CODEWITHSUNDEEP
tensorflowkeraslstm
yang
yang

Reputation: 508

How to use embedding models in tensorflow hub with LSTM layer?

I'm learning tensorflow 2 working through the text classification with TF hub tutorial. It used an embedding module from TF hub. I was wondering if I could modify the model to include a LSTM layer. Here's what I've tried:

train_data, validation_data, test_data = tfds.load(
    name="imdb_reviews",
    split=('train[:60%]', 'train[60%:]', 'test'),
    as_supervised=True)
embedding = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1"
hub_layer = hub.KerasLayer(embedding, input_shape=[],
                           dtype=tf.string, trainable=True)

model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Embedding(10000, 50))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.summary()

model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])

history = model.fit(train_data.shuffle(10000).batch(512),
                    epochs=10,
                    validation_data=validation_data.batch(512),
                    verbose=1)

results = model.evaluate(test_data.batch(512), verbose=2)

for name, value in zip(model.metrics_names, results):
  print("%s: %.3f" % (name, value))

I don't know how to get the vocabulary size from the hub_layer. So I just put 10000 there. When run it, it throws this exception:

tensorflow.python.framework.errors_impl.InvalidArgumentError:  indices[480,1] = -6 is not in [0, 10000)
     [[node sequential/embedding/embedding_lookup (defined at .../learning/tensorflow/text_classify.py:36) ]] [Op:__inference_train_function_36284]

Errors may have originated from an input operation.
Input Source operations connected to node sequential/embedding/embedding_lookup:
 sequential/embedding/embedding_lookup/34017 (defined at Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/contextlib.py:112)

Function call stack:
train_function

I stuck here. My questions are:

  1. how should I use the embedding module from TF hub to feed an LSTM layer? it looks like embedding lookup has some issues with the setting.

  2. how do I get the vocabulary size from the hub layer?

Thanks

Upvotes: 4

Views: 1894

Answers (1)

yang
yang

Reputation: 508

Finally figured out the way to link pre-trained embeddings to LSTM or other layers. Just post the steps here in case anyone feels helpful.

Embedding layer has to be the first layer in the model. (hub_layer is the same as Embedding layer.) The not very intuitive part is that any text input to the hub layer will be converted to only one vector of shape [embedding_dim]. You need to do sentence splitting and tokenization to make sure whatever input to the model is a sequence in the form of array of arrays. e.g., "Let us prepare the data." should be converted to [["let"],["us"],["prepare"], ["the"], ["data"]]. You will also need to pad the sequences if you are using batch mode.

In addition, you will need to convert your target tokens to int if your training labels are strings. The input to the model is array of strings with shape [batch, seq_length], the hub embedding layer converts it to [batch, seq_length, embed_dim]. (If you add a LSTM or other RNN layer, the output from the layer is [batch, seq_length, rnn_units]. ) The output dense layer will output index of text instead of actual text. The index of text is stored in the downloaded tfhub directory as "tokens.txt". You can load the file and convert text to the corresponding index. Otherwise you cannot compute the loss.

Upvotes: 2

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