What are embedding layers in TF learn explain with example

This recipe explains what are embedding layers in TF learn with example

Recipe Objective

This recipe explains what are embedding layer in TFLayer.

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Importing Libraries

We'll import the necessary libraries.

import numpy as np
from .. import utils
from .. import initializations
from .. import variables as vs
import tensorflow.compat.v1 as tf
from .recurrent import retrieve_seq_length_op
from __future__ import division, print_function, absolute_import

Embedding

Its syntax is: tflearn.layers.embedding_ops.embedding (incoming, input_dim, output_dim, validate_indices=False, weights_init='truncated_normal', trainable=True, restore=True, reuse=False, scope=None, name='Embedding')

def Embedding(incoming, input_dim, output_dim, validate_indices=False, weights_init='truncated_normal', trainable=True, restore=True, reuse=False, scope=None, name="Embedding"):

    ip = utils.get_incoming_shape(incoming)
    assert len(ip) == 2, "Incoming Tensor shape must be 2-D"

    if isinstance(weights_init, str):
       weights_init = initializations.get(weights_init)()

    with tf.variable_scope(scope, default_name=name, values=[incoming], reuse=reuse) as scope:
       name = scope.name
       with tf.device('/gpu:0'):
          w = vs.variable("w", shape=[input_dim, output_dim], initializer=weights_init, trainable=trainable, restore=restore)
          tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/' + name, w)
       inf = tf.cast(incoming, tf.int32)
       inf = tf.nn.embedding_lookup(w, inf, validate_indices=validate_indices)
    inf.w = w
    inf.scope = scope

    shape = [-1] + inf.get_shape().as_list()[1:3] + [1]
    inf.seq_length = retrieve_seq_length_op(tf.reshape(incoming, shape))

    tf.add_to_collection(tf.GraphKeys.LAYER_TENSOR + '/' + name, inf)

    return inf

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