Fix stateful LSTM example
This commit is contained in:
Francois Chollet
parent
5e9aafca33
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2091bfe911
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'''Example script to predict sequence using stateful rnns.
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At least 10 epochs are required before the generated text
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starts sounding coherent.
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'''
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import numpy as np
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import matplotlib.pyplot as mpl
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from keras.models import Sequential
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from keras.layers.core import Dense
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from keras.layers.recurrent import LSTM
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# since we are using stateful rnn tsteps can be set to 1
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tsteps = 1
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batch_size = 25
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epochs = 25
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# number of elements ahead that are used to make the prediction
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lahead = 1
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def gen_cosine_amp(amp=100, period=25, x0=0, xn=50000, step=1, k=0.0001):
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"""
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Generates an absolute cosine time series with the amplitude exponentially
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decreasing
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Keyword arguments:
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amp -- amplitude of the cosine function
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period -- period of the cosine function
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x0 -- initial x of the time series
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xn -- final x of the time series
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step -- step of the time series discretization
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k -- exponential rate
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"""
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cos = np.zeros(((xn - x0) * step, 1, 1))
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for i in range(len(cos)):
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idx = x0 + i * step
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cos[i, 0, 0] = amp * np.cos(idx / (2 * np.pi * period))
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cos[i, 0, 0] = cos[i, 0, 0] * np.exp(-k * idx)
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return cos
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print('Creating Data')
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cos = gen_cosine_amp()
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print('Input shape:')
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print(cos.shape)
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print('Calculating expected predicted_out')
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expected_out = np.zeros((len(cos), 1))
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for i in range(len(cos) - lahead):
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expected_out[i, 0] = np.mean(cos[i + 1:i + lahead + 1])
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print('Output shape')
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print(expected_out.shape)
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print('Creating Model')
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model = Sequential()
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model.add(
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LSTM(
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50,
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batch_input_shape=(
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batch_size,
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tsteps,
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1),
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return_sequences=True,
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stateful=True))
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model.add(
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LSTM(
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50,
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batch_input_shape=(
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batch_size,
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tsteps,
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1),
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return_sequences=False,
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stateful=True))
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model.add(Dense(1))
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model.compile(loss='rmse', optimizer='rmsprop')
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print('Training')
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for i in range(epochs):
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model.fit(
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cos,
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expected_out,
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batch_size=batch_size,
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verbose=1,
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nb_epoch=1)
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model.reset_states()
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print('Predicting')
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predicted_out = model.predict(cos, batch_size=batch_size)
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print('Ploting Results')
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mpl.subplot(2, 1, 1)
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mpl.plot(expected_out)
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mpl.title('Expected')
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mpl.subplot(2, 1, 2)
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mpl.plot(predicted_out)
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mpl.title('Predicted')
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mpl.show()
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84
examples/stateful_lstm.py
Normal file
84
examples/stateful_lstm.py
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@ -0,0 +1,84 @@
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'''Example script showing how to use stateful RNNs
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to model long sequences efficiently.
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'''
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from __future__ import print_function
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import numpy as np
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import matplotlib.pyplot as plt
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from keras.models import Sequential
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from keras.layers.core import Dense
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from keras.layers.recurrent import LSTM
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# since we are using stateful rnn tsteps can be set to 1
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tsteps = 1
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batch_size = 25
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epochs = 25
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# number of elements ahead that are used to make the prediction
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lahead = 1
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def gen_cosine_amp(amp=100, period=25, x0=0, xn=50000, step=1, k=0.0001):
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"""Generates an absolute cosine time series with the amplitude
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exponentially decreasing
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Arguments:
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amp: amplitude of the cosine function
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period: period of the cosine function
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x0: initial x of the time series
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xn: final x of the time series
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step: step of the time series discretization
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k: exponential rate
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"""
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cos = np.zeros(((xn - x0) * step, 1, 1))
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for i in range(len(cos)):
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idx = x0 + i * step
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cos[i, 0, 0] = amp * np.cos(idx / (2 * np.pi * period))
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cos[i, 0, 0] = cos[i, 0, 0] * np.exp(-k * idx)
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return cos
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print('Generating Data')
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cos = gen_cosine_amp()
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print('Input shape:', cos.shape)
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expected_output = np.zeros((len(cos), 1))
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for i in range(len(cos) - lahead):
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expected_output[i, 0] = np.mean(cos[i + 1:i + lahead + 1])
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print('Output shape')
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print(expected_output.shape)
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print('Creating Model')
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model = Sequential()
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model.add(LSTM(50,
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batch_input_shape=(batch_size, tsteps, 1),
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return_sequences=True,
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stateful=True))
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model.add(LSTM(50,
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batch_input_shape=(batch_size, tsteps, 1),
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return_sequences=False,
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stateful=True))
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model.add(Dense(1))
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model.compile(loss='rmse', optimizer='rmsprop')
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print('Training')
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for i in range(epochs):
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print('Epoch', i, '/', epochs)
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model.fit(cos,
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expected_output,
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batch_size=batch_size,
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verbose=1,
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nb_epoch=1)
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model.reset_states()
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print('Predicting')
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predicted_output = model.predict(cos, batch_size=batch_size)
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print('Ploting Results')
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plt.subplot(2, 1, 1)
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plt.plot(expected_output)
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plt.title('Expected')
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plt.subplot(2, 1, 2)
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plt.plot(predicted_output)
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plt.title('Predicted')
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plt.show()
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