lstm_sin.py

import numpy as np
import pandas as pd
import tensorflow as tf
from matplotlib import pyplot as plt

from tensorflow.contrib import learn
from sklearn.metrics import mean_squared_error

from lstm_predictior import generate_data, lstm_model
LOG_DIR = 'resources/logs/'
TIMESTEPS = 1
RNN_LAYERS = [{'num_units': 4}]
DENSE_LAYERS = None
TRAINING_STEPS = 100
PRINT_STEPS = TRAINING_STEPS / 10
BATCH_SIZE = 100

regressor = learn.Estimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS, DENSE_LAYERS),
                            model_dir=LOG_DIR)

X, y = generate_data(np.sin, np.linspace(0, 100, 10000, dtype=np.float32), TIMESTEPS, seperate=False)

# create a lstm instance and validation monitor
validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],
                                                     every_n_steps=PRINT_STEPS,
                                                     early_stopping_rounds=1000)
# print(X['train'])
# print(y['train'])

regressor.fit(X['train'], y['train'],
              monitors=[validation_monitor],
              batch_size=BATCH_SIZE,
              steps=TRAINING_STEPS)

print X['test'].shape
print y['test'].shape
predicted = regressor.predict(X['test'],as_iterable=False)
#rmse = np.sqrt(((predicted - y['test']) ** 2).mean(axis=0))
score = mean_squared_error(predicted, y['test'])
print ("MSE: %f" % score)

plot_predicted, = plt.plot(predicted, label='predicted')
plot_test, = plt.plot(y['test'], label='test')
plt.legend(handles=[plot_predicted, plot_test])
plt.show()