classify_text.py 文件源码

def __init__(self, widths, vocab_size=5000):
        from keras.models import Sequential
        from keras.layers import Embedding, Dense, TimeDistributedMerge
        from keras.layers.advanced_activations import ELU
        from keras.preprocessing.sequence import pad_sequences
        from keras.optimizers import SGD
        self.n_classes = widths[-1]
        self.vocab_size = vocab_size
        self.word_to_int = {}
        self.int_to_word = np.ndarray(shape=(vocab_size+1,), dtype='int64')
        self.model = Sequential()
        self.model.add(Embedding(vocab_size, widths[0]))
        self.model.add(TimeDistributedMerge(mode='ave'))
        for width in widths[1:-1]:
            layer = Dense(output_dim=hidden_width, init='he_normal', activation=ELU(1.0))
            self.model.add(layer)
        self.model.add(
            Dense(
                n_classes,
                init='zero',
                activation='softmax'))
        sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
        self.model.compile(loss='categorical_crossentropy', optimizer=sgd)