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<h1 id="how-to-use-a-stateful-lstm-model-stateful-vs-stateless-lstm-performance-comparison">How to use a stateful LSTM model, stateful vs stateless LSTM performance comparison</h1>
<p><a href="/layers/recurrent/#lstm">More documentation about the Keras LSTM model</a></p>
<p>The models are trained on an input/output pair, where
the input is a generated uniformly distributed
random sequence of length = <code>input_len</code>,
and the output is a moving average of the input with window length = <code>tsteps</code>.
Both <code>input_len</code> and <code>tsteps</code> are defined in the "editable parameters"
section.</p>
<p>A larger <code>tsteps</code> value means that the LSTM will need more memory
to figure out the input-output relationship.
This memory length is controlled by the <code>lahead</code> variable (more details below).</p>
<p>The rest of the parameters are:</p>
<ul>
<li><code>input_len</code>: the length of the generated input sequence</li>
<li><code>lahead</code>: the input sequence length that the LSTM
is trained on for each output point</li>
<li><code>batch_size</code>, <code>epochs</code>: same parameters as in the <code>model.fit(...)</code>
function</li>
</ul>
<p>When <code>lahead > 1</code>, the model input is preprocessed to a "rolling window view"
of the data, with the window length = <code>lahead</code>.
This is similar to sklearn's <code>view_as_windows</code>
with <code>window_shape</code> <a href="http://scikit-image.org/docs/0.10.x/api/skimage.util.html#view-as-windows">being a single number.</a></p>
<p>When <code>lahead < tsteps</code>, only the stateful LSTM converges because its
statefulness allows it to see beyond the capability that lahead
gave it to fit the n-point average. The stateless LSTM does not have
this capability, and hence is limited by its <code>lahead</code> parameter,
which is not sufficient to see the n-point average.</p>
<p>When <code>lahead >= tsteps</code>, both the stateful and stateless LSTM converge.</p>
<pre><code class="python">from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM
# ----------------------------------------------------------
# EDITABLE PARAMETERS
# Read the documentation in the script head for more details
# ----------------------------------------------------------
# length of input
input_len = 1000
# The window length of the moving average used to generate
# the output from the input in the input/output pair used
# to train the LSTM
# e.g. if tsteps=2 and input=[1, 2, 3, 4, 5],
# then output=[1.5, 2.5, 3.5, 4.5]
tsteps = 2
# The input sequence length that the LSTM is trained on for each output point
lahead = 1
# training parameters passed to "model.fit(...)"
batch_size = 1
epochs = 10
# ------------
# MAIN PROGRAM
# ------------
print("*" * 33)
if lahead >= tsteps:
print("STATELESS LSTM WILL ALSO CONVERGE")
else:
print("STATELESS LSTM WILL NOT CONVERGE")
print("*" * 33)
np.random.seed(1986)
print('Generating Data...')
def gen_uniform_amp(amp=1, xn=10000):
"""Generates uniform random data between
-amp and +amp
and of length xn
# Arguments
amp: maximum/minimum range of uniform data
xn: length of series
"""
data_input = np.random.uniform(-1 * amp, +1 * amp, xn)
data_input = pd.DataFrame(data_input)
return data_input
# Since the output is a moving average of the input,
# the first few points of output will be NaN
# and will be dropped from the generated data
# before training the LSTM.
# Also, when lahead > 1,
# the preprocessing step later of "rolling window view"
# will also cause some points to be lost.
# For aesthetic reasons,
# in order to maintain generated data length = input_len after pre-processing,
# add a few points to account for the values that will be lost.
to_drop = max(tsteps - 1, lahead - 1)
data_input = gen_uniform_amp(amp=0.1, xn=input_len + to_drop)
# set the target to be a N-point average of the input
expected_output = data_input.rolling(window=tsteps, center=False).mean()
# when lahead > 1, need to convert the input to "rolling window view"
# https://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html
if lahead > 1:
data_input = np.repeat(data_input.values, repeats=lahead, axis=1)
data_input = pd.DataFrame(data_input)
for i, c in enumerate(data_input.columns):
data_input[c] = data_input[c].shift(i)
# drop the nan
expected_output = expected_output[to_drop:]
data_input = data_input[to_drop:]
print('Input shape:', data_input.shape)
print('Output shape:', expected_output.shape)
print('Input head: ')
print(data_input.head())
print('Output head: ')
print(expected_output.head())
print('Input tail: ')
print(data_input.tail())
print('Output tail: ')
print(expected_output.tail())
print('Plotting input and expected output')
plt.plot(data_input[0][:10], '.')
plt.plot(expected_output[0][:10], '-')
plt.legend(['Input', 'Expected output'])
plt.title('Input')
plt.show()
def create_model(stateful):
model = Sequential()
model.add(LSTM(20,
input_shape=(lahead, 1),
batch_size=batch_size,
stateful=stateful))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam')
return model
print('Creating Stateful Model...')
model_stateful = create_model(stateful=True)
# split train/test data
def split_data(x, y, ratio=0.8):
to_train = int(input_len * ratio)
# tweak to match with batch_size
to_train -= to_train % batch_size
x_train = x[:to_train]
y_train = y[:to_train]
x_test = x[to_train:]
y_test = y[to_train:]
# tweak to match with batch_size
to_drop = x.shape[0] % batch_size
if to_drop > 0:
x_test = x_test[:-1 * to_drop]
y_test = y_test[:-1 * to_drop]
# some reshaping
reshape_3 = lambda x: x.values.reshape((x.shape[0], x.shape[1], 1))
x_train = reshape_3(x_train)
x_test = reshape_3(x_test)
reshape_2 = lambda x: x.values.reshape((x.shape[0], 1))
y_train = reshape_2(y_train)
y_test = reshape_2(y_test)
return (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = split_data(data_input, expected_output)
print('x_train.shape: ', x_train.shape)
print('y_train.shape: ', y_train.shape)
print('x_test.shape: ', x_test.shape)
print('y_test.shape: ', y_test.shape)
print('Training')
for i in range(epochs):
print('Epoch', i + 1, '/', epochs)
# Note that the last state for sample i in a batch will
# be used as initial state for sample i in the next batch.
# Thus we are simultaneously training on batch_size series with
# lower resolution than the original series contained in data_input.
# Each of these series are offset by one step and can be
# extracted with data_input[i::batch_size].
model_stateful.fit(x_train,
y_train,
batch_size=batch_size,
epochs=1,
verbose=1,
validation_data=(x_test, y_test),
shuffle=False)
model_stateful.reset_states()
print('Predicting')
predicted_stateful = model_stateful.predict(x_test, batch_size=batch_size)
print('Creating Stateless Model...')
model_stateless = create_model(stateful=False)
print('Training')
model_stateless.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test),
shuffle=False)
print('Predicting')
predicted_stateless = model_stateless.predict(x_test, batch_size=batch_size)
# ----------------------------
print('Plotting Results')
plt.subplot(3, 1, 1)
plt.plot(y_test)
plt.title('Expected')
plt.subplot(3, 1, 2)
# drop the first "tsteps-1" because it is not possible to predict them
# since the "previous" timesteps to use do not exist
plt.plot((y_test - predicted_stateful).flatten()[tsteps - 1:])
plt.title('Stateful: Expected - Predicted')
plt.subplot(3, 1, 3)
plt.plot((y_test - predicted_stateless).flatten())
plt.title('Stateless: Expected - Predicted')
plt.show()
</code></pre>
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