Python tensorflow.python.keras.initializers.RandomNormal() Examples

The following are 2 code examples of tensorflow.python.keras.initializers.RandomNormal(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.keras.initializers , or try the search function .
Example #1
Source Project: DeepCTR   Author: shenweichen   File: feature_column.py    License: Apache License 2.0 5 votes vote down vote up
def __new__(cls, name, vocabulary_size, embedding_dim=4, use_hash=False, dtype="int32", embeddings_initializer=None,
                embedding_name=None,
                group_name=DEFAULT_GROUP_NAME, trainable=True):

        if embedding_dim == "auto":
            embedding_dim = 6 * int(pow(vocabulary_size, 0.25))
        if embeddings_initializer is None:
            embeddings_initializer = RandomNormal(mean=0.0, stddev=0.0001, seed=2020)

        if embedding_name is None:
            embedding_name = name

        return super(SparseFeat, cls).__new__(cls, name, vocabulary_size, embedding_dim, use_hash, dtype,
                                              embeddings_initializer,
                                              embedding_name, group_name, trainable) 
Example #2
Source Project: icme2019   Author: ShenDezhou   File: input_embedding.py    License: MIT License 4 votes vote down vote up
def create_embedding_dict(feature_dim_dict, embedding_size, init_std, seed, l2_reg, prefix='sparse', seq_mask_zero=True):
    if embedding_size == 'auto':

        sparse_embedding = {feat.name: Embedding(feat.dimension, 6 * int(pow(feat.dimension, 0.25)),
                                                 embeddings_initializer=RandomNormal(
            mean=0.0, stddev=init_std, seed=seed),
            embeddings_regularizer=l2(l2_reg),
            name=prefix+'_emb_' + str(i) + '-' + feat.name) for i, feat in
            enumerate(feature_dim_dict["sparse"])}
    else:

        sparse_embedding = {feat.name: Embedding(feat.dimension, embedding_size,
                                                 embeddings_initializer=RandomNormal(
                                                     mean=0.0, stddev=init_std, seed=seed),
                                                 embeddings_regularizer=l2(
                                                     l2_reg),
                                                 name=prefix+'_emb_' + str(i) + '-' + feat.name) for i, feat in
                            enumerate(feature_dim_dict["sparse"])}

    if 'sequence' in feature_dim_dict:
        count = len(sparse_embedding)
        sequence_dim_list = feature_dim_dict['sequence']
        for feat in sequence_dim_list:
            # if feat.name not in sparse_embedding:
            if embedding_size == "auto":
                sparse_embedding[feat.name] = Embedding(feat.dimension, 6 * int(pow(feat.dimension, 0.25)),
                                                        embeddings_initializer=RandomNormal(
                                                            mean=0.0, stddev=init_std, seed=seed),
                                                        embeddings_regularizer=l2(
                                                            l2_reg),
                                                        name=prefix + '_emb_' + str(count) + '-' + feat.name, mask_zero=seq_mask_zero)

            else:
                sparse_embedding[feat.name] = Embedding(feat.dimension, embedding_size,
                                                        embeddings_initializer=RandomNormal(
                                                            mean=0.0, stddev=init_std, seed=seed),
                                                        embeddings_regularizer=l2(
                                                            l2_reg),
                                                        name=prefix+'_emb_' + str(count) + '-' + feat.name, mask_zero=seq_mask_zero)

            count += 1

    return sparse_embedding