Add example of stateful LSTM sequence prediction

examples/lstm_stateful_seq.py

@@ -0,0 +1,97 @@
+'''Example script to predict sequence using stateful rnns.
+At least 10 epochs are required before the generated text
+starts sounding coherent.
+'''
+
+import numpy as np
+import matplotlib.pyplot as mpl
+from keras.models import Sequential
+from keras.layers.core import Dense
+from keras.layers.recurrent import 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=25, x0=0, xn=50000, step=1, k=0.0001):
+    """
+    Generates an absolute cosine time series with the amplitude exponentially
+    decreasing
+
+    Keyword 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(idx / (2 * np.pi * period))
+        cos[i, 0, 0] = cos[i, 0, 0] * np.exp(-k * idx)
+    return cos
+
+
+print('Creating Data')
+cos = gen_cosine_amp()
+print('Input shape:')
+print(cos.shape)
+print('Calculating expected predicted_out')
+expected_out = np.zeros((len(cos), 1))
+for i in range(len(cos) - lahead):
+    expected_out[i, 0] = np.mean(cos[i + 1:i + lahead + 1])
+
+print('Output shape')
+print(expected_out.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='rmse', optimizer='rmsprop')
+
+print('Training')
+for i in range(epochs):
+    model.fit(
+        cos,
+        expected_out,
+        batch_size=batch_size,
+        verbose=1,
+        nb_epoch=1)
+    model.reset_states()
+
+print('Predicting')
+predicted_out = model.predict(cos, batch_size=batch_size)
+
+print('Ploting Results')
+mpl.subplot(2, 1, 1)
+mpl.plot(expected_out)
+mpl.title('Expected')
+mpl.subplot(2, 1, 2)
+mpl.plot(predicted_out)
+mpl.title('Predicted')
+mpl.show()