old_contents: '''This example demonstrates the use of fasttext for text classification
  
  Based on Joulin et al's paper:
  
  Bags of Tricks for Efficient Text Classification
  https://arxiv.org/abs/1607.01759
  
  Can achieve accuracy around 88% after 5 epochs in 70s.
  
  '''
  
  from __future__ import print_function
  import numpy as np
  np.random.seed(1337)  # for reproducibility
  
  from keras.preprocessing import sequence
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.layers import Embedding
  from keras.layers import Convolution1D, MaxPooling1D, AveragePooling1D
  from keras.datasets import imdb
  from keras import backend as K
  
  
  # set parameters:
  max_features = 20000
  maxlen = 400
  batch_size = 32
  embedding_dims = 20
  nb_epoch = 5
  
  print('Loading data...')
  (X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
  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()
  
  # we start off with an efficient embedding layer which maps
  # our vocab indices into embedding_dims dimensions
  model.add(Embedding(max_features,
                      embedding_dims,
                      input_length=maxlen))
  
  # we add a AveragePooling1D, which
  model.add(AveragePooling1D(pool_length=model.output_shape[1]))
  
  # We flatten the output of the conv layer,
  # so that we can add a dense layer:
  model.add(Flatten())
  
  # We project onto a single unit output layer, and squash it with a sigmoid:
  model.add(Dense(1))
  model.add(Dropout(0.2))
  model.add(Activation('sigmoid'))
  
  model.compile(loss='binary_crossentropy',
                optimizer='adam',
                metrics=['accuracy'])
  
  model.fit(X_train, y_train,
            batch_size=batch_size,
            nb_epoch=nb_epoch,
            validation_data=(X_test, y_test))
  
new_contents: '''This example demonstrates the use of fasttext for text classification
  
  Based on Joulin et al's paper:
  
  Bags of Tricks for Efficient Text Classification
  https://arxiv.org/abs/1607.01759
  
  Can achieve accuracy around 88% after 5 epochs in 70s.
  
  '''
  
  from __future__ import print_function
  import numpy as np
  np.random.seed(1337)  # for reproducibility
  
  from keras.preprocessing import sequence
  from keras.models import Sequential
  from keras.layers import Dense, Activation, Flatten
  from keras.layers import Embedding
  from keras.layers import AveragePooling1D
  from keras.datasets import imdb
  from keras import backend as K
  
  
  # set parameters:
  max_features = 20000
  maxlen = 400
  batch_size = 32
  embedding_dims = 20
  nb_epoch = 5
  
  print('Loading data...')
  (X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
  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()
  
  # we start off with an efficient embedding layer which maps
  # our vocab indices into embedding_dims dimensions
  model.add(Embedding(max_features,
                      embedding_dims,
                      input_length=maxlen))
  
  # we add a AveragePooling1D, which will average the embeddings
  # of all words in the document
  model.add(AveragePooling1D(pool_length=model.output_shape[1]))
  
  # We flatten the output of the conv layer,
  # so that we can add a dense layer:
  model.add(Flatten())
  
  # We project onto a single unit output layer, and squash it with a sigmoid:
  model.add(Dense(1, activation = 'sigmoid'))
  
  model.compile(loss='binary_crossentropy',
                optimizer='adam',
                metrics=['accuracy'])
  
  model.fit(X_train, y_train,
            batch_size=batch_size,
            nb_epoch=nb_epoch,
            validation_data=(X_test, y_test))
  
subject: Remove Dropout and unnecessary imports