def get_validation_data_batch():
validation_paths = get_validation_paths()
validation_generator = SoundExampleGenerator(
validation_paths,
batch_size=192,
num_mels=num_mels,
fixed_sound_length=fixed_sound_length,
preprocessing_fn=preprocess_mobilenet_input,
augment=False,
)
validation_data = validation_generator[0]
return validation_data
optimizer=RMSprop(lr=learning_rate),
metrics=["acc"])
# model.summary()
return model
if __name__ == "__main__":
train_paths = get_train_paths()
fixed_sound_length = 150
num_mels = 20
train_generator = SoundExampleGenerator(
train_paths, fixed_sound_length=fixed_sound_length, num_mels=num_mels)
train_sample_batch_x, train_sample_batch_y = train_generator[0]
input_vector_size = len(train_sample_batch_x[0][0])
validation_paths = get_validation_paths()
validation_generator = SoundExampleGenerator(
validation_paths,
batch_size=192,
fixed_sound_length=fixed_sound_length,
num_mels=num_mels,
augment=False,
)
validation_data = validation_generator[0]
model = get_lstm_model(input_vector_size)
os.makedirs(DATA_DIR / "models", exist_ok=True)
model_save_path = os.path.join(DATA_DIR / "models", "lstm.h5")
model_checkpoint = ModelCheckpoint(model_save_path,