Using a pre-trained word embedding (word2vec or Glove) in TensorFlow

I've recently reviewed an interesting implementation for convolutional text classification. However all TensorFlow code I've reviewed uses a random (not pre-trained) embedding vectors like the following:

with tf.device('/cpu:0'), tf.name_scope("embedding"):
    W = tf.Variable(
        tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
        name="W")
    self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x)
    self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

Does anybody know how to use the results of Word2vec or a GloVe pre-trained word embedding instead of a random one?

There are a few ways that you can use a pre-trained embedding in TensorFlow. Let's say that you have the embedding in a NumPy array called embedding, with vocab_size rows and embedding_dim columns and you want to create a tensor W that can be used in a call to tf.nn.embedding_lookup().

1. Simply create W as a tf.constant() that takes embedding as its value:

   W = tf.constant(embedding, name="W")
   

   This is the easiest approach, but it is not memory efficient because the value of a tf.constant() is stored multiple times in memory. Since embedding can be very large, you should only use this approach for toy examples.

2. Create W as a tf.Variable and initialize it from the NumPy array via a tf.placeholder():

   W = tf.Variable(tf.constant(0.0, shape=[vocab_size, embedding_dim]),
                   trainable=False, name="W")
   
   embedding_placeholder = tf.placeholder(tf.float32, [vocab_size, embedding_dim])
   embedding_init = W.assign(embedding_placeholder)
   
   # ...
   sess = tf.Session()
   
   sess.run(embedding_init, feed_dict={embedding_placeholder: embedding})
   

   This avoid storing a copy of embedding in the graph, but it does require enough memory to keep two copies of the matrix in memory at once (one for the NumPy array, and one for the tf.Variable). Note that I've assumed that you want to hold the embedding matrix constant during training, so W is created with trainable=False.

3. If the embedding was trained as part of another TensorFlow model, you can use a tf.train.Saver to load the value from the other model's checkpoint file. This means that the embedding matrix can bypass Python altogether. Create W as in option 2, then do the following:

   W = tf.Variable(...)
   
   embedding_saver = tf.train.Saver({"name_of_variable_in_other_model": W})
   
   # ...
   sess = tf.Session()
   embedding_saver.restore(sess, "checkpoint_filename.ckpt")
   

I use this method to load and share embedding.

W = tf.get_variable(name="W", shape=embedding.shape, initializer=tf.constant_initializer(embedding), trainable=False)