'''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 AveragePooling1D 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))

Implement a fasttext example (#3446)