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def | __init__ (self, model, input_record, input_specs, name="feature_sparse_to_dense", kwargs) |
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def | add_ops (self, net) |
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def | get_metadata (self) |
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def | __init__ (self, model, prefix, input_record, predict_input_record_fields=None, tags=None, kwargs) |
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def | get_type (self) |
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def | predict_input_record (self) |
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def | input_record (self) |
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def | predict_output_schema (self) |
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def | predict_output_schema (self, output_schema) |
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def | output_schema (self) |
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def | output_schema (self, output_schema) |
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def | get_parameters (self) |
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def | get_fp16_compatible_parameters (self) |
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def | get_memory_usage (self) |
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def | add_init_params (self, init_net) |
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def | create_param (self, param_name, shape, initializer, optimizer, ps_param=None, regularizer=None) |
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def | get_next_blob_reference (self, name) |
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def | add_operators (self, net, init_net=None, context=InstantiationContext.TRAINING) |
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def | add_ops (self, net) |
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def | add_eval_ops (self, net) |
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def | add_train_ops (self, net) |
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def | add_ops_to_accumulate_pred (self, net) |
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def | add_param_copy_operators (self, net) |
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def | export_output_for_metrics (self) |
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def | export_params_for_metrics (self) |
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| input_specs |
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| output_schema |
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| zero |
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| zero_range |
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| name |
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| model |
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| kwargs |
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| request_only |
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| precomputation_request_only |
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| precomputation_object_only |
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| eval_output_schema |
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| tags |
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| params |
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Definition at line 10 of file feature_sparse_to_dense.py.
def caffe2.python.layers.feature_sparse_to_dense.FeatureSparseToDense.__init__ |
( |
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self, |
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model, |
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input_record, |
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input_specs, |
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name = "feature_sparse_to_dense" , |
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kwargs |
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) |
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`input_specs` follows the format of FeatureSpec from schema. To be more
precise it's a namedtuple that should have:
'feature_type', 'feature_names', 'feature_ids'
Definition at line 13 of file feature_sparse_to_dense.py.
The documentation for this class was generated from the following file: