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Caffe2 - Python API
A deep learning, cross platform ML framework
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Caffe2 - Python API
A deep learning, cross platform ML framework
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Public Member Functions | |
| def | __init__ (self, name, input_feature_schema, trainer_extra_schema) |
| def | add_metric_field (self, name, value) |
| def | add_global_constant (self, name, array=None, dtype=None, initializer=None) |
| def | create_init_net (self, name) |
| def | next_layer_name (self, prefix) |
| def | add_layer (self, layer) |
| def | get_parameter_blobs (self) |
| def | default_optimizer (self) |
| def | default_optimizer (self, optimizer) |
| def | input_feature_schema (self) |
| def | trainer_extra_schema (self) |
| def | metrics_schema (self) |
| def | output_schema (self) |
| def | output_schema (self, schema) |
| def | loss (self) |
| def | loss (self, loss) |
| def | __getattr__ (self, layer) |
| def | layers (self) |
| def | apply_optimizers (self, train_net, train_init_net, grad_map) |
| def | NoOptim (self, args, kwargs) |
 Public Member Functions inherited from model_helper.ModelHelperBase | |
| def | __init__ (self, name=None, init_params=True, allow_not_known_ops=True, skip_sparse_optim=False, param_model=None) |
| def | get_name (self) |
| def | add_param (self, param, key=None, shape=None, length=None) |
| def | param_info (self, grad_type=None, id=None) |
| def | GetParams (self, namescope=None, top_scope=False) |
| def | Proto (self) |
| def | InitProto (self) |
| def | RunAllOnGPU (self, args, kwargs) |
| def | CreateDB (self, blob_out, db, db_type, kwargs) |
| def | AddGradientOperators (self, args, kwargs) |
| def | get_param_to_grad (self, params) |
| def | GetOptimizationPairs (self, params=None) |
| def | GetComputedParams (self, namescope=None) |
| def | GetAllParams (self, namescope=None) |
| def | TensorProtosDBInput (self, unused_blob_in, blob_out, batch_size, db, db_type, kwargs) |
| def | AddOperator (self, op_type, inputs, parameters, args, kwargs) |
| def | GetDevices (self) |
| def | __getattr__ (self, op_type) |
Public Attributes | |
| param_to_optim | |
| param_init_net | |
| global_constants | |
| global_constant_initializers | |
| Public Attributes inherited from model_helper.ModelHelperBase | |
| name | |
| net | |
| param_init_net | |
| param_to_grad | |
| params | |
| computed_params | |
| gradient_ops_added | |
| init_params | |
| allow_not_known_ops | |
| skip_sparse_optim | |
| weights | |
| biases | |
| grad_map | |
Model helper for building models on top of layers abstractions. Each layer is the abstraction that is higher level than Operator. Layer is responsible for ownership of it's own parameters and can easily be instantiated in multiple nets possible with different sets of ops. As an example: one can easily instantiate predict and train nets from the same set of layers, where predict net will have subset of the operators from train net.
Definition at line 16 of file layer_model_helper.py.
| def layer_model_helper.LayerModelHelper.metrics_schema | ( | self | ) |
Returns the schema that represents model output that should be used for metric reporting. During the training/evaluation this schema will be appended to the schema that represents model output.
Definition at line 167 of file layer_model_helper.py.
1.8.14