6f528bb277012ec96bae86c30ef1dbd5eb3c2130,tensorlayer/layers.py,Word2vecEmbeddingInputlayer,__init__,#Word2vecEmbeddingInputlayer#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,244

Before Change

        // multiply by the embedding matrix.
        // embed is the outputs of the hidden layer (embedding layer), it is a
        // row vector with "embedding_size" values.
        embeddings = tf.Variable(
            E_init(shape=[vocabulary_size, embedding_size], **E_init_args))
        embed = tf.nn.embedding_lookup(embeddings, self.inputs)

        // Construct the variables for the NCE loss (i.e. negative sampling)
        nce_weights = tf.Variable(
            nce_W_init(shape=[vocabulary_size, embedding_size], **nce_W_init_args))
        nce_biases = tf.Variable(nce_b_init([vocabulary_size], **nce_b_init_args))

        // Compute the average NCE loss for the batch.
        // tf.nce_loss automatically draws a new sample of the negative labels
        // each time we evaluate the loss.
        self.nce_cost = tf.reduce_mean(
            tf.nn.nce_loss(weights=nce_weights, biases=nce_biases,
                           inputs=embed, labels=train_labels,
                           num_sampled=num_sampled, num_classes=vocabulary_size,

After Change

        // multiply by the embedding matrix.
        // embed is the outputs of the hidden layer (embedding layer), it is a
        // row vector with "embedding_size" values.
        with tf.variable_scope(name) as vs:
            // embeddings = tf.Variable(
            //     E_init(shape=[vocabulary_size, embedding_size], **E_init_args))
            embeddings = tf.get_variable(name="embeddings",
                                    shape=(vocabulary_size, embedding_size),
                                    initializer=E_init,
                                    **E_init_args)
            embed = tf.nn.embedding_lookup(embeddings, self.inputs)

            // Construct the variables for the NCE loss (i.e. negative sampling)
            // nce_weights = tf.Variable(
            //     nce_W_init(shape=[vocabulary_size, embedding_size], **nce_W_init_args))
            nce_weights = tf.get_variable(name="nce_weights",
                                    shape=(vocabulary_size, embedding_size),
                                    initializer=nce_W_init,
                                    **nce_W_init_args)
            // nce_biases = tf.Variable(nce_b_init([vocabulary_size], **nce_b_init_args))
            nce_biases = tf.get_variable(name="nce_biases",
                                    shape=(vocabulary_size),
                                    initializer=nce_b_init,
                                    **nce_b_init_args)

        // Compute the average NCE loss for the batch.
        // tf.nce_loss automatically draws a new sample of the negative labels
        // each time we evaluate the loss.
        self.nce_cost = tf.reduce_mean(
            tf.nn.nce_loss(weights=nce_weights, biases=nce_biases,
                           inputs=embed, labels=train_labels,
                           num_sampled=num_sampled, num_classes=vocabulary_size,

Instances