def training(file_path, epochs, noise_std, do_gif=False):
def create_gif(path, title, step=1):
images = []
for i, filename in enumerate(os.listdir(path)):
if i % step == 0:
images.append(imageio.imread(os.path.join(path, filename)))
imageio.mimsave(os.path.join(ENC_PLOT_DATA_PATH, title),
images,
duration=0.1)
shutil.rmtree(path)
def get_data(file_path):
# TODO: try using 2 proteins: train on the first one test on the other one
data = read_data(file_path, normalize='std')
x_train, x_test = train_test_split(data,
test_size=0.1,
random_state=42)
return x_train, x_test
def add_noise(x, std):
return x + normal(0, std, size=x.shape)
for file in os.listdir(file_path):
if not (file.endswith(".csv")):
continue
name = file.replace(".csv", "")
print("Analysing %s" % name)
x_train, x_test = get_data(os.path.join(file_path, file))
x_train_noisy, x_test_noisy = add_noise(
x_train, std=noise_std), add_noise(x_test, std=noise_std)
network_model = Sequential(input_size=x_train.shape[1], encoded_size=2)
network_model.add(
Bidirectional(LSTM(10, return_sequences=True),
input_shape=(5, 10)))
network_model.add(Bidirectional(LSTM(10)))
network_model.add(Dense(5))
network_model.add(Activation('softmax'))
network_model.compile(loss='categorical_crossentropy',
optimizer='rmsprop')
data = read_data(os.path.join(file_path, file), normalize='std')
# encoded = AutoEncoder(load_model("encoding/models/proteins-autoencoder_6EQE_Angles_And_RSA.h5")).encode(data)
# df = pd.DataFrame(encoded)
# df.to_csv("encoding/models/6EQE_Angles_And_RSA_result.csv", index=False, header=False)
network_model.plot(data,
title=name,
file=os.path.join(ENC_PLOT_DATA_PATH, name + "_" +
str(epochs) + ".jpg"))
