AI::MXNet::Symbol - Symbolic interface of MXNet.
Get name string from the symbol, this function only works for non-grouped symbol. Returns ------- value : str The name of this symbol, returns None for grouped symbol.
Get an attribute string from the symbol, this function only works for non-grouped symbol. Parameters ---------- key : str The key to get attribute from. Returns ------- value : str The attribute value of the key, returns None if attribute do not exist.
Get all attributes from the symbol. Returns ------- ret : hash ref of str to str a dicitonary mapping attribute keys to values
Recursively get all attributes from the symbol and its childrens Returns ------- ret : hash ref of str to hash ref. Returns a dict whose keys are names of the symbol and its children. Values of the returned dict are dictionaries that map attribute keys to values.
Get a new grouped symbol whose output contains all the internal outputs of this symbol. Returns ------- sgroup : AI::MXNet::Symbol The internal symbol of the symbol.
Get a new grouped symbol whose output contains inputs to output nodes of the original symbol Returns ------- sgroup : Symbol or undef The children of the head node. If the symbol has no inputs undef will be returned.
List all the arguments in the symbol. Returns ------- args : array ref of strings
List all outputs in the symbol. Returns ------- $out : array ref of strings.
List all auxiliary states in the symbol. Returns ------- aux_states : array ref of string List the names of the auxiliary states. Notes ----- Auxiliary states are special states of symbols that do not corresponds to an argument, and do not have gradient. But still be useful for the specific operations. A common example of auxiliary state is the moving_mean and moving_variance in BatchNorm. Most operators do not have Auxiliary states.
Lists all arguments and auxiliary states of this Symbol. Returns ------- inputs : array ref of str List of all inputs. Examples -------- >>> my $bn = mx->sym->BatchNorm(name=>'bn');
Infer the type of outputs and arguments of given known types of arguments. User can either pass in the known types in positional way or keyword argument way. Tuple of Nones is returned if there is not enough information passed in. An error will be raised if there is inconsistency found in the known types passed in. Parameters ---------- args : Array Provide type of arguments in a positional way. Unknown type can be marked as None kwargs : Hash ref, must ne ssupplied as as sole argument to the method. Provide keyword arguments of known types. Returns ------- arg_types : array ref of Dtype or undef List of types of arguments. The order is in the same order as list_arguments() out_types : array ref of Dtype or undef List of types of outputs. The order is in the same order as list_outputs() aux_types : array ref of Dtype or undef List of types of outputs. The order is in the same order as list_auxiliary()
Infer the shape of outputs and arguments of given known shapes of arguments. User can either pass in the known shapes in positional way or keyword argument way. Tuple of Nones is returned if there is not enough information passed in. An error will be raised if there is inconsistency found in the known shapes passed in. Parameters ---------- *args : Provide shape of arguments in a positional way. Unknown shape can be marked as undef **kwargs : Provide keyword arguments of known shapes. Returns ------- arg_shapes : array ref of Shape or undef List of shapes of arguments. The order is in the same order as list_arguments() out_shapes : array ref of Shape or undef List of shapes of outputs. The order is in the same order as list_outputs() aux_shapes : array ref of Shape or undef List of shapes of outputs. The order is in the same order as list_auxiliary()
Partially infer the shape. The same as infer_shape, except that the partial results can be returned.
The debug string. Returns ------- debug_str : string Debug string of the symbol.
Save the symbol into a file. You can also use Storable to do the job if you only work with Perl. The advantage of load/save is the file is language agnostic. This means the file saved using save can be loaded by other language binding of mxnet. You also get the benefit being able to directly load/save from cloud storage(S3, HDFS) Parameters ---------- fname : str The name of the file - s3://my-bucket/path/my-s3-symbol - hdfs://my-bucket/path/my-hdfs-symbol - /path-to/my-local-symbol See Also -------- load : Used to load symbol from file.
Save the symbol into a JSON string. See Also -------- load_json : Used to load symbol from JSON string.
Bind current symbol to get an executor, allocate all the ndarrays needed. Allows specifying data types. This function will ask user to pass in ndarray of position they like to bind to, and it will automatically allocate the ndarray for arguments and auxiliary states that user did not specify explicitly. Parameters ---------- :$ctx : AI::MXNet::Context The device context the generated executor to run on. :$grad_req: string {'write', 'add', 'null'}, or list of str or dict of str to str, optional Specifies how we should update the gradient to the args_grad. - 'write' means everytime gradient is write to specified args_grad NDArray. - 'add' means everytime gradient is add to the specified NDArray. - 'null' means no action is taken, the gradient may not be calculated. :$type_dict : hash ref of str->Dtype Input type map, name->dtype :$group2ctx : hash ref of string to AI::MXNet::Context The mapping of the ctx_group attribute to the context assignment. :$shapes : hash ref of str->Shape Input shape map, name->shape :$shared_arg_names : Maybe[ArrayRef[Str]] The argument names whose 'NDArray' of shared_exec can be reused for initializing the current executor. :$shared_exec : Maybe[AI::MXNet::Executor] The executor whose arg_arrays, arg_arrays, grad_arrays, and aux_arrays can be reused for initializing the current executor. :$shared_buffer : Maybe[HashRef[AI::MXNet::NDArray]] The dict mapping argument names to the `NDArray` that can be reused for initializing the current executor. This buffer will be checked for reuse if one argument name of the current executor is not found in `shared_arg_names`. Returns ------- $executor : AI::MXNet::Executor The generated Executor
Bind current symbol to get an executor. Parameters ---------- :$ctx : AI::MXNet::Context The device context the generated executor to run on. :$args : HashRef[AI::MXNet::NDArray]|ArrayRef[AI::MXNet::NDArray] Input arguments to the symbol. - If type is array ref of NDArray, the position is in the same order of list_arguments. - If type is hash ref of str to NDArray, then it maps the name of arguments to the corresponding NDArray. - In either case, all the arguments must be provided. :$args_grad : Maybe[HashRef[AI::MXNet::NDArray]|ArrayRef[AI::MXNet::NDArray]] When specified, args_grad provide NDArrays to hold the result of gradient value in backward. - If type is array ref of NDArray, the position is in the same order of list_arguments. - If type is hash ref of str to NDArray, then it maps the name of arguments to the corresponding NDArray. - When the type is hash ref of str to NDArray, users only need to provide the dict for needed argument gradient. Only the specified argument gradient will be calculated. :$grad_req : {'write', 'add', 'null'}, or array ref of str or hash ref of str to str, optional Specifies how we should update the gradient to the args_grad. - 'write' means everytime gradient is write to specified args_grad NDArray. - 'add' means everytime gradient is add to the specified NDArray. - 'null' means no action is taken, the gradient may not be calculated. :$aux_states : array ref of NDArray, or hash ref of str to NDArray, optional Input auxiliary states to the symbol, only need to specify when list_auxiliary_states is not empty. - If type is array ref of NDArray, the position is in the same order of list_auxiliary_states - If type is hash ref of str to NDArray, then it maps the name of auxiliary_states to the corresponding NDArray, - In either case, all the auxiliary_states need to be provided. :$group2ctx : hash ref of string to AI::MXNet::Context The mapping of the ctx_group attribute to the context assignment. :$shared_exec : AI::MXNet::Executor Executor to share memory with. This is intended for runtime reshaping, variable length sequences, etc. The returned executor shares state with shared_exec, and should not be used in parallel with it. Returns ------- $executor : AI::MXNet::Executor The generated Executor Notes ----- Auxiliary states are special states of symbols that do not corresponds to an argument, and do not have gradient. But still be useful for the specific operations. A common example of auxiliary state is the moving_mean and moving_variance in BatchNorm. Most operators do not have auxiliary states and this parameter can be safely ignored. User can give up gradient by using a hash ref in args_grad and only specify the gradient they're interested in.
Evaluate a symbol given arguments The `eval` method combines a call to `bind` (which returns an executor) with a call to `forward` (executor method). For the common use case, where you might repeatedly evaluate with same arguments, eval is slow. In that case, you should call `bind` once and then repeatedly call forward. Eval allows simpler syntax for less cumbersome introspection. Parameters ---------- :$ctx : Context The device context the generated executor to run on. Optional, defaults to cpu(0) :$args array ref of NDArray or hash ref of NDArray - If the type is an array ref of NDArray, the position is in the same order of list_arguments. - If the type is a hash of str to NDArray, then it maps the name of the argument to the corresponding NDArray. - In either case, all arguments must be provided. Returns ---------- result : an array ref of NDArrays corresponding to the values taken by each symbol when evaluated on given args. When called on a single symbol (not a group), the result will be an array ref with one element. Examples: my $result = $symbol->eval(ctx => mx->gpu, args => {data => mx->nd->ones([5,5])}); my $result = $symbol->eval(args => {data => mx->nd->ones([5,5])});
Get the autodiff of current symbol. This function can only be used if current symbol is a loss function. Parameters ---------- $wrt : Array of String keyword arguments of the symbol that the gradients are taken. Returns ------- grad : AI::MXNet::Symbol A gradient Symbol with returns to be the corresponding gradients.
Create a symbolic variable with specified name. Parameters ---------- name : str Name of the variable. attr : hash ref of string -> string Additional attributes to set on the variable. shape : array ref of positive integers Optionally, one can specify the shape of a variable. This will be used during shape inference. If user specified a different shape for this variable using keyword argument when calling shape inference, this shape information will be ignored. lr_mult : float Specify learning rate muliplier for this variable. wd_mult : float Specify weight decay muliplier for this variable. dtype : Dtype Similar to shape, we can specify dtype for this variable. init : initializer (mx->init->*) Specify initializer for this variable to override the default initializer kwargs : hash ref other additional attribute variables Returns ------- variable : Symbol The created variable symbol.
A synonym to Variable.
Create a symbol that groups symbols together. Parameters ---------- symbols : array ref List of symbols to be grouped. Returns ------- sym : Symbol The created group symbol.
Load symbol from a JSON file. You can also use Storable to do the job if you only work with Perl. The advantage of load/save is the file is language agnostic. This means the file saved using save can be loaded by other language binding of mxnet. You also get the benefit being able to directly load/save from cloud storage(S3, HDFS) Parameters ---------- fname : str The name of the file, examples: - `s3://my-bucket/path/my-s3-symbol` - `hdfs://my-bucket/path/my-hdfs-symbol` - `/path-to/my-local-symbol` Returns ------- sym : Symbol The loaded symbol. See Also -------- AI::MXNet::Symbol->save : Used to save symbol into file.
Parameters ---------- json_str : str A json string. Returns ------- sym : Symbol The loaded symbol. See Also -------- AI::MXNet::Symbol->tojson : Used to save symbol into json string.
Simlar function in the MXNet ndarray as numpy.arange See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html. Parameters ---------- start : number Start of interval. The interval includes this value. The default start value is 0. stop : number, optional End of interval. The interval does not include this value. step : number, optional Spacing between values repeat : int, optional "The repeating time of all elements. E.g repeat=3, the element a will be repeated three times --> a, a, a. dtype : type, optional The value type of the NDArray, default to np.float32 Returns ------- out : Symbol The created Symbol
To install AI::MXNet, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::MXNet
CPAN shell
perl -MCPAN -e shell install AI::MXNet
For more information on module installation, please visit the detailed CPAN module installation guide.