Spaces:
Runtime error
Runtime error
import tensorflow.keras as tf | |
import numpy as np | |
from sklearn.preprocessing import StandardScaler | |
verbose = 0 | |
# TODO: Refactor this module | |
def predict_series(values, r1_nodes=5, r2_nodes=0, fc1_nodes=0, steps=20, use_lstm=True, *args, **kwargs): | |
train = np.array(values) | |
train_last_value = train[-1] | |
train = train[1:] - train[:-1] | |
sc = StandardScaler() | |
train = sc.fit_transform(train.reshape(-1, 1)) | |
T = 25 | |
X = [] | |
Y = [] | |
for t in range(len(train) - T): | |
x = train[t:t + T] | |
X.append(x) | |
Y.append(train[t + T]) | |
X = np.array(X).reshape(-1, T, 1) | |
Y = np.array(Y) | |
i = tf.layers.Input(shape=(T, 1)) | |
if use_lstm: | |
rnn_layer = tf.layers.LSTM | |
else: | |
rnn_layer = tf.layers.GRU | |
if r2_nodes: | |
x = rnn_layer(r1_nodes, return_sequences=True)(i) | |
x = rnn_layer(r2_nodes)(x) | |
else: | |
x = rnn_layer(r1_nodes)(i) | |
if fc1_nodes: | |
x = tf.layers.Dense(fc1_nodes, activation='relu')(x) | |
x = tf.layers.Dense(1)(x) | |
model = tf.models.Model(i, x) | |
# TODO: optimize execution time | |
"""lr_schedule = tf.optimizers.schedules.ExponentialDecay( | |
initial_learning_rate=0.2, | |
decay_steps=10, | |
decay_rate=0.8) | |
optimizer = tf.optimizers.Ftrl(learning_rate=0.001, learning_rate_power=-0.1)""" | |
model.compile( | |
loss='mse', #tf.losses.LogCosh(), | |
optimizer=tf.optimizers.Adamax(lr=0.1) #LogCosh()'sgd' | |
) | |
callbacks = [tf.callbacks.EarlyStopping(patience=150, monitor='loss', restore_best_weights=True)] | |
r = model.fit( | |
X, Y, | |
epochs=500, | |
callbacks=callbacks, | |
verbose=verbose, | |
validation_split=0.0 | |
) | |
pred = np.array([]) | |
last_x = X[-1] | |
for _ in range(steps): | |
p = model.predict(last_x.reshape(1, -1, 1))[0, 0] | |
pred = np.append(pred, p) | |
last_x = np.roll(last_x, -1) | |
last_x[-1] = p | |
pred = sc.inverse_transform(pred.reshape(-1, 1)) | |
pred.reshape(-1) | |
pred[0] = train_last_value + pred[0] | |
for i in range(1, len(pred)): | |
pred[i] += pred[i-1] | |
result = {'result': list(pred.reshape(-1)), 'epochs': r.epoch[-1] + 1, 'loss': min(r.history['loss']), 'loss_last': r.history['loss'][-1]} | |
return result | |
if __name__ == "__main__": | |
from time import time | |
t1 = time() | |
verbose = 2 | |
data = np.sin(np.arange(0.0, 28.0, 0.35)*2) | |
result = predict_series(data, steps=66, r1_nodes=14, r2_nodes=14, fc1_nodes=20) | |
print('exec time: {:8.3f}'.format(time()-t1)) | |
#print(result['result'][:2]) | |
print(print(result['epochs'], result['loss'])) | |
import seaborn as sns | |
sns.lineplot(x=range(30), y=data[-30:], color='r') | |
sns.lineplot(x=range(30, 30+len(result['result'])), y=result['result'], color='b') | |