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e2b7bc1 | 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 | from __future__ import annotations
import os
import tensorflow as tf
from tensorflow.keras.layers import (Activation, Bidirectional, Conv3D, Dense,
Dropout, LSTM, MaxPool3D, Reshape)
from tensorflow.keras.models import Sequential
# Disable all GPUS
tf.config.set_visible_devices([], 'GPU')
def load_model() -> Sequential:
model = Sequential()
model.add(Conv3D(128, 3, input_shape=(75, 46, 140, 1), padding='same'))
model.add(Activation('relu'))
model.add(MaxPool3D((1, 2, 2)))
model.add(Conv3D(256, 3, padding='same'))
model.add(Activation('relu'))
model.add(MaxPool3D((1, 2, 2)))
model.add(Conv3D(75, 3, padding='same'))
model.add(Activation('relu'))
model.add(MaxPool3D((1, 2, 2)))
# Reshape instead of TimeDistributed(Flatten) — matches your trained weights
model.add(Reshape((75, 5 * 17 * 75)))
model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
model.add(Dropout(.5))
model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
model.add(Dropout(.5))
model.add(Dense(41, kernel_initializer='he_normal', activation='softmax'))
base_dir = os.path.dirname(os.path.abspath(__file__))
weights_path = os.path.abspath(
os.path.join(base_dir, '..', 'models', 'checkpoint.weights.h5')
)
if not os.path.exists(weights_path):
raise FileNotFoundError(f"Model weights not found at: {weights_path}")
model.load_weights(weights_path)
return model
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