CODEWITHSUNDEEP
pythonkerassentence-similarity
lila
lila

Reputation: 121

Sentence similarity using keras

I'm trying to implement sentence similarity architecture based on this work using the STS dataset. Labels are normalized similarity scores from 0 to 1 so it is assumed to be a regression model.

My problem is that the loss goes directly to NaN starting from the first epoch. What am I doing wrong?

I have already tried updating to latest keras and theano versions.

The code for my model is:

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)

I also tried using a simple Lambda instead of the Merge layer, but it has the same result.

def cosine_distance(vests):
    x, y = vests
    x = K.l2_normalize(x, axis=-1)
    y = K.l2_normalize(y, axis=-1)
    return -K.mean(x * y, axis=-1, keepdims=True)

def cos_dist_output_shape(shapes):
    shape1, shape2 = shapes
    return (shape1[0],1)

distance = Lambda(cosine_distance, output_shape=cos_dist_output_shape)([processed_a, processed_b])

Upvotes: 12

Views: 7146

Answers (2)

JAB
JAB

Reputation: 12781

I didn't run into the nan issue, but my loss wouldn't change. I found this info check this out

def cosine_distance(shapes):
    y_true, y_pred = shapes
    def l2_normalize(x, axis):
        norm = K.sqrt(K.sum(K.square(x), axis=axis, keepdims=True))
        return K.sign(x) * K.maximum(K.abs(x), K.epsilon()) /     K.maximum(norm, K.epsilon())
    y_true = l2_normalize(y_true, axis=-1)
    y_pred = l2_normalize(y_pred, axis=-1)
    return K.mean(1 - K.sum((y_true * y_pred), axis=-1))

Upvotes: 0

Kun
Kun

Reputation: 591

The nan is a common issue in deep learning regression. Because you are using Siamese network, you can try followings:

  1. check your data: do they need to be normalized?
  2. try to add an Dense layer into your network as the last layer, but be careful picking up an activation function, e.g. relu
  3. try to use another loss function, e.g. contrastive_loss
  4. smaller your learning rate, e.g. 0.0001
  5. cos mode does not carefully deal with division by zero, might be the cause of NaN

It is not easy to make deep learning work perfectly.

Upvotes: 3

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