x = UpSampling1D(2)(x)
out = Conv1D(6, kernel1, activation='softmax', padding='same')(x)
outputs.append(out)
outputs.append(y_out)
# x = Conv1D(6, 20, activation="relu", padding='same')(x)
autoencoder = Model(inputs, outputs)
autoencoder.compile(optimizer='adam', loss='categorical_crossentropy')
return autoencoder
def autocorr(x):
result = np.correlate(x, x, mode='full')
return result[len(result) // 2:]
if __name__ == "__main__":
import numpy as np
MODEL_PATH = "triple_binary_detection"
model = build_triple_detection_network((4096, 1))
plot_model(model)
model = load_model("models\\" + MODEL_PATH + "_ma.h5")
model.summary()
X = load_good_holter()
model = train_eval(model, X, only_eval=True, save_path=MODEL_PATH, generator=artefact_for_detection_3_in_2_out,
size=4096, epochs=50)
if name == "dual_detection":
generator = artefact_for_detection_dual
elif name == "triple_detection":
generator = artefact_for_detection_3_in_2_out
else:
generator = artefact_for_detection
res, _ = load_split()
path = "C:\\Users\\donte_000\\Downloads\\Telegram Desktop\\noise_lbl.csv"
idxs = pd.read_csv(path, encoding='utf-8', sep=";")['i']
X = load_dataset()["x"]
x = X[np.where(idxs == 0, True, False), :, 0]
x = np.expand_dims(x, 2)
res1 = [0, 0]
res1[0], res1[1] = train_test_split(x, test_size=0.25, random_state=42)
res2 = load_good_holter()
res3 = [0, 0]
X = load_mit()
res3[0], res3[1] = train_test_split(X, test_size=0.1, random_state=32)
train_eval(model, (res1[0], res1[1]),
only_eval=True,
save_path=name + "_mit_test2",
generator=generator,
size=4096,
epochs=100,
noise_prob=[1 / 5, 1 / 5, 1 / 5, 1 / 5, 1 / 5, 0],
noise_type='em')