old_contents: from __future__ import absolute_import
  from __future__ import print_function
  import numpy as np
  
  from keras.preprocessing import sequence
  from keras.optimizers import SGD, RMSprop, Adagrad
  from keras.utils import np_utils
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation
  from keras.layers.embeddings import Embedding
  from keras.layers.recurrent import LSTM, GRU
  from keras.datasets import imdb
  
  '''
      Train a LSTM on the IMDB sentiment classification task.
  
      The dataset is actually too small for LSTM to be of any advantage 
      compared to simpler, much faster methods such as TF-IDF+LogReg.
  
      Notes: 
  
      - RNNs are tricky. Choice of batch size is important, 
      choice of loss and optimizer is critical, etc. 
      Most configurations won't converge.
  
      - LSTM loss decrease during training can be quite different 
      from what you see with CNNs/MLPs/etc. It's more or less a sigmoid
      instead of an inverse exponential.
  
      GPU command:
          THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_lstm.py
  
      250s/epoch on GPU (GT 650M), vs. 400s/epoch on CPU (2.4Ghz Core i7).
  '''
  
  max_features=20000
  maxlen = 100 # cut texts after this number of words (among top max_features most common words)
  batch_size = 16
  
  print("Loading data...")
  (X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features, test_split=0.2)
  print(len(X_train), 'train sequences')
  print(len(X_test), 'test sequences')
  
  print("Pad sequences (samples x time)")
  X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
  X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
  print('X_train shape:', X_train.shape)
  print('X_test shape:', X_test.shape)
  
  print('Build model...')
  model = Sequential()
  model.add(Embedding(max_features, 256))
  model.add(LSTM(256, 128)) # try using a GRU instead, for fun
  model.add(Dropout(0.5))
  model.add(Dense(128, 1))
  model.add(Activation('sigmoid'))
  
  # try using different optimizers and different optimizer configs
  model.compile(loss='binary_crossentropy', optimizer='adam', class_mode="binary")
  
  print("Train...")
  model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=5, validation_split=0.1, show_accuracy=True)
  score = model.evaluate(X_test, y_test, batch_size=batch_size)
  print('Test score:', score)
  
  classes = model.predict_classes(X_test, batch_size=batch_size)
  acc = np_utils.accuracy(classes, y_test)
  print('Test accuracy:', acc)
  
  
new_contents: from __future__ import absolute_import
  from __future__ import print_function
  import numpy as np
  
  from keras.preprocessing import sequence
  from keras.optimizers import SGD, RMSprop, Adagrad
  from keras.utils import np_utils
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation
  from keras.layers.embeddings import Embedding
  from keras.layers.recurrent import LSTM, GRU
  from keras.datasets import imdb
  
  '''
      Train a LSTM on the IMDB sentiment classification task.
  
      The dataset is actually too small for LSTM to be of any advantage 
      compared to simpler, much faster methods such as TF-IDF+LogReg.
  
      Notes: 
  
      - RNNs are tricky. Choice of batch size is important, 
      choice of loss and optimizer is critical, etc. 
      Some configurations won't converge.
  
      - LSTM loss decrease patterns during training can be quite different 
      from what you see with CNNs/MLPs/etc. 
  
      GPU command:
          THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_lstm.py
  
      250s/epoch on GPU (GT 650M), vs. 400s/epoch on CPU (2.4Ghz Core i7). 
      Increasing the batch_size increases the GPU speedup.
  '''
  
  max_features = 20000
  maxlen = 100 # cut texts after this number of words (among top max_features most common words)
  batch_size = 16
  
  np.random.seed(1337)
  
  print("Loading data...")
  (X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features, test_split=0.2)
  print(len(X_train), 'train sequences')
  print(len(X_test), 'test sequences')
  
  print("Pad sequences (samples x time)")
  X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
  X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
  print('X_train shape:', X_train.shape)
  print('X_test shape:', X_test.shape)
  
  print('Build model...')
  model = Sequential()
  model.add(Embedding(max_features, 256, mask_zero=True))
  model.add(LSTM(256, 128)) # try using a GRU instead, for fun
  model.add(Dropout(0.5))
  model.add(Dense(128, 1))
  model.add(Activation('sigmoid'))
  
  # try using different optimizers and different optimizer configs
  model.compile(loss='binary_crossentropy', optimizer='adam', class_mode="binary")
  
  print("Train...")
  model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=4, validation_split=0.1, show_accuracy=True)
  score = model.evaluate(X_test, y_test, batch_size=batch_size)
  print('Test score:', score)
  
  classes = model.predict_classes(X_test, batch_size=batch_size)
  acc = np_utils.accuracy(classes, y_test)
  print('Test accuracy:', acc)
  
  
subject: Update IMDB example