(units=nb_classes, activation="linear")(
gap_layer
)
model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
model.compile(
loss=tf.keras.losses.MeanAbsoluteError(),
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
metrics=[tf.keras.metrics.MeanSquaredError()],
)
return model |