小编lil*_*ila的帖子

我正在尝试使用STS数据集基于这项工作实现句子相似性架构.标签是从0到1的归一化相似度分数,因此假设它是回归模型.

我的问题是损失直接NaN从第一个时代开始.我究竟做错了什么？

我已经尝试更新到最新的keras和theano版本.

我的模型的代码是:

def create_lstm_nn(input_dim):
    seq = Sequential()`
    # embedd using pretrained 300d embedding
    seq.add(Embedding(vocab_size, emb_dim, mask_zero=True, weights=[embedding_weights]))
    # encode via LSTM
    seq.add(LSTM(128))
    seq.add(Dropout(0.3))
    return seq

lstm_nn = create_lstm_nn(input_dim)

input_a = Input(shape=(input_dim,))
input_b = Input(shape=(input_dim,))

processed_a = lstm_nn(input_a)
processed_b = lstm_nn(input_b)

cos_distance = merge([processed_a, processed_b], mode='cos', dot_axes=1)
cos_distance = Reshape((1,))(cos_distance)
distance = Lambda(lambda x: 1-x)(cos_distance)

model = Model(input=[input_a, input_b], output=distance)

# train
rms = RMSprop()
model.compile(loss='mse', optimizer=rms)
model.fit([X1, X2], y, validation_split=0.3, batch_size=128, nb_epoch=20) …