fasttext:

'''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

Results on IMDB datasets with uni and bi-gram embeddings:

    Uni-gram: 0.8813 test accuracy after 5 epochs. 8s/epoch on i7 cpu.

    Bi-gram : 0.9056 test accuracy after 5 epochs. 2s/epoch on GTx 980M gpu.

'''

from __future__ import print_function

import numpy as np

from keras.preprocessing import sequence

from keras.models import Sequential

from keras.layers import Dense

from keras.layers import Embedding

from keras.layers import GlobalAveragePooling1D

from keras.datasets import imdb

import numpy as np

import json

import warnings

def load_data(path='imdb.npz', num_words=None, skip_top=0,

              maxlen=None, seed=113,

              start_char=1, oov_char=2, index_from=3, **kwargs):

    """Loads the IMDB dataset.

    # Arguments

        path: where to cache the data (relative to `~/.keras/dataset`).

        num_words: max number of words to include. Words are ranked

            by how often they occur (in the training set) and only

            the most frequent words are kept

        skip_top: skip the top N most frequently occurring words

            (which may not be informative).

        maxlen: sequences longer than this will be filtered out.

        seed: random seed for sample shuffling.

        start_char: The start of a sequence will be marked with this character.

            Set to 1 because 0 is usually the padding character.

        oov_char: words that were cut out because of the `num_words`

            or `skip_top` limit will be replaced with this character.

        index_from: index actual words with this index and higher.

    # Returns

        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

    # Raises

        ValueError: in case `maxlen` is so low

            that no input sequence could be kept.

    Note that the 'out of vocabulary' character is only used for

    words that were present in the training set but are not included

    because they're not making the `num_words` cut here.

    Words that were not seen in the training set but are in the test set

    have simply been skipped.

    """

    # Legacy support

    if 'nb_words' in kwargs:

        warnings.warn('The `nb_words` argument in `load_data` '

                      'has been renamed `num_words`.')

        num_words = kwargs.pop('nb_words')

    if kwargs:

        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))

    #path = get_file(path,

    #                origin='https://s3.amazonaws.com/text-datasets/imdb.npz',

    #                file_hash='599dadb1135973df5b59232a0e9a887c')

    with np.load(path) as f:

        x_train, labels_train = f['x_train'], f['y_train']

        x_test, labels_test = f['x_test'], f['y_test']

    np.random.seed(seed)

    indices = np.arange(len(x_train))

    np.random.shuffle(indices)

    x_train = x_train[indices]

    labels_train = labels_train[indices]

    indices = np.arange(len(x_test))

    np.random.shuffle(indices)

    x_test = x_test[indices]

    labels_test = labels_test[indices]

    xs = np.concatenate([x_train, x_test])

    labels = np.concatenate([labels_train, labels_test])

    if start_char is not None:

        xs = [[start_char] + [w + index_from for w in x] for x in xs]

    elif index_from:

        xs = [[w + index_from for w in x] for x in xs]

    if maxlen:

        xs, labels = _remove_long_seq(maxlen, xs, labels)

        if not xs:

            raise ValueError('After filtering for sequences shorter than maxlen=' +

                             str(maxlen) + ', no sequence was kept. '

                             'Increase maxlen.')

    if not num_words:

        num_words = max([max(x) for x in xs])

    # by convention, use 2 as OOV word

    # reserve 'index_from' (=3 by default) characters:

    # 0 (padding), 1 (start), 2 (OOV)

    if oov_char is not None:

        xs = [[w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs]

    else:

        xs = [[w for w in x if skip_top <= w < num_words] for x in xs]

    idx = len(x_train)

    x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])

    x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

    return (x_train, y_train), (x_test, y_test)

def create_ngram_set(input_list, ngram_value=2):

    """

    Extract a set of n-grams from a list of integers.

    >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)

    {(4, 9), (4, 1), (1, 4), (9, 4)}

    >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)

    [(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]

    """

    return set(zip(*[input_list[i:] for i in range(ngram_value)]))

def add_ngram(sequences, token_indice, ngram_range=2):

    """

    Augment the input list of list (sequences) by appending n-grams values.

    Example: adding bi-gram

    >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]

    >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}

    >>> add_ngram(sequences, token_indice, ngram_range=2)

    [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]]

    Example: adding tri-gram

    >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]

    >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018}

    >>> add_ngram(sequences, token_indice, ngram_range=3)

    [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42, 2018]]

    """

    new_sequences = []

    for input_list in sequences:

        new_list = input_list[:]

        for ngram_value in range(2, ngram_range + 1):

            for i in range(len(new_list) - ngram_value + 1):

                ngram = tuple(new_list[i:i + ngram_value])

                if ngram in token_indice:

                    new_list.append(token_indice[ngram])

        new_sequences.append(new_list)

    return new_sequences

# Set parameters:

# ngram_range = 2 will add bi-grams features

ngram_range = 1

max_features = 20000

maxlen = 400

batch_size = 32

embedding_dims = 50

epochs = 5

print('Loading data...')

# the data, split between train and test sets

#(x_train, y_train), (x_test, y_test) = load_data()

(x_train, y_train), (x_test, y_test) = load_data(num_words=max_features)

print(len(x_train), 'train sequences')

print(len(x_test), 'test sequences')

print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))

print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))

if ngram_range > 1:

    print('Adding {}-gram features'.format(ngram_range))

    # Create set of unique n-gram from the training set.

    ngram_set = set()

    for input_list in x_train:

        for i in range(2, ngram_range + 1):

            set_of_ngram = create_ngram_set(input_list, ngram_value=i)

            ngram_set.update(set_of_ngram)

    # Dictionary mapping n-gram token to a unique integer.

    # Integer values are greater than max_features in order

    # to avoid collision with existing features.

    start_index = max_features + 1

    token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}

    indice_token = {token_indice[k]: k for k in token_indice}

    # max_features is the highest integer that could be found in the dataset.

    max_features = np.max(list(indice_token.keys())) + 1

    # Augmenting x_train and x_test with n-grams features

    x_train = add_ngram(x_train, token_indice, ngram_range)

    x_test = add_ngram(x_test, token_indice, ngram_range)

    print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))

    print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))

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 GlobalAveragePooling1D, which will average the embeddings

# of all words in the document

model.add(GlobalAveragePooling1D())

# 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,

          epochs=epochs, validation_data=(x_test, y_test))

效果：

Train on 25000 samples, validate on 25000 samples

Epoch 1/50

2018-06-06 15:50:28.133461: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

25000/25000 [==============================] - 9s 379us/step - loss: 0.6125 - acc: 0.7431 - val_loss: 0.5050 - val_acc: 0.8227

Epoch 2/50

25000/25000 [==============================] - 10s 402us/step - loss: 0.4059 - acc: 0.8633 - val_loss: 0.3738 - val_acc: 0.8646

Epoch 3/50

25000/25000 [==============================] - 11s 441us/step - loss: 0.3061 - acc: 0.8934 - val_loss: 0.3219 - val_acc: 0.8783

Epoch 4/50

25000/25000 [==============================] - 9s 375us/step - loss: 0.2550 - acc: 0.9110 - val_loss: 0.2970 - val_acc: 0.8853

Epoch 5/50

可以看到一个epoch只需要10来秒，还是很快的！但是我训练到50个epoch后发现acc 100%，但是验证集上数据acc 86%，看来是过拟合了。

再看看传统cnn：

'''This example demonstrates the use of Convolution1D for text classification.

Gets to 0.89 test accuracy after 2 epochs.

90s/epoch on Intel i5 2.4Ghz CPU.

10s/epoch on Tesla K40 GPU.

'''

from __future__ import print_function

from keras.preprocessing import sequence

from keras.models import Sequential

from keras.layers import Dense, Dropout, Activation

from keras.layers import Embedding

from keras.layers import Conv1D, GlobalMaxPooling1D

from keras.datasets import imdb

# set parameters:

max_features = 5000

import numpy as np

import json

import warnings

def load_data(path='imdb.npz', num_words=None, skip_top=0,

              maxlen=None, seed=113,

              start_char=1, oov_char=2, index_from=3, **kwargs):

    """Loads the IMDB dataset.

    # Arguments

        path: where to cache the data (relative to `~/.keras/dataset`).

        num_words: max number of words to include. Words are ranked

            by how often they occur (in the training set) and only

            the most frequent words are kept

        skip_top: skip the top N most frequently occurring words

            (which may not be informative).

        maxlen: sequences longer than this will be filtered out.

        seed: random seed for sample shuffling.

        start_char: The start of a sequence will be marked with this character.

            Set to 1 because 0 is usually the padding character.

        oov_char: words that were cut out because of the `num_words`

            or `skip_top` limit will be replaced with this character.

        index_from: index actual words with this index and higher.

    # Returns

        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

    # Raises

        ValueError: in case `maxlen` is so low

            that no input sequence could be kept.

    Note that the 'out of vocabulary' character is only used for

    words that were present in the training set but are not included

    because they're not making the `num_words` cut here.

    Words that were not seen in the training set but are in the test set

    have simply been skipped.

    """

    # Legacy support

    if 'nb_words' in kwargs:

        warnings.warn('The `nb_words` argument in `load_data` '

                      'has been renamed `num_words`.')

        num_words = kwargs.pop('nb_words')

    if kwargs:

        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))

    #path = get_file(path,

    #                origin='https://s3.amazonaws.com/text-datasets/imdb.npz',

    #                file_hash='599dadb1135973df5b59232a0e9a887c')

    with np.load(path) as f:

        x_train, labels_train = f['x_train'], f['y_train']

        x_test, labels_test = f['x_test'], f['y_test']

    np.random.seed(seed)

    indices = np.arange(len(x_train))

    np.random.shuffle(indices)

    x_train = x_train[indices]

    labels_train = labels_train[indices]

    indices = np.arange(len(x_test))

    np.random.shuffle(indices)

    x_test = x_test[indices]

    labels_test = labels_test[indices]

    xs = np.concatenate([x_train, x_test])

    labels = np.concatenate([labels_train, labels_test])

    if start_char is not None:

        xs = [[start_char] + [w + index_from for w in x] for x in xs]

    elif index_from:

        xs = [[w + index_from for w in x] for x in xs]

    if maxlen:

        xs, labels = _remove_long_seq(maxlen, xs, labels)

        if not xs:

            raise ValueError('After filtering for sequences shorter than maxlen=' +

                             str(maxlen) + ', no sequence was kept. '

                             'Increase maxlen.')

    if not num_words:

        num_words = max([max(x) for x in xs])

    # by convention, use 2 as OOV word

    # reserve 'index_from' (=3 by default) characters:

    # 0 (padding), 1 (start), 2 (OOV)

    if oov_char is not None:

        xs = [[w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs]

    else:

        xs = [[w for w in x if skip_top <= w < num_words] for x in xs]

    idx = len(x_train)

    x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])

    x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

    return (x_train, y_train), (x_test, y_test)

def create_ngram_set(input_list, ngram_value=2):

    """

    Extract a set of n-grams from a list of integers.

    >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)

    {(4, 9), (4, 1), (1, 4), (9, 4)}

    >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)

    [(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]

    """

    return set(zip(*[input_list[i:] for i in range(ngram_value)]))

def add_ngram(sequences, token_indice, ngram_range=2):

    """

    Augment the input list of list (sequences) by appending n-grams values.

    Example: adding bi-gram

    >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]

    >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}

    >>> add_ngram(sequences, token_indice, ngram_range=2)

    [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]]

    Example: adding tri-gram

    >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]

    >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018}

    >>> add_ngram(sequences, token_indice, ngram_range=3)

    [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42, 2018]]

    """

    new_sequences = []

    for input_list in sequences:

        new_list = input_list[:]

        for ngram_value in range(2, ngram_range + 1):

            for i in range(len(new_list) - ngram_value + 1):

                ngram = tuple(new_list[i:i + ngram_value])

                if ngram in token_indice:

                    new_list.append(token_indice[ngram])

        new_sequences.append(new_list)

    return new_sequences

maxlen = 400

batch_size = 32

embedding_dims = 50

filters = 250

kernel_size = 3

hidden_dims = 250

epochs = 5

print('Loading data...')

(x_train, y_train), (x_test, y_test) = load_data(num_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))

model.add(Dropout(0.2))

# we add a Convolution1D, which will learn filters

# word group filters of size filter_length:

model.add(Conv1D(filters,

                 kernel_size,

                 padding='valid',

                 activation='relu',

                 strides=1))

# we use max pooling:

model.add(GlobalMaxPooling1D())

# We add a vanilla hidden layer:

model.add(Dense(hidden_dims))

model.add(Dropout(0.2))

model.add(Activation('relu'))

# We project onto a single unit output layer, and squash it with a sigmoid:

model.add(Dense(1))

model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',

              optimizer='adam',

              metrics=['accuracy'])

model.fit(x_train, y_train,

          batch_size=batch_size,

          epochs=epochs,

validation_data=(x_test, y_test))

效果：

Train on 25000 samples, validate on 25000 samples
Epoch 1/5
2018-06-06 16:10:34.733973: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
25000/25000 [==============================] - 117s 5ms/step - loss: 0.4044 - acc: 0.8007 - val_loss: 0.3212 - val_acc: 0.8600
Epoch 2/5
25000/25000 [==============================] - 121s 5ms/step - loss: 0.2323 - acc: 0.9057 - val_loss: 0.2903 - val_acc: 0.8801
Epoch 3/5
25000/25000 [==============================] - 124s 5ms/step - loss: 0.1640 - acc: 0.9377 - val_loss: 0.2720 - val_acc: 0.8900
Epoch 4/5
25000/25000 [==============================] - 116s 5ms/step - loss: 0.1136 - acc: 0.9579 - val_loss: 0.3353 - val_acc: 0.8811
Epoch 5/5
25000/25000 [==============================] - 114s 5ms/step - loss: 0.0764 - acc: 0.9726 - val_loss: 0.3958 - val_acc: 0.8793

可以看出cnn的确要慢10倍。

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