85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
'''Example script showing how to use stateful RNNs
|
|
to model long sequences efficiently.
|
|
'''
|
|
from __future__ import print_function
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from keras.models import Sequential
|
|
from keras.layers import Dense, LSTM
|
|
|
|
|
|
# since we are using stateful rnn tsteps can be set to 1
|
|
tsteps = 1
|
|
batch_size = 25
|
|
epochs = 25
|
|
# number of elements ahead that are used to make the prediction
|
|
lahead = 1
|
|
|
|
|
|
def gen_cosine_amp(amp=100, period=1000, x0=0, xn=50000, step=1, k=0.0001):
|
|
"""Generates an absolute cosine time series with the amplitude
|
|
exponentially decreasing
|
|
|
|
Arguments:
|
|
amp: amplitude of the cosine function
|
|
period: period of the cosine function
|
|
x0: initial x of the time series
|
|
xn: final x of the time series
|
|
step: step of the time series discretization
|
|
k: exponential rate
|
|
"""
|
|
cos = np.zeros(((xn - x0) * step, 1, 1))
|
|
for i in range(len(cos)):
|
|
idx = x0 + i * step
|
|
cos[i, 0, 0] = amp * np.cos(2 * np.pi * idx / period)
|
|
cos[i, 0, 0] = cos[i, 0, 0] * np.exp(-k * idx)
|
|
return cos
|
|
|
|
|
|
print('Generating Data')
|
|
cos = gen_cosine_amp()
|
|
print('Input shape:', cos.shape)
|
|
|
|
expected_output = np.zeros((len(cos), 1))
|
|
for i in range(len(cos) - lahead):
|
|
expected_output[i, 0] = np.mean(cos[i + 1:i + lahead + 1])
|
|
|
|
print('Output shape')
|
|
print(expected_output.shape)
|
|
|
|
print('Creating Model')
|
|
model = Sequential()
|
|
model.add(LSTM(50,
|
|
batch_input_shape=(batch_size, tsteps, 1),
|
|
return_sequences=True,
|
|
stateful=True))
|
|
model.add(LSTM(50,
|
|
batch_input_shape=(batch_size, tsteps, 1),
|
|
return_sequences=False,
|
|
stateful=True))
|
|
model.add(Dense(1))
|
|
model.compile(loss='mse', optimizer='rmsprop')
|
|
|
|
print('Training')
|
|
for i in range(epochs):
|
|
print('Epoch', i, '/', epochs)
|
|
model.fit(cos,
|
|
expected_output,
|
|
batch_size=batch_size,
|
|
verbose=1,
|
|
nb_epoch=1,
|
|
shuffle=False)
|
|
model.reset_states()
|
|
|
|
print('Predicting')
|
|
predicted_output = model.predict(cos, batch_size=batch_size)
|
|
|
|
print('Plotting Results')
|
|
plt.subplot(2, 1, 1)
|
|
plt.plot(expected_output)
|
|
plt.title('Expected')
|
|
plt.subplot(2, 1, 2)
|
|
plt.plot(predicted_output)
|
|
plt.title('Predicted')
|
|
plt.show()
|