old_contents: '''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()
  
new_contents: '''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,
                 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()
  
subject: Remove extraneous batch_input_shape (#4393)