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 File: feature_column.py    From DeepCTR with 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 File: input_embedding.py    From icme2019 with 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