在 TensorFlow 中使用预先训练好的单词嵌入(word2vec 或 Glove)

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最佳答案

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().

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.
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.
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")
```

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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)

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The answer of @mrry is not right because it provoques the overwriting of the embeddings weights each the network is run, so if you are following a minibatch approach to train your network, you are overwriting the weights of the embeddings. So, on my point of view the right way to pre-trained embeddings is:

embeddings = tf.get_variable("embeddings", shape=[dim1, dim2], initializer=tf.constant_initializer(np.array(embeddings_matrix))

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I was also facing embedding issue, So i wrote detailed tutorial with dataset. Here I would like to add what I tried You can also try this method,

import tensorflow as tf


tf.reset_default_graph()


input_x=tf.placeholder(tf.int32,shape=[None,None])


#you have to edit shape according to your embedding size




Word_embedding = tf.get_variable(name="W", shape=[400000,100], initializer=tf.constant_initializer(np.array(word_embedding)), trainable=False)
embedding_loopup= tf.nn.embedding_lookup(Word_embedding,input_x)


with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for ii in final_:
print(sess.run(embedding_loopup,feed_dict={input_x:[ii]}))

Here is working detailed Tutorial Ipython example if you want to understand from scratch , take a look .

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2.0 Compatible Answer: There are many Pre-Trained Embeddings, which are developed by Google and which have been Open Sourced.

Some of them are Universal Sentence Encoder (USE), ELMO, BERT, etc.. and it is very easy to reuse them in your code.

Code to reuse the Pre-Trained Embedding, Universal Sentence Encoder is shown below:

  !pip install "tensorflow_hub>=0.6.0"
!pip install "tensorflow>=2.0.0"


import tensorflow as tf
import tensorflow_hub as hub


module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
embed = hub.KerasLayer(module_url)
embeddings = embed(["A long sentence.", "single-word",
"http://example.com"])
print(embeddings.shape)  #(3,128)

For more information the Pre-Trained Embeddings developed and open-sourced by Google, refer TF Hub Link.

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With tensorflow version 2 its quite easy if you use the Embedding layer

X=tf.keras.layers.Embedding(input_dim=vocab_size,
output_dim=300,
input_length=Length_of_input_sequences,
embeddings_initializer=matrix_of_pretrained_weights
)(ur_inp)