tf.estimator.WarmStartSettings

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Settings for warm-starting in tf.estimator.Estimators.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.estimator.WarmStartSettings, `tf.compat.v2.estimator.WarmStartSettings`

tf.estimator.WarmStartSettings(
    ckpt_to_initialize_from, vars_to_warm_start='.*', var_name_to_vocab_info=None,
    var_name_to_prev_var_name=None
)

Example Use with canned tf.estimator.DNNEstimator:

emb_vocab_file = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_file(
        "sc_vocab_file", "new_vocab.txt", vocab_size=100),
    dimension=8)
emb_vocab_list = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_list(
        "sc_vocab_list", vocabulary_list=["a", "b"]),
    dimension=8)
estimator = tf.estimator.DNNClassifier(
  hidden_units=[128, 64], feature_columns=[emb_vocab_file, emb_vocab_list],
  warm_start_from=ws)

where ws could be defined as:

Warm-start all weights in the model (input layer and hidden weights). Either the directory or a specific checkpoint can be provided (in the case of the former, the latest checkpoint will be used):

ws = WarmStartSettings(ckpt_to_initialize_from="/tmp")
ws = WarmStartSettings(ckpt_to_initialize_from="/tmp/model-1000")

Warm-start only the embeddings (input layer):

ws = WarmStartSettings(ckpt_to_initialize_from="/tmp",
                       vars_to_warm_start=".*input_layer.*")

Warm-start all weights but the embedding parameters corresponding to sc_vocab_file have a different vocab from the one used in the current model:

vocab_info = tf.estimator.VocabInfo(
    new_vocab=sc_vocab_file.vocabulary_file,
    new_vocab_size=sc_vocab_file.vocabulary_size,
    num_oov_buckets=sc_vocab_file.num_oov_buckets,
    old_vocab="old_vocab.txt"
)
ws = WarmStartSettings(
    ckpt_to_initialize_from="/tmp",
    var_name_to_vocab_info={
        "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
    })

Warm-start only sc_vocab_file embeddings (and no other variables), which have a different vocab from the one used in the current model:

vocab_info = tf.estimator.VocabInfo(
    new_vocab=sc_vocab_file.vocabulary_file,
    new_vocab_size=sc_vocab_file.vocabulary_size,
    num_oov_buckets=sc_vocab_file.num_oov_buckets,
    old_vocab="old_vocab.txt"
)
ws = WarmStartSettings(
    ckpt_to_initialize_from="/tmp",
    vars_to_warm_start=None,
    var_name_to_vocab_info={
        "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
    })

Warm-start all weights but the parameters corresponding to sc_vocab_file have a different vocab from the one used in current checkpoint, and only 100 of those entries were used:

vocab_info = tf.estimator.VocabInfo(
    new_vocab=sc_vocab_file.vocabulary_file,
    new_vocab_size=sc_vocab_file.vocabulary_size,
    num_oov_buckets=sc_vocab_file.num_oov_buckets,
    old_vocab="old_vocab.txt",
    old_vocab_size=100
)
ws = WarmStartSettings(
    ckpt_to_initialize_from="/tmp",
    var_name_to_vocab_info={
        "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
    })

Warm-start all weights but the parameters corresponding to sc_vocab_file have a different vocab from the one used in current checkpoint and the parameters corresponding to sc_vocab_list have a different name from the current checkpoint:

vocab_info = tf.estimator.VocabInfo(
    new_vocab=sc_vocab_file.vocabulary_file,
    new_vocab_size=sc_vocab_file.vocabulary_size,
    num_oov_buckets=sc_vocab_file.num_oov_buckets,
    old_vocab="old_vocab.txt",
    old_vocab_size=100
)
ws = WarmStartSettings(
    ckpt_to_initialize_from="/tmp",
    var_name_to_vocab_info={
        "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
    },
    var_name_to_prev_var_name={
        "input_layer/sc_vocab_list_embedding/embedding_weights":
            "old_tensor_name"
    })

Warm-start all TRAINABLE variables:

ws = WarmStartSettings(ckpt_to_initialize_from="/tmp",
                       vars_to_warm_start=".*")

Warm-start all variables (including non-TRAINABLE):

ws = WarmStartSettings(ckpt_to_initialize_from="/tmp",
                       vars_to_warm_start=[".*"])

Warm-start non-TRAINABLE variables "v1", "v1/Momentum", and "v2" but not "v2/momentum":

ws = WarmStartSettings(ckpt_to_initialize_from="/tmp",
                       vars_to_warm_start=["v1", "v2[^/]"])

Attributes
ckpt_to_initialize_from	[Required] A string specifying the directory with checkpoint file(s) or path to checkpoint from which to warm-start the model parameters.
vars_to_warm_start	[Optional] One of the following: A regular expression (string) that captures which variables to warm-start (see tf.compat.v1.get_collection). This expression will only consider variables in the TRAINABLE_VARIABLES collection -- if you need to warm-start non_TRAINABLE vars (such as optimizer accumulators or batch norm statistics), please use the below option. A list of strings, each a regex scope provided to tf.compat.v1.get_collection with GLOBAL_VARIABLES (please see tf.compat.v1.get_collection). For backwards compatibility reasons, this is separate from the single-string argument type. A list of Variables to warm-start. If you do not have access to the Variable objects at the call site, please use the above option. None, in which case only TRAINABLE variables specified in var_name_to_vocab_info will be warm-started. Defaults to '.*', which warm-starts all variables in the TRAINABLE_VARIABLES collection. Note that this excludes variables such as accumulators and moving statistics from batch norm.
var_name_to_vocab_info	[Optional] Dict of variable names (strings) to tf.estimator.VocabInfo. The variable names should be "full" variables, not the names of the partitions. If not explicitly provided, the variable is assumed to have no (changes to) vocabulary.
var_name_to_prev_var_name	[Optional] Dict of variable names (strings) to name of the previously-trained variable in ckpt_to_initialize_from. If not explicitly provided, the name of the variable is assumed to be same between previous checkpoint and current model. Note that this has no effect on the set of variables that is warm-started, and only controls name mapping (use vars_to_warm_start for controlling what variables to warm-start).