save or load model

This section will describe two ways of saving network information in detail.

pre-defined function in Network

Use pre-defined functions save_state and state_from_dict of spaic.Network to save or load the weight of the model directly.

The optional parameters are filename , direct and save. If users use save_state without giving any parameters, the function will use default name autoname with random number as the direct name and save the weight into the './autoname/parameters/_parameters_dict.pt' . If given filename, or direct , it will save the weight into 'direct/filename/parameters/_parameters_dict.pt' . Parameter save is default as True , which means it will save the weight. If users choose False , this function will return the parameter_dict of the model directly.

The parameters of state_from_dict is same as save_state but have two more parameters: state and direct ,and save parameters is unneeded. If users provide state , this function will use given parameters to replace the parameter dict of the backend. If state is None, this function will decide the saving path according to filename and direct. The device will decide where to storage the parameters.

Net.save_state('Test1', True)
...
Net.state_from_dict(filename='Test1', device=device)

network_save and network_load

The network save module spaic.Network_saver.network_save and spaic.Network_loader.network_load in spaic.Library will save the whole network structure of the model and the weight information separately. This method requires a filename filename when used, and then the platform will create a new file ./filename/filename.json in the running directory of the current program to save the network structure. At the same time, when using network_save , users also can choose the save format between json or yaml .

network_dir = network_save(Net=Net, filename='TestNet',
                                        trans_format='json', combine=False, save=True)

# network_dir = 'TestNet'
Net2 = network_load(network_dir, device=device)

In network_save :

Net – the specific network object in SPAIC
filename – filename, network_save will save the Net with this name
path – file storage path, a new folder will be created based on the filename if target path doesn’t have such folder
trans_format – save format, can choose json or yaml , default as json
combine – this parameters decides whether save the weight and network structure in one file, default as False
save – this parameters decides whether save the structure locally, if choose True , this function will save locally and return the file name. If choose False , it will only return the structure as a dict.
save_weight – this parameters decides whether save the backend information and weights of the model

Then, I will give some example to explain the meaning of saved file:

# information about Nodes
-   input:
        _class_label: <nod> # Indicate this object is node
        _dt: 0.1 # Length of every time step
        _time: null #
        coding_method: poisson # Encode method
        coding_var_name: O # Output target of this node
        dec_target: null # Decode target of this node, since this is input node, it doesn't have decode target
        name: input # name of this node
        num: 784 # element number of this node
        shape: # shape
        - 784

# information about NeuronGroups
-   layer1:
        _class_label: <neg> # Indicate this object is NeuronGroup
        id: autoname1<net>_layer1<neg> # ID of this NeuronGroup, it is NeuronGroup 'layer1' of the network 'autoname1'
        model_name: clif # neuron model of this NeuronGroup, it's CLIF
        name: layer1 # name of this NeuronGroup
        num: 10 # neuron number of this NeuronGroup
        parameters: {} # parameters of kwargs, like some parameters of neuron model
        shape: # shape
        - 10
        type: null # type of this NeuronGroup, it is just like a label for Projection

-   layer3:
    -   layer1:
            _class_label: <neg> # Indicate this object is NeuronGroup
            id: autoname1<net>_layer3<asb>_layer1<neg>  # ID of this NeuronGroup，it is NeuronGroup 'layer1' of the Assembly 'layer3' of the network 'autoname1'
            model_name: clif # neuron model of this NeuronGroup, it's CLIF
            name: layer1 # name of this NeuronGroup
            num: 10 # neuron number of this NeuronGroup
            parameters: {} # parameters of kwargs, like some parameters of neuron model
            shape: # shape
            - 10
            type: null # type of this NeuronGroup, it is just like a label for Projection

    -   connection0:
            _class_label: <con> # Indicate this object is Connection
            link_type: full # link type of this Connection, it is full connection
            max_delay: 0 # the maximum delay step of this Connection
            name: connection0 # name of this Connection
            parameters: {} # parameters of kwargs, like some parameters of convolution connection
            post: layer3   # postsynaptic neuron, here is point to Assembly layer3
            post_var_name: Isyn   # the output of this synapse, here is 'Isyn', a default value
            pre: layer2    # presynaptic neuron, here is point to layer2
            pre_var_name: O         # input of this synapse, here is 'O', a default value
            sparse_with_mask: false # whether use mask, details will be explained in chapter 'Basic Structure.Connection'
            weight: # weight matrix
                autoname1<net>_layer3<asb>_connection0<con>:autoname1<net>_layer3<asb>_layer3<neg><-autoname1<net>_layer3<asb>_layer2<neg>:{weight}: # here is the ID of this weight
                -   - 0.05063159018754959

# information about Connections
-   connection1:
        _class_label: <con> # Indicate this object is Connection
        link_type: full # link type of this Connection, it is full connection
        max_delay: 0 # the maximum delay step of this Connection
        name: connection1 # name of this Connection
        parameters:  # parameters of kwargs, like some parameters of convolution connection, here is the parameter for randomly initializing the weight
            w_mean: 0.02
            w_std: 0.05
        post: layer1   # postsynaptic neuron, here is point to layer1
        post_var_name: Isyn   # the output of this synapse, here is 'Isyn', a default value
        pre: input     # presynaptic neuron, here is point to input node
        pre_var_name: O         # input of this synapse, here is 'O', a default value
        sparse_with_mask: false # whether use mask, details will be explained in chapter 'Basic Structure.Connection'
        weight: # weight matrix
            autoname1<net>_connection1<con>:autoname1<net>_layer1<neg><-autoname1<net>_input<nod>:{weight}:
            -   - 0.05063159018754959
                ......

# information about Learners
-   learner2:
        _class_label: <learner> # Indicate this object is Learner
        algorithm: full_online_STDP # the algorithms of this Learner, here is full_online_STDP
        lr_schedule_name: null # the learning rate scheduler of this Learner, here is unused
        name: _learner2 # name of this Learner
        optim_name: null # the optimizer of this Learner, here is unused
        parameters: {} # parameters of kwargs
        trainable: # the training target of this Learner
        - connection1
        - connection2