I've recently reviewed an interesting implementation for [convolutional text classification](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/). 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?