fasttext.py 文件源码

def setup_model(embeddings, seq_len, vocab_size):

    # Add input
    inputs = Input(shape=(seq_len, ), dtype='int32', name='inputs')

    # Add word vector embeddings
    embedding = Embedding(input_dim=vocab_size, output_dim=embedding_size, 
                         input_length=seq_len, name='embedding', 
                         trainable=True)(inputs)

    h = GlobalAveragePooling1D()(embedding)

    # Add output layer
    output = Dense(units=output_size,
                    activation='sigmoid',  
                    kernel_initializer='he_normal',
                    # kernel_regularizer=regularizers.l2(l2_reg_lambda),
                    # kernel_constraint=maxnorm(max_norm),
                    # bias_constraint=maxnorm(max_norm),
                    name='output')(h)

    # build the model
    model = Model(inputs=inputs, outputs=output)
    model.compile(loss={'output':'binary_crossentropy'},
                optimizer=Adam(lr=base_lr, epsilon=1e-6, decay=decay_rate),
                metrics=["accuracy"])

    return model