From 3d85fcd5fde5f82eeb8216ecb008372c1db4e705 Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Thu, 25 May 2023 15:00:20 +0200 Subject: [PATCH 01/24] new model added --- bindsnet/analysis/plotting.py | 227 +++++++++++++++++++ bindsnet/learning/__init__.py | 2 + bindsnet/learning/learning.py | 89 ++++++++ bindsnet/models/__init__.py | 2 + bindsnet/models/models.py | 120 +++++++++- bindsnet/network/nodes.py | 33 ++- examples/mnist/net_24_5.pt | Bin 0 -> 636751 bytes examples/mnist/plot_mnist.py | 103 +++++++++ examples/mnist/salah_example.py | 384 ++++++++++++++++++++++++++++++++ network.pt | Bin 0 -> 636751 bytes 10 files changed, 950 insertions(+), 10 deletions(-) create mode 100644 examples/mnist/net_24_5.pt create mode 100644 examples/mnist/plot_mnist.py create mode 100644 examples/mnist/salah_example.py create mode 100644 network.pt diff --git a/bindsnet/analysis/plotting.py b/bindsnet/analysis/plotting.py index 89007b08..d5e1d66c 100644 --- a/bindsnet/analysis/plotting.py +++ b/bindsnet/analysis/plotting.py @@ -842,3 +842,230 @@ def plot_voltages( plt.tight_layout() return ims, axes + +# I added this plot_traces which is completely based on voltage_plot, just changed the word voltage +def plot_traces( + traces: Dict[str, torch.Tensor], + ims: Optional[List[AxesImage]] = None, + axes: Optional[List[Axes]] = None, + time: Tuple[int, int] = None, + n_neurons: Optional[Dict[str, Tuple[int, int]]] = None, + cmap: Optional[str] = "jet", + plot_type: str = "color", + thresholds: Dict[str, torch.Tensor] = None, + figsize: Tuple[float, float] = (8.0, 4.5), +) -> Tuple[List[AxesImage], List[Axes]]: + # language=rst + """ + Plot traces for any group(s) of neurons. + + :param traces: Contains trace data by neuron layers. + :param ims: Used for re-drawing the plots. + :param axes: Used for re-drawing the plots. + :param time: Plot traces of neurons in given time range. Default is entire + simulation time. + :param n_neurons: Plot traces of neurons in given range of neurons. Default is all + neurons. + :param cmap: Matplotlib colormap to use. + :param figsize: Horizontal, vertical figure size in inches. + :param plot_type: The way how to draw graph. 'color' for pcolormesh, 'line' for + curved lines. + :param thresholds: Thresholds of the neurons in each layer. + :return: ``ims, axes``: Used for re-drawing the plots. + """ + n_subplots = len(traces.keys()) + + # for key in traces.keys(): + # traces[key] = traces[key].view(-1, traces[key].size(-1)) + traces = {k: v.view(v.size(0), -1) for (k, v) in traces.items()} + + if time is None: + for key in traces.keys(): + time = (0, traces[key].size(0)) + break + + if n_neurons is None: + n_neurons = {} + + for key, val in traces.items(): + if key not in n_neurons.keys(): + n_neurons[key] = (0, val.size(1)) + + if not ims: + fig, axes = plt.subplots(n_subplots, 1, figsize=figsize) + ims = [] + if n_subplots == 1: # Plotting only one image + for v in traces.items(): + if plot_type == "line": + ims.append( + axes.plot( + v[1] + .detach() + .clone() + .cpu() + .numpy()[ + time[0] : time[1], + n_neurons[v[0]][0] : n_neurons[v[0]][1], + ] + ) + ) + + if thresholds is not None and thresholds[v[0]].size() == torch.Size( + [] + ): + ims.append( + axes.axhline( + y=thresholds[v[0]].item(), c="r", linestyle="--" + ) + ) + else: + ims.append( + axes.pcolormesh( + v[1] + .cpu() + .numpy()[ + time[0] : time[1], + n_neurons[v[0]][0] : n_neurons[v[0]][1], + ] + .T, + cmap=cmap, + ) + ) + + args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1]) + plt.title("%s traces for neurons (%d - %d) from t = %d to %d " % args) + plt.xlabel("Time (ms)") + + if plot_type == "line": + plt.ylabel("trace") + else: + plt.ylabel("Neuron index") + + axes.set_aspect("auto") + + else: # Plot each layer at a time + for i, v in enumerate(traces.items()): + if plot_type == "line": + ims.append( + axes[i].plot( + v[1] + .cpu() + .numpy()[ + time[0] : time[1], + n_neurons[v[0]][0] : n_neurons[v[0]][1], + ] + ) + ) + if thresholds is not None and thresholds[v[0]].size() == torch.Size( + [] + ): + ims.append( + axes[i].axhline( + y=thresholds[v[0]].item(), c="r", linestyle="--" + ) + ) + else: + ims.append( + axes[i].matshow( + v[1] + .cpu() + .numpy()[ + time[0] : time[1], + n_neurons[v[0]][0] : n_neurons[v[0]][1], + ] + .T, + cmap=cmap, + ) + ) + args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1]) + axes[i].set_title( + "%s traces for neurons (%d - %d) from t = %d to %d " % args + ) + + for ax in axes: + ax.set_aspect("auto") + + if plot_type == "color": + plt.setp(axes, xlabel="Simulation time", ylabel="Neuron index") + elif plot_type == "line": + plt.setp(axes, xlabel="Simulation time", ylabel="trace") + + plt.tight_layout() + + else: + # Plotting figure given + if n_subplots == 1: # Plotting only one image + for v in traces.items(): + axes.clear() + if plot_type == "line": + axes.plot( + v[1] + .cpu() + .numpy()[ + time[0] : time[1], n_neurons[v[0]][0] : n_neurons[v[0]][1] + ] + ) + if thresholds is not None and thresholds[v[0]].size() == torch.Size( + [] + ): + axes.axhline(y=thresholds[v[0]].item(), c="r", linestyle="--") + else: + axes.matshow( + v[1] + .cpu() + .numpy()[ + time[0] : time[1], n_neurons[v[0]][0] : n_neurons[v[0]][1] + ] + .T, + cmap=cmap, + ) + args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1]) + axes.set_title( + "%s traces for neurons (%d - %d) from t = %d to %d " % args + ) + axes.set_aspect("auto") + + else: + # Plot each layer at a time + for i, v in enumerate(traces.items()): + axes[i].clear() + if plot_type == "line": + axes[i].plot( + v[1] + .cpu() + .numpy()[ + time[0] : time[1], n_neurons[v[0]][0] : n_neurons[v[0]][1] + ] + ) + if thresholds is not None and thresholds[v[0]].size() == torch.Size( + [] + ): + axes[i].axhline( + y=thresholds[v[0]].item(), c="r", linestyle="--" + ) + else: + axes[i].matshow( + v[1] + .cpu() + .numpy()[ + time[0] : time[1], n_neurons[v[0]][0] : n_neurons[v[0]][1] + ] + .T, + cmap=cmap, + ) + args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1]) + axes[i].set_title( + "%s traces for neurons (%d - %d) from t = %d to %d " % args + ) + + for ax in axes: + ax.set_aspect("auto") + + if plot_type == "color": + plt.setp(axes, xlabel="Simulation time", ylabel="Neuron index") + elif plot_type == "line": + plt.setp(axes, xlabel="Simulation time", ylabel="trace") + + plt.tight_layout() + + return ims, axes diff --git a/bindsnet/learning/__init__.py b/bindsnet/learning/__init__.py index 5a733783..25bb57aa 100644 --- a/bindsnet/learning/__init__.py +++ b/bindsnet/learning/__init__.py @@ -5,6 +5,7 @@ LearningRule, NoOp, PostPre, + Modified_PostPre, Rmax, WeightDependentPostPre, ) @@ -13,6 +14,7 @@ "LearningRule", "NoOp", "PostPre", + "Modified_PostPre", "WeightDependentPostPre", "Hebbian", "MSTDP", diff --git a/bindsnet/learning/learning.py b/bindsnet/learning/learning.py index e2c171cd..69913705 100644 --- a/bindsnet/learning/learning.py +++ b/bindsnet/learning/learning.py @@ -549,6 +549,95 @@ def _conv3d_connection_update(self, **kwargs) -> None: super().update() +#=================================================== +class Modified_PostPre(LearningRule): + # language=rst + """ + Simple STDP rule involving both pre- and post-synaptic spiking activity. By default, + pre-synaptic update is negative and the post-synaptic update is positive. + """ + + def __init__( + self, + connection: AbstractConnection, + nu: Optional[Union[float, Sequence[float], Sequence[torch.Tensor]]] = None, + reduction: Optional[callable] = None, + weight_decay: float = 0.0, + **kwargs, + ) -> None: + # language=rst + """ + Constructor for ``PostPre`` learning rule. + + :param connection: An ``AbstractConnection`` object whose weights the + ``PostPre`` learning rule will modify. + :param nu: Single or pair of learning rates for pre- and post-synaptic events. It also + accepts a pair of tensors to individualize learning rates of each neuron. + In this case, their shape should be the same size as the connection weights. + :param reduction: Method for reducing parameter updates along the batch + dimension. + :param weight_decay: Coefficient controlling rate of decay of the weights each iteration. + """ + super().__init__( + connection=connection, + nu=nu, + reduction=reduction, + weight_decay=weight_decay, + **kwargs, + ) + + assert ( + self.source.traces and self.target.traces + ), "Both pre- and post-synaptic nodes must record spike traces." + + if isinstance(connection, (Connection, LocalConnection)): + self.update = self._connection_update # rewrites the update rule defined in the base class + else: + raise NotImplementedError( + "This learning rule is not supported for this Connection type." + ) + + def _connection_update(self, **kwargs) -> None: + # language=rst + """ + Post-pre learning rule for ``Connection`` subclass of ``AbstractConnection`` + class. + """ + batch_size = self.source.batch_size + + """ + # Pre-synaptic update. + if self.nu[0].any(): + source_s = self.source.s.view(batch_size, -1).unsqueeze(2).float() + target_x = self.target.x.view(batch_size, -1).unsqueeze(1) * self.nu[0] + + # self.source.s : 28 *28 array of 0 and 1 source_s : array converted to 1D vector (784*1) + # self.target.x : array of 100 values (~1e-5) target_x : araray (20*5) (values ~1e-9) + # source : first layer, target = second layer. + # s: spike occurances (0 or 1) for each neuron x : exp decaying trace + + self.connection.w -= self.reduction(torch.bmm(source_s, target_x), dim=0) + del source_s, target_x + + # Post-synaptic update. + if self.nu[1].any(): + target_s = (self.target.s.view(batch_size, -1).unsqueeze(1).float() * self.nu[1]) # 100 values of 0&1 + source_x = self.source.x.view(batch_size, -1).unsqueeze(2) # 784 value 1D ( values between 0 and 1) + self.connection.w += self.reduction(torch.bmm(source_x, target_s), dim=0) + del source_x, target_s + """ + + # Post-synaptic update. + if self.nu[1].any(): + target_s = (self.target.s.view(batch_size, -1).unsqueeze(1).float() * self.nu[1]) # 100 values of 0&1 + source_x2 = self.source.x2.view(batch_size, -1).unsqueeze(2) # 784 value 1D ( values between 0 and 1) + self.connection.w += self.reduction(torch.bmm(source_x2, target_s), dim=0) + del source_x2, target_s + + super().update() + + +#=================================================== class WeightDependentPostPre(LearningRule): # language=rst diff --git a/bindsnet/models/__init__.py b/bindsnet/models/__init__.py index b57121bc..8a13b8d8 100644 --- a/bindsnet/models/__init__.py +++ b/bindsnet/models/__init__.py @@ -1,5 +1,6 @@ from bindsnet.models.models import ( DiehlAndCook2015, + Salah_model, DiehlAndCook2015v2, IncreasingInhibitionNetwork, LocallyConnectedNetwork, @@ -9,6 +10,7 @@ __all__ = [ "TwoLayerNetwork", "DiehlAndCook2015v2", + "Salah_model", "DiehlAndCook2015", "IncreasingInhibitionNetwork", "LocallyConnectedNetwork", diff --git a/bindsnet/models/models.py b/bindsnet/models/models.py index 50f27872..5cf1f8cf 100644 --- a/bindsnet/models/models.py +++ b/bindsnet/models/models.py @@ -5,7 +5,7 @@ from scipy.spatial.distance import euclidean from torch.nn.modules.utils import _pair -from bindsnet.learning import PostPre +from bindsnet.learning import PostPre, Modified_PostPre from bindsnet.network import Network from bindsnet.network.nodes import DiehlAndCookNodes, Input, LIFNodes from bindsnet.network.topology import Connection, LocalConnection @@ -199,6 +199,124 @@ def __init__( self.add_connection(exc_inh_conn, source="Ae", target="Ai") self.add_connection(inh_exc_conn, source="Ai", target="Ae") +#============================================================================= + +class Salah_model(Network): + # language=rst + """ + Implements the spiking neural network architecture from `(Diehl & Cook 2015) + `_. + """ + + def __init__( + self, + n_inpt: int, + n_neurons: int = 100, + exc: float = 22.5, + inh: float = 17.5, + dt: float = 1.0, + nu: Optional[Union[float, Sequence[float]]] = (1e-4, 1e-2), + reduction: Optional[callable] = None, + wmin: float = 0.0, + wmax: float = 1.0, + norm: float = 78.4, + theta_plus: float = 0.05, + tc_theta_decay: float = 1e7, + inpt_shape: Optional[Iterable[int]] = None, + ) -> None: + # language=rst + """ + Constructor for class ``DiehlAndCook2015``. + + :param n_inpt: Number of input neurons. Matches the 1D size of the input data. + :param n_neurons: Number of excitatory, inhibitory neurons. + :param exc: Strength of synapse weights from excitatory to inhibitory layer. + :param inh: Strength of synapse weights from inhibitory to excitatory layer. + :param dt: Simulation time step. + :param nu: Single or pair of learning rates for pre- and post-synaptic events, + respectively. + :param reduction: Method for reducing parameter updates along the minibatch + dimension. + :param wmin: Minimum allowed weight on input to excitatory synapses. + :param wmax: Maximum allowed weight on input to excitatory synapses. + :param norm: Input to excitatory layer connection weights normalization + constant. + :param theta_plus: On-spike increment of ``DiehlAndCookNodes`` membrane + threshold potential. + :param tc_theta_decay: Time constant of ``DiehlAndCookNodes`` threshold + potential decay. + :param inpt_shape: The dimensionality of the input layer. + """ + super().__init__(dt=dt) + + self.n_inpt = n_inpt + self.inpt_shape = inpt_shape + self.n_neurons = n_neurons + self.exc = exc + self.inh = inh + self.dt = dt + + # Layers + input_layer = Input( + n=self.n_inpt, shape=self.inpt_shape, traces=True, tc_trace=20.0 + ) + exc_layer = DiehlAndCookNodes( + n=self.n_neurons, + traces=True, + rest=-65.0, + reset=-60.0, + thresh=-52.0, + refrac=5, + tc_decay=100.0, + tc_trace=20.0, + theta_plus=theta_plus, + tc_theta_decay=tc_theta_decay, + ) + inh_layer = LIFNodes( + n=self.n_neurons, + traces=False, + rest=-60.0, + reset=-45.0, + thresh=-40.0, + tc_decay=10.0, + refrac=2, + tc_trace=20.0, + ) + + # Connections + w = 0.3 * torch.rand(self.n_inpt, self.n_neurons) + input_exc_conn = Connection( + source=input_layer, + target=exc_layer, + w=w, + update_rule=Modified_PostPre, + nu=nu, + reduction=reduction, + wmin=wmin, + wmax=wmax, + norm=norm, + ) + w = self.exc * torch.diag(torch.ones(self.n_neurons)) + exc_inh_conn = Connection( + source=exc_layer, target=inh_layer, w=w, wmin=0, wmax=self.exc + ) + w = -self.inh * ( + torch.ones(self.n_neurons, self.n_neurons) + - torch.diag(torch.ones(self.n_neurons)) + ) + inh_exc_conn = Connection( + source=inh_layer, target=exc_layer, w=w, wmin=-self.inh, wmax=0 + ) + + # Add to network + self.add_layer(input_layer, name="X") + self.add_layer(exc_layer, name="Ae") + self.add_layer(inh_layer, name="Ai") + self.add_connection(input_exc_conn, source="X", target="Ae") + self.add_connection(exc_inh_conn, source="Ae", target="Ai") + self.add_connection(inh_exc_conn, source="Ai", target="Ae") + +#============================================================================= class DiehlAndCook2015v2(Network): # language=rst diff --git a/bindsnet/network/nodes.py b/bindsnet/network/nodes.py index cf8b709c..16026d3d 100644 --- a/bindsnet/network/nodes.py +++ b/bindsnet/network/nodes.py @@ -2,7 +2,6 @@ from functools import reduce from operator import mul from typing import Iterable, Optional, Union - import torch @@ -67,6 +66,8 @@ def __init__( if self.traces: self.register_buffer("x", torch.Tensor()) # Firing traces. + self.register_buffer("t_", torch.Tensor()) # added delta_t is registred + self.register_buffer("x2", torch.Tensor()) # added x2 bisimoid trace is registred self.register_buffer( "tc_trace", torch.tensor(tc_trace) ) # Time constant of spike trace decay. @@ -93,19 +94,29 @@ def forward(self, x: torch.Tensor) -> None: :param x: Inputs to the layer. """ + def bisigmoid_trace(t_): #added + A = 7.95E+05; k0 = 0.474723045; x0 = 20.77893753; k1 = 0.757072031; x1 = 48.93860322 + result = (-A / (1 + torch.exp(-k0*(t_-x0))) - A / (1 + torch.exp(-k1*(t_-x1))) + A) / A + result.masked_fill_(t_ > 60, torch.tensor(0)) + return result + if self.traces: # Decay and set spike traces. self.x *= self.trace_decay + + self.t_ += torch.tensor(self.dt) #added + self.x2 = bisigmoid_trace(self.t_) #added if self.traces_additive: self.x += self.trace_scale * self.s.float() else: self.x.masked_fill_(self.s.bool(), self.trace_scale) + self.t_.masked_fill_(self.s.bool(), torch.tensor(0)) #added, reset t_ to 0 after spiking if self.sum_input: # Add current input to running sum. self.summed += x.float() - + def reset_state_variables(self) -> None: # language=rst """ @@ -115,6 +126,8 @@ def reset_state_variables(self) -> None: if self.traces: self.x.zero_() # Spike traces. + self.t_.zero_() # added + self.x2.zero_() # added if self.sum_input: self.summed.zero_() # Summed inputs. @@ -144,6 +157,8 @@ def set_batch_size(self, batch_size) -> None: if self.traces: self.x = torch.zeros(batch_size, *self.shape, device=self.x.device) + self.t_ = torch.zeros(batch_size, *self.shape, device=self.t_.device) #added + self.x2 = torch.zeros(batch_size, *self.shape, device=self.x2.device) #added if self.sum_input: self.summed = torch.zeros( @@ -207,7 +222,6 @@ def __init__( trace_scale=trace_scale, sum_input=sum_input, ) - def forward(self, x: torch.Tensor) -> None: # language=rst """ @@ -217,16 +231,16 @@ def forward(self, x: torch.Tensor) -> None: """ # Set spike occurrences to input values. self.s = x - super().forward(x) - + + def reset_state_variables(self) -> None: # language=rst """ Resets relevant state variables. """ super().reset_state_variables() - + class McCullochPitts(Nodes): # language=rst @@ -1083,9 +1097,9 @@ def forward(self, x: torch.Tensor) -> None: # Decrement refractory counters. self.refrac_count -= self.dt - + # Check for spiking neurons. - self.s = self.v >= self.thresh + self.theta + self.s = self.v >= self.thresh + self.theta # spikes whenever it exceds threshold # Refractoriness, voltage reset, and adaptive thresholds. self.refrac_count.masked_fill_(self.s, self.refrac) @@ -1107,7 +1121,7 @@ def forward(self, x: torch.Tensor) -> None: # Voltage clipping to lower bound. if self.lbound is not None: self.v.masked_fill_(self.v < self.lbound, self.lbound) - + super().forward(x) def reset_state_variables(self) -> None: @@ -1131,6 +1145,7 @@ def compute_decays(self, dt) -> None: self.theta_decay = torch.exp( -self.dt / self.tc_theta_decay ) # Adaptive threshold decay (per timestep). + def set_batch_size(self, batch_size) -> None: # language=rst diff --git 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zQ!V^3YLj=v&h9D=+o<)X7<2Sp^x$|oT^q>t#^9am=@zlHDyxWJXBj&>zzUV{X6Adc)&u{ z!fL%U|8O4QsajaAcjh0F0~V_mR_mRLv$pa!r>YfJ>z(-r)&3=_E&oLCH1$>~tVU{@ zuhz@FF<%Z)Evwc$E4fCT%)4;6HL8WxdZ+1i?do|)bE;ZlwcdHi+wwr(ExQJ)wyd_z z+fF~*op<3>wZdw>vzUkDr@VV-x3#J*|4i>(uUhzLdgn&f!fL&K3fha3i(e={rbvF7FO@d4!Jg zE=41?-kHC@Tt})FR_mSH&NkVUkF(VZtM$%gTlc5@5w6NMyQ#LU<}k&>Qsu%%+PW&+ ztkyg6M*pUHn;ocDSgm*F?=Vvx)t1$Ir{Yn)ve~QD3ajT?{P5+o&W8Om)EQ-?3OqB-#h-bH@m}67Js%1vasc}$?Nlj+-LT n_train: + break + # Get next input sample. + inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} + if gpu: + inputs = {k: v.cuda() for k, v in inputs.items()} + + if step % update_interval == 0 and step > 0: + # Convert the array of labels into a tensor + label_tensor = torch.tensor(labels, device=device) + + # Get network predictions. + all_activity_pred = all_activity( + spikes=spike_record, assignments=assignments, n_labels=n_classes + ) + proportion_pred = proportion_weighting( + spikes=spike_record, + assignments=assignments, + proportions=proportions, + n_labels=n_classes, + ) + + # Compute network accuracy according to available classification strategies. + accuracy["all"].append( + 100 + * torch.sum(label_tensor.long() == all_activity_pred).item() + / len(label_tensor) + ) + accuracy["proportion"].append( + 100 + * torch.sum(label_tensor.long() == proportion_pred).item() + / len(label_tensor) + ) + + print( + "\nAll activity accuracy: %.2f (last), %.2f (average), %.2f (best)" + % ( + accuracy["all"][-1], + np.mean(accuracy["all"]), + np.max(accuracy["all"]), + ) + ) + print( + "Proportion weighting accuracy: %.2f (last), %.2f (average), %.2f" + " (best)\n" + % ( + accuracy["proportion"][-1], + np.mean(accuracy["proportion"]), + np.max(accuracy["proportion"]), + ) + ) + + # Assign labels to excitatory layer neurons. + assignments, proportions, rates = assign_labels( + spikes=spike_record, + labels=label_tensor, + n_labels=n_classes, + rates=rates, + ) + + labels = [] + + labels.append(batch["label"]) + + # Run the network on the input. + network.run(inputs=inputs, time=time) + + # Get voltage recording. + exc_voltages = exc_voltage_monitor.get("v") + inh_voltages = inh_voltage_monitor.get("v") + + #added + # Get trace recording. + inp_traces = inp_trace_monitor.get("x2") + exc_traces = exc_trace_monitor.get("x2") + + # Add to spikes recording. + spike_record[step % update_interval] = spikes["Ae"].get("s").squeeze() + + # Optionally plot various simulation information. + if plot: + image = batch["image"].view(28, 28) + inpt = inputs["X"].view(time, 784).sum(0).view(28, 28) + input_exc_weights = network.connections[("X", "Ae")].w + square_weights = get_square_weights( + input_exc_weights.view(784, n_neurons), n_sqrt, 28 + ) + square_assignments = get_square_assignments(assignments, n_sqrt) + spikes_ = {layer: spikes[layer].get("s") for layer in spikes} + voltages = {"Ae": exc_voltages, "Ai": inh_voltages} + traces = {"X": inp_traces, "Ae": exc_traces} # added + # inpt_axes, inpt_ims = plot_input( + # image, inpt, label=batch["label"], axes=inpt_axes, ims=inpt_ims + # ) + #spike_ims, spike_axes = plot_spikes(spikes_, ims=spike_ims, axes=spike_axes) + weights_im = plot_weights(square_weights, im=weights_im) + #assigns_im = plot_assignments(square_assignments, im=assigns_im) + #perf_ax = plot_performance(accuracy, x_scale=update_interval, ax=perf_ax) + #voltage_ims, voltage_axes = plot_voltages( + # voltages, ims=voltage_ims, axes=voltage_axes, plot_type="line") + + #added + #trace_ims, trace_axes = plot_traces( + # traces, n_neurons = {"X": (250, 280)}, ims=trace_ims, axes=trace_axes, plot_type="line") + + plt.pause(1e-8) + + network.reset_state_variables() # Reset state variables. + +network.save('./network.pt') # added + +print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - start)) +print("Training complete.\n") + +# Load MNIST data. +test_dataset = MNIST( + PoissonEncoder(time=time, dt=dt), + None, + root=os.path.join("..", "..", "data", "MNIST"), + download=True, + train=False, + transform=transforms.Compose( + [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] + ), +) + +# Sequence of accuracy estimates. +accuracy = {"all": 0, "proportion": 0} + +# Record spikes during the simulation. +spike_record = torch.zeros((1, int(time / dt), n_neurons), device=device) + +# Train the network. +print("\nBegin testing\n") +network.train(mode=False) +start = t() + +pbar = tqdm(total=n_test) +for step, batch in enumerate(test_dataset): + if step >= n_test: + break + # Get next input sample. + inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} + if gpu: + inputs = {k: v.cuda() for k, v in inputs.items()} + + # Run the network on the input. + network.run(inputs=inputs, time=time) + + # Add to spikes recording. + spike_record[0] = spikes["Ae"].get("s").squeeze() + + # Convert the array of labels into a tensor + label_tensor = torch.tensor(batch["label"], device=device) + + # Get network predictions. + all_activity_pred = all_activity( + spikes=spike_record, assignments=assignments, n_labels=n_classes + ) + proportion_pred = proportion_weighting( + spikes=spike_record, + assignments=assignments, + proportions=proportions, + n_labels=n_classes, + ) + + # Compute network accuracy according to available classification strategies. + accuracy["all"] += float(torch.sum(label_tensor.long() == all_activity_pred).item()) + accuracy["proportion"] += float( + torch.sum(label_tensor.long() == proportion_pred).item() + ) + + network.reset_state_variables() # Reset state variables. + pbar.set_description_str("Test progress: ") + pbar.update() + +print("\nAll activity accuracy: %.2f" % (accuracy["all"] / n_test)) +print("Proportion weighting accuracy: %.2f \n" % (accuracy["proportion"] / n_test)) + +print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - start)) +print("Testing complete.\n") diff --git a/network.pt b/network.pt new file 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plot_traces, # added - ) -from bindsnet.datasets import MNIST -from bindsnet.evaluation import all_activity, assign_labels, proportion_weighting -from bindsnet.models import Salah_model -from bindsnet.network.monitors import Monitor -from bindsnet.utils import get_square_assignments, get_square_weights -from bindsnet.network import Network, load - -parser = argparse.ArgumentParser() -parser.add_argument("--seed", type=int, default=0) -parser.add_argument("--n_neurons", type=int, default=100) -parser.add_argument("--n_test", type=int, default=10000) -parser.add_argument("--n_workers", type=int, default=-1) -parser.add_argument("--theta_plus", type=float, default=0.05) -parser.add_argument("--time", type=int, default=250) -parser.add_argument("--dt", type=int, default=1.0) -parser.add_argument("--intensity", type=float, default=128) -parser.add_argument("--progress_interval", type=int, default=10) -parser.add_argument("--update_interval", type=int, default=250) -parser.add_argument("--test", dest="train", action="store_false") -parser.add_argument("--plot", dest="plot", action="store_true") -parser.add_argument("--gpu", dest="gpu", action="store_true") -parser.set_defaults(plot=True, gpu=False) - -args = parser.parse_args() - -seed = args.seed -n_neurons = args.n_neurons -n_test = args.n_test -n_workers = args.n_workers -theta_plus = args.theta_plus -time = args.time -dt = args.dt -intensity = args.intensity -progress_interval = args.progress_interval -update_interval = args.update_interval -plot = args.plot -gpu = args.gpu -n_sqrt = int(np.ceil(np.sqrt(n_neurons))) - -#load pre-trained network -my_network = load("net_24_5.pt") - -#extract weights -input_exc_weights = my_network.connections[("X", "Ae")].w -square_weights = get_square_weights( input_exc_weights.view(784, n_neurons), n_sqrt, 28 ) - -spikes = {} -for layer in set(my_network.layers): - spikes[layer] = Monitor( - my_network.layers[layer], state_vars=["s"], time=int(time / dt), device="cpu" - ) - my_network.add_monitor(spikes[layer], name="%s_spikes" % layer) - - - -print(dir(my_network)) -#weights_im = plot_weights(square_weights, im=None) -#plt.pause(400) - - - diff --git a/examples/mnist/salah_example.py b/examples/mnist/salah_example.py index e4297c2e..2d3899d9 100644 --- a/examples/mnist/salah_example.py +++ b/examples/mnist/salah_example.py @@ -38,15 +38,16 @@ parser.add_argument("--dt", type=int, default=1.0) parser.add_argument("--intensity", type=float, default=128) parser.add_argument("--progress_interval", type=int, default=10) -parser.add_argument("--update_interval", type=int, default=250) +parser.add_argument("--update_interval", type=int, default=250) parser.add_argument("--train", dest="train", action="store_true") parser.add_argument("--test", dest="train", action="store_false") parser.add_argument("--plot", dest="plot", action="store_true") parser.add_argument("--gpu", dest="gpu", action="store_true") -parser.set_defaults(plot=False, gpu=True) # plot was True - +parser.set_defaults(plot=False, gpu=True, train="True", n_train=20000, n_epochs=1) args = parser.parse_args() +save_as = "exp_7" + seed = args.seed n_neurons = args.n_neurons n_epochs = args.n_epochs @@ -82,9 +83,6 @@ if n_workers == -1: n_workers = gpu * 4 * torch.cuda.device_count() # it was 0 -if not train: - update_interval = n_test - n_sqrt = int(np.ceil(np.sqrt(n_neurons))) start_intensity = intensity @@ -197,8 +195,7 @@ # Create a dataloader to iterate and batch data dataloader = torch.utils.data.DataLoader( - train_dataset, batch_size=1, shuffle=True, num_workers=n_workers, pin_memory=gpu - ) + train_dataset, batch_size=1, shuffle=True, num_workers=n_workers, pin_memory=gpu) for step, batch in enumerate(tqdm(dataloader)): if step > n_train: @@ -207,7 +204,7 @@ inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} if gpu: inputs = {k: v.cuda() for k, v in inputs.items()} - + if step % update_interval == 0 and step > 0: # Convert the array of labels into a tensor label_tensor = torch.tensor(labels, device=device) @@ -310,79 +307,11 @@ network.reset_state_variables() # Reset state variables. -network.save('./net_27_5.pt') # added -assign_labels_fn = {"assignments": assignments, "proportions": proportions, "rates": rates} -torch.save(assign_labels_fn, "./assign_labels.pt") +train_time = t()-start +network.save(f"./net_{save_as}.pt") # added +train_details = {"assignments": assignments, "proportions": proportions, "rates": rates,"train_accur":accuracy, "train_time": train_time} +torch.save(train_details, f"./details_{save_as}.pt") -print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - start)) +print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, train_time)) print("Training complete.\n") -''' -# Load MNIST data. -test_dataset = MNIST( - PoissonEncoder(time=time, dt=dt), - None, - root=os.path.join("..", "..", "data", "MNIST"), - download=True, - train=False, - transform=transforms.Compose( - [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] - ), -) - -# Sequence of accuracy estimates. -accuracy = {"all": 0, "proportion": 0} - -# Record spikes during the simulation. -spike_record = torch.zeros((1, int(time / dt), n_neurons), device=device) - -# Test the network. -print("\nBegin testing\n") -network.train(mode=False) -start = t() - -pbar = tqdm(total=n_test) -for step, batch in enumerate(test_dataset): - if step >= n_test: - break - # Get next input sample. - inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} - if gpu: - inputs = {k: v.cuda() for k, v in inputs.items()} - - # Run the network on the input. - network.run(inputs=inputs, time=time) - - # Add to spikes recording. - spike_record[0] = spikes["Ae"].get("s").squeeze() - - # Convert the array of labels into a tensor - label_tensor = torch.tensor(batch["label"], device=device) - - # Get network predictions. - all_activity_pred = all_activity( - spikes=spike_record, assignments=assignments, n_labels=n_classes - ) - proportion_pred = proportion_weighting( - spikes=spike_record, - assignments=assignments, - proportions=proportions, - n_labels=n_classes, - ) - - # Compute network accuracy according to available classification strategies. - accuracy["all"] += float(torch.sum(label_tensor.long() == all_activity_pred).item()) - accuracy["proportion"] += float( - torch.sum(label_tensor.long() == proportion_pred).item() - ) - - network.reset_state_variables() # Reset state variables. - pbar.set_description_str("Test progress: ") - pbar.update() - -print("\nAll activity accuracy: %.2f" % (accuracy["all"] / n_test)) -print("Proportion weighting accuracy: %.2f \n" % (accuracy["proportion"] / n_test)) - -print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - start)) -print("Testing complete.\n") -''' From f014e87fdffa8567fc365ac691907c80721a7747 Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Thu, 24 Aug 2023 16:46:56 +0200 Subject: [PATCH 05/24] first commit in auguest --- bindsnet/learning/learning.py | 4 ++-- bindsnet/models/models.py | 2 +- examples/mnist/details_exp_8.pt | Bin 0 -> 11585 bytes examples/mnist/details_exp_9.pt | Bin 0 -> 23041 bytes examples/mnist/evaluate_plot.py | 12 ++++++------ examples/mnist/net_exp_8.pt | Bin 0 -> 636751 bytes examples/mnist/net_exp_9.pt | Bin 0 -> 636751 bytes examples/mnist/salah_example.py | 6 +++--- 8 files changed, 12 insertions(+), 12 deletions(-) create mode 100644 examples/mnist/details_exp_8.pt create mode 100644 examples/mnist/details_exp_9.pt create mode 100644 examples/mnist/net_exp_8.pt create mode 100644 examples/mnist/net_exp_9.pt diff --git a/bindsnet/learning/learning.py b/bindsnet/learning/learning.py index 69913705..a5fb4e3c 100644 --- a/bindsnet/learning/learning.py +++ b/bindsnet/learning/learning.py @@ -590,7 +590,7 @@ def __init__( self.source.traces and self.target.traces ), "Both pre- and post-synaptic nodes must record spike traces." - if isinstance(connection, (Connection, LocalConnection)): + if isinstance(connection, (Connection, LocalConnection)): # added: Modified_PostPre will work only fore these 2 connections self.update = self._connection_update # rewrites the update rule defined in the base class else: raise NotImplementedError( @@ -614,7 +614,7 @@ def _connection_update(self, **kwargs) -> None: # self.source.s : 28 *28 array of 0 and 1 source_s : array converted to 1D vector (784*1) # self.target.x : array of 100 values (~1e-5) target_x : araray (20*5) (values ~1e-9) # source : first layer, target = second layer. - # s: spike occurances (0 or 1) for each neuron x : exp decaying trace + # s: spike occurances (0 or 1) for each neuron; x : exp decaying trace self.connection.w -= self.reduction(torch.bmm(source_s, target_x), dim=0) del source_s, target_x diff --git a/bindsnet/models/models.py b/bindsnet/models/models.py index 5cf1f8cf..d54c99c5 100644 --- a/bindsnet/models/models.py +++ b/bindsnet/models/models.py @@ -285,7 +285,7 @@ def __init__( # Connections w = 0.3 * torch.rand(self.n_inpt, self.n_neurons) - input_exc_conn = Connection( + input_exc_conn = Connection( # Modied_PostPre is only working for Connection and LocalConnection) source=input_layer, target=exc_layer, w=w, diff --git a/examples/mnist/details_exp_8.pt b/examples/mnist/details_exp_8.pt new file mode 100644 index 0000000000000000000000000000000000000000..589b4e7842ef32a182d151b6c707e7360128599f GIT binary patch literal 11585 zcmb7q2|QNK|2DEKk|ku#F0!<7pP4grPD0tYB1uH6?Y6b4NGVw=iR=n(NjPre81Os=9=Z`xH%3J6%vw^6#B2EEHqpw zI3y-;acFcv$nwYlYxCg1n85LoOGE8wNuk+)rv#DJTp=SL8D96m=;*}@!^1+tW1_jj z^J5~S<}Vr_5F5kmFC7pS5)`{QG&mq8Bs@AIDj?2Enk!;t3xRQ>-8@N&_^XEr|hK9_K zSsW1_E$tc=91;~0Y`b`Vj5Jr;*n=w*!yWFy9l>ynjEabih+=?T*+o1Gi&(dIp%H;G z0>5&MUrX-DMdHpchQ)B@8HbM2$=p$n(vHH(T!oZHeA|jKTqO^#vX2;#T~uI9NHll! zFRP1wS+L@&{B1#1U_tGdg)zS@sK;pX$L=Dvd88Vm}HM_*aBuT6WIdIHrcU-89chYWRLCH8aozj%UZT$ 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a/examples/mnist/evaluate_plot.py +++ b/examples/mnist/evaluate_plot.py @@ -44,7 +44,7 @@ parser.add_argument("--test", dest="train", action="store_false") parser.add_argument("--plot", dest="plot", action="store_true") parser.add_argument("--gpu", dest="gpu", action="store_true") -parser.set_defaults(plot=True, gpu=False, train="False", n_test=1000 ) +parser.set_defaults(plot=True, gpu=False, train="False", n_test=10000 ) args = parser.parse_args() seed = args.seed @@ -63,7 +63,7 @@ train = args.train plot = args.plot gpu = args.gpu -saved_as = "exp_7" +saved_as = "exp_9" #================================================ # Sets up Gpu use (not used) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -138,7 +138,7 @@ print("\nBegin testing\n") start = t() -#''' +''' pbar = tqdm(total=n_test) for step, batch in enumerate(test_dataset): if step >= n_test: @@ -181,7 +181,7 @@ print("\nAll activity accuracy: %.2f \n" % (100*accuracy["all"] / n_test)) print("Proportion weighting accuracy: %.2f \n" % (100*accuracy["proportion"] / n_test)) print("Testing complete after: %.4f seconds \n" % (t() - start)) - +''' #================================================================================== #********************************** Ploting ************************************** @@ -195,8 +195,8 @@ weights_im = plot_weights(square_weights, im=None) assigns_im = plot_assignments(square_assignments, im=None) perf_ax = plot_performance(train_accur, x_scale=update_interval, ax=None) -plt.pause(400) -#''' +plt.pause(300) + #print(train_details['train_time']) #print(train_details["train_accur"]) diff --git a/examples/mnist/net_exp_8.pt b/examples/mnist/net_exp_8.pt new file mode 100644 index 0000000000000000000000000000000000000000..8441fea6a7ef0a9c01ca5d5cc86b6edb52a9e98c GIT binary patch literal 636751 zcmeF42VfLM7l0#8Kmo3SQ)=4oX^pS2D#q_rH7?dR+OkJ!v=sr$#!j0sL0Yd`DTS>^ZzQ8ksh(0cRYKPy7Mrb>YUMEGH=xSpuHn9_)TwOli8zf=Y8M7|-RHT_z*Ek(^#cor39>wyQHo2lE 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delete mode 100644 test_file.txt diff --git a/test_file.txt b/test_file.txt deleted file mode 100644 index e69de29b..00000000 From dd2ff6b518a1f0fb3030158deae82a345407e633 Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Mon, 28 Aug 2023 18:10:09 +0200 Subject: [PATCH 08/24] aa --- bindsnet/evaluation/evaluation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bindsnet/evaluation/evaluation.py b/bindsnet/evaluation/evaluation.py index 5271d762..83f15a70 100644 --- a/bindsnet/evaluation/evaluation.py +++ b/bindsnet/evaluation/evaluation.py @@ -118,7 +118,7 @@ def all_activity( n_assigns = torch.sum(assignments == i).float() if n_assigns > 0: - # Get indices of samples with this label. + # Get indices of samples with this label. # correcting : get the number of neurons with this label indices = torch.nonzero(assignments == i).view(-1) # Compute layer-wise firing rate for this label. From 61f5774bb405aba391d7c4197b6d4ee10865f117 Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Tue, 29 Aug 2023 11:30:38 +0200 Subject: [PATCH 09/24] updated --- examples/mnist/evaluate_plot.py | 2 +- examples/mnist/salah_example.py | 14 ++++++++------ 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/examples/mnist/evaluate_plot.py b/examples/mnist/evaluate_plot.py index 8568b777..95ad86df 100644 --- a/examples/mnist/evaluate_plot.py +++ b/examples/mnist/evaluate_plot.py @@ -197,7 +197,7 @@ perf_ax = plot_performance(train_accur, x_scale=update_interval, ax=None) plt.pause(300) -#print(train_details['train_time']) +print("training time", train_details['train_time']) #print(train_details["train_accur"]) diff --git a/examples/mnist/salah_example.py b/examples/mnist/salah_example.py index 46b7c644..18eed8d2 100644 --- a/examples/mnist/salah_example.py +++ b/examples/mnist/salah_example.py @@ -20,7 +20,7 @@ from bindsnet.datasets import MNIST from bindsnet.encoding import PoissonEncoder from bindsnet.evaluation import all_activity, assign_labels, proportion_weighting -from bindsnet.models import Salah_model +from bindsnet.models import Salah_model # import model from bindsnet.network.monitors import Monitor from bindsnet.utils import get_square_assignments, get_square_weights @@ -43,7 +43,7 @@ parser.add_argument("--test", dest="train", action="store_false") parser.add_argument("--plot", dest="plot", action="store_true") parser.add_argument("--gpu", dest="gpu", action="store_true") -parser.set_defaults(plot=False, gpu=False, train="True", n_train=60000, n_epochs=3) +parser.set_defaults(plot=False, gpu=False, train="True", n_train=60000, n_epochs=1) args = parser.parse_args() save_as = "exp_9" @@ -81,7 +81,7 @@ # Determines number of workers to use if n_workers == -1: - n_workers = 0#gpu * 4 * torch.cuda.device_count() # it was 0 + n_workers = 0#gpu * 4 * torch.cuda.device_count() n_sqrt = int(np.ceil(np.sqrt(n_neurons))) start_intensity = intensity @@ -110,7 +110,7 @@ download=True, train=True, transform=transforms.Compose( - [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] + [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] # intensity = 128 ), ) @@ -189,7 +189,7 @@ for epoch in range(n_epochs): labels = [] - if epoch % progress_interval == 0: + if epoch % progress_interval == 0: print("Progress: %d / %d (%.4f seconds)" % (epoch, n_epochs, t() - start)) start = t() @@ -211,7 +211,9 @@ # Get network predictions. all_activity_pred = all_activity( - spikes=spike_record, assignments=assignments, n_labels=n_classes + spikes=spike_record, + assignments=assignments, + n_labels=n_classes, ) proportion_pred = proportion_weighting( spikes=spike_record, From 5302f566c06a6ccfb2dfb151a2ba2d3bfc08d6e7 Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Tue, 29 Aug 2023 19:37:26 +0200 Subject: [PATCH 10/24] changed Modified_PostPre to Bi_sigmoid in all commanding file --- bindsnet/learning/__init__.py | 4 ++-- bindsnet/learning/learning.py | 4 ++-- bindsnet/models/models.py | 20 ++++++++++-------- examples/mnist/details_exp_10.pt | Bin 0 -> 11590 bytes examples/mnist/evaluate_plot.py | 3 ++- examples/mnist/net_exp_10.pt | Bin 0 -> 636746 bytes examples/mnist/salah_example.py | 35 +++++++++++++++++++++++++++---- 7 files changed, 48 insertions(+), 18 deletions(-) create mode 100644 examples/mnist/details_exp_10.pt create mode 100644 examples/mnist/net_exp_10.pt diff --git a/bindsnet/learning/__init__.py b/bindsnet/learning/__init__.py index 25bb57aa..e9fb5a1a 100644 --- a/bindsnet/learning/__init__.py +++ b/bindsnet/learning/__init__.py @@ -5,7 +5,7 @@ LearningRule, NoOp, PostPre, - Modified_PostPre, + Bi_sigmoid, Rmax, WeightDependentPostPre, ) @@ -14,7 +14,7 @@ "LearningRule", "NoOp", "PostPre", - "Modified_PostPre", + "Bi_sigmoid", "WeightDependentPostPre", "Hebbian", "MSTDP", diff --git a/bindsnet/learning/learning.py b/bindsnet/learning/learning.py index a5fb4e3c..bbcee2e7 100644 --- a/bindsnet/learning/learning.py +++ b/bindsnet/learning/learning.py @@ -550,7 +550,7 @@ def _conv3d_connection_update(self, **kwargs) -> None: super().update() #=================================================== -class Modified_PostPre(LearningRule): +class Bi_sigmoid(LearningRule): # language=rst """ Simple STDP rule involving both pre- and post-synaptic spiking activity. By default, @@ -590,7 +590,7 @@ def __init__( self.source.traces and self.target.traces ), "Both pre- and post-synaptic nodes must record spike traces." - if isinstance(connection, (Connection, LocalConnection)): # added: Modified_PostPre will work only fore these 2 connections + if isinstance(connection, (Connection, LocalConnection)): # added: Bi_sigmoid will work only fore these 2 connections self.update = self._connection_update # rewrites the update rule defined in the base class else: raise NotImplementedError( diff --git a/bindsnet/models/models.py b/bindsnet/models/models.py index d54c99c5..df9874b4 100644 --- a/bindsnet/models/models.py +++ b/bindsnet/models/models.py @@ -5,7 +5,7 @@ from scipy.spatial.distance import euclidean from torch.nn.modules.utils import _pair -from bindsnet.learning import PostPre, Modified_PostPre +from bindsnet.learning import PostPre, Bi_sigmoid from bindsnet.network import Network from bindsnet.network.nodes import DiehlAndCookNodes, Input, LIFNodes from bindsnet.network.topology import Connection, LocalConnection @@ -203,9 +203,11 @@ def __init__( class Salah_model(Network): # language=rst - """ - Implements the spiking neural network architecture from `(Diehl & Cook 2015) - `_. + """ + It implements the same network architecture model used by `(Diehl & Cook 2015)`, + that has input, excitatoy and inhebetory neurons layers. But this model uses + a Bi-sigmoid learning rule (Bi_sigmoid), which is hardware friendly. The Bi-sigmoid rule + describes the learning behavior of the MTJs-based synapses. """ def __init__( @@ -263,9 +265,9 @@ def __init__( exc_layer = DiehlAndCookNodes( n=self.n_neurons, traces=True, - rest=-65.0, - reset=-60.0, - thresh=-52.0, + rest= -65.0, + reset= -60.0, + thresh= -52.0, refrac=5, tc_decay=100.0, tc_trace=20.0, @@ -278,8 +280,8 @@ def __init__( rest=-60.0, reset=-45.0, thresh=-40.0, - tc_decay=10.0, refrac=2, + tc_decay=10.0, tc_trace=20.0, ) @@ -289,7 +291,7 @@ def __init__( source=input_layer, target=exc_layer, w=w, - update_rule=Modified_PostPre, + update_rule=Bi_sigmoid, nu=nu, reduction=reduction, wmin=wmin, diff --git a/examples/mnist/details_exp_10.pt b/examples/mnist/details_exp_10.pt new file mode 100644 index 0000000000000000000000000000000000000000..1b9942a1c7ab2a127503f4a6d55f10359c4a11a9 GIT binary patch literal 11590 zcmb7K30zHE+dq-0lu99GiZT=`L}x#1uV?SlNE$?@n^c4hr$TNSl1fFXNK#QrNF)si zH}jZTBEvOziOfT$@0{)3$9=u`{l4F~e!ufu)ARhFaqYGCKFi&;rK&{Irj6u3N=J#N zBp@)%Z+dX3Z(#UbUn|Rj0e)eA1Ln>QcHmn}#{VcwTP%`E`uJ$d+5JL8r%#(bD{yvL 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parser.add_argument("--update_interval", type=int, default=250) -parser.add_argument("--train", dest="train", action="store_true") +# adding/not adding --train to CL makes args.train true/false +parser.add_argument("--train", dest="train", action="store_true") parser.add_argument("--test", dest="train", action="store_false") parser.add_argument("--plot", dest="plot", action="store_true") parser.add_argument("--gpu", dest="gpu", action="store_true") -parser.set_defaults(plot=False, gpu=False, train="True", n_train=60000, n_epochs=1) +# But if none of the four is added, these are the default ones: +parser.set_defaults(plot=False, gpu=False, train="True") args = parser.parse_args() -save_as = "exp_9" +print(args) +save_as = "exp_10" +#modified +# Salah model has been modified +seed = args.seed +n_neurons = args.n_neurons +n_epochs = args.n_epochs +n_test = args.n_test +n_train = 20000 +n_workers = args.n_workers +exc = args.exc +inh = args.inh +theta_plus = args.theta_plus +time = args.time +dt = args.dt +intensity = args.intensity +progress_interval = args.progress_interval +update_interval = args.update_interval +train = args.train +plot = args.plot +gpu = args.gpu + +''' +#Original seed = args.seed n_neurons = args.n_neurons n_epochs = args.n_epochs @@ -65,6 +90,8 @@ train = args.train plot = args.plot gpu = args.gpu +''' + # Sets up Gpu use device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -81,7 +108,7 @@ # Determines number of workers to use if n_workers == -1: - n_workers = 0#gpu * 4 * torch.cuda.device_count() + n_workers = 0 #gpu * 4 * torch.cuda.device_count() n_sqrt = int(np.ceil(np.sqrt(n_neurons))) start_intensity = intensity From 4c1b7ae70f79d3a1655ea1b540ba5336bcf69c48 Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Tue, 29 Aug 2023 19:52:14 +0200 Subject: [PATCH 11/24] changed Modified_PostPre by Bi_sigmoid everywhere --- examples/mnist/evaluate_plot.py | 2 +- examples/mnist/net_24_5.pt | Bin 636751 -> 636745 bytes examples/mnist/net_27_5.pt | Bin 637455 -> 637449 bytes examples/mnist/net_exp_1.pt | Bin 637455 -> 637449 bytes examples/mnist/net_exp_2.pt | Bin 637455 -> 637449 bytes examples/mnist/net_exp_3.pt | Bin 637455 -> 637449 bytes examples/mnist/net_exp_4.pt | Bin 636751 -> 636745 bytes examples/mnist/net_exp_5.pt | Bin 636751 -> 636745 bytes examples/mnist/net_exp_6.pt | Bin 637455 -> 637449 bytes examples/mnist/net_exp_7.pt | Bin 637455 -> 637449 bytes examples/mnist/net_exp_8.pt | Bin 636751 -> 636745 bytes examples/mnist/net_exp_9.pt | Bin 636751 -> 636745 bytes 12 files changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/mnist/evaluate_plot.py b/examples/mnist/evaluate_plot.py index d5f09e51..d5cfa9ef 100644 --- a/examples/mnist/evaluate_plot.py +++ b/examples/mnist/evaluate_plot.py @@ -63,7 +63,7 @@ train = args.train plot = args.plot gpu = args.gpu -saved_as = "exp_10" +saved_as = "exp_9" #================================================ # Sets up Gpu use (not used) diff 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#================================================================================== #********************************** Ploting ************************************** +print("training time", train_details['train_time']) +print(train_details["train_accur"]) + #extract weights input_exc_weights = network.connections[("X", "Ae")].w square_weights = get_square_weights( input_exc_weights.view(784, n_neurons), n_sqrt, 28 ) @@ -198,8 +201,6 @@ perf_ax = plot_performance(train_accur, x_scale=update_interval, ax=None) plt.pause(300) -print("training time", train_details['train_time']) -#print(train_details["train_accur"]) diff --git a/examples/mnist/net_exp_10.pt b/examples/mnist/net_exp_10.pt index e11fe86b259dc71cdaae7d4424df2cc70aa1849c..1eefee1decd213aee5c66f5e87d4f55c50cea0b4 100644 GIT binary patch literal 636751 zcmeF42VfM{)_^0R6afKIDON-Qv4Zp_EFy|hV&kb80|b&#cd~$}Ac!=vV8dPzJ1SsB zM1okcVeeh+U98yNoo});vomw=&dlzHBrN~$w`Wej=T6z(1m55Q)obMB)T))^`qLn% 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z$;MQVjdF@@6-H;!U_plR0d{eOmh4XoTZPeCvcLNH$TKU9&MhZfMwQ@f1!jfOnQb@! znfMM@uFoUoH7htw`nFV-*+5fUuFndi6K@E1Qf_v*0<*&CEZJcuTJo9|MyK>$y{y?G z3d{7I7D?-) zSHW4**GE}i+c-I11z$_E)1JyeME G*Af7&n-mKG delta 67 zcmX^4PVM|VwGFO90>1evnQ57+De(dM#U%knsm=aE?fycHK+FWh%s|Wn#H>Kf2E^<@ M%(2~Hi1XJH07gg_EC2ui From 11fef2bb2b2702dacc5d5e37d3882f2fb5e13548 Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Wed, 25 Oct 2023 15:51:34 +0200 Subject: [PATCH 13/24] last commit --- bindsnet/learning/learning.py | 48 ++++++++++++------------------- examples/mnist/details_exp_12.pt | Bin 0 -> 9734 bytes examples/mnist/details_exp_13.pt | Bin 0 -> 9734 bytes examples/mnist/net_exp_12.pt | Bin 0 -> 636751 bytes examples/mnist/net_exp_13.pt | Bin 0 -> 636751 bytes examples/mnist/salah_example.py | 25 +--------------- 6 files changed, 20 insertions(+), 53 deletions(-) create mode 100644 examples/mnist/details_exp_12.pt create mode 100644 examples/mnist/details_exp_13.pt create mode 100644 examples/mnist/net_exp_12.pt create mode 100644 examples/mnist/net_exp_13.pt diff --git a/bindsnet/learning/learning.py b/bindsnet/learning/learning.py index bbcee2e7..ac6354bc 100644 --- a/bindsnet/learning/learning.py +++ b/bindsnet/learning/learning.py @@ -389,6 +389,11 @@ def _connection_update(self, **kwargs) -> None: """ Post-pre learning rule for ``Connection`` subclass of ``AbstractConnection`` class. + + self.source.s : 28 *28 array of 0 and 1 source_s : array converted to 1D vector (784*1) + self.target.x : array of 100 values (~1e-5) target_x : araray (20*5) (values ~1e-9) + source : first layer, target = second layer. + s: spike occurances (0 or 1) for each neuron; x : exp decaying trace """ batch_size = self.source.batch_size @@ -553,8 +558,9 @@ def _conv3d_connection_update(self, **kwargs) -> None: class Bi_sigmoid(LearningRule): # language=rst """ - Simple STDP rule involving both pre- and post-synaptic spiking activity. By default, - pre-synaptic update is negative and the post-synaptic update is positive. + Bi_sigmoid STDP rule involving only post-synaptic spiking activity. The weight update + quantity is poisitive if the post-synaptic spike occures shortly after the presynatpic spike, + and negative otherwise. """ def __init__( @@ -567,10 +573,10 @@ def __init__( ) -> None: # language=rst """ - Constructor for ``PostPre`` learning rule. + Constructor for ``Bi_sigmoid`` learning rule. :param connection: An ``AbstractConnection`` object whose weights the - ``PostPre`` learning rule will modify. + ``Bi_sigmoid`` learning rule will modify. :param nu: Single or pair of learning rates for pre- and post-synaptic events. It also accepts a pair of tensors to individualize learning rates of each neuron. In this case, their shape should be the same size as the connection weights. @@ -591,7 +597,7 @@ def __init__( ), "Both pre- and post-synaptic nodes must record spike traces." if isinstance(connection, (Connection, LocalConnection)): # added: Bi_sigmoid will work only fore these 2 connections - self.update = self._connection_update # rewrites the update rule defined in the base class + self.update = self._connection_update # rewrites the update rule defined in the base class else: raise NotImplementedError( "This learning rule is not supported for this Connection type." @@ -600,37 +606,21 @@ def __init__( def _connection_update(self, **kwargs) -> None: # language=rst """ - Post-pre learning rule for ``Connection`` subclass of ``AbstractConnection`` + Bi_sigmoid learning rule for ``Connection`` subclass of ``AbstractConnection`` class. + + self.source.s : 28 *28 array of 0 and 1 source_s : array converted to 1D vector (784*1) + self.target.x2 : array of 100 values (~1e-5) target_x2 : araray (20*5) (values ~1e-9) + source : first layer, target = second layer. + s: spike occurances (0 or 1) for each neuron; x2 : bi_sigmoid decaying trace + In this rule we only use the spiking of post (target_s) and the bi_sigmoid trace of pre (source_x2) """ batch_size = self.source.batch_size - """ - # Pre-synaptic update. - if self.nu[0].any(): - source_s = self.source.s.view(batch_size, -1).unsqueeze(2).float() - target_x = self.target.x.view(batch_size, -1).unsqueeze(1) * self.nu[0] - - # self.source.s : 28 *28 array of 0 and 1 source_s : array converted to 1D vector (784*1) - # self.target.x : array of 100 values (~1e-5) target_x : araray (20*5) (values ~1e-9) - # source : first layer, target = second layer. - # s: spike occurances (0 or 1) for each neuron; x : exp decaying trace - - self.connection.w -= self.reduction(torch.bmm(source_s, target_x), dim=0) - del source_s, target_x - - # Post-synaptic update. - if self.nu[1].any(): - target_s = (self.target.s.view(batch_size, -1).unsqueeze(1).float() * self.nu[1]) # 100 values of 0&1 - source_x = self.source.x.view(batch_size, -1).unsqueeze(2) # 784 value 1D ( values between 0 and 1) - self.connection.w += self.reduction(torch.bmm(source_x, target_s), dim=0) - del source_x, target_s - """ - # Post-synaptic update. if self.nu[1].any(): target_s = (self.target.s.view(batch_size, -1).unsqueeze(1).float() * self.nu[1]) # 100 values of 0&1 - source_x2 = self.source.x2.view(batch_size, -1).unsqueeze(2) # 784 value 1D ( values between 0 and 1) + source_x2 = self.source.x2.view(batch_size, -1).unsqueeze(2) # 784 value 1D ( values between -1 and 1) self.connection.w += self.reduction(torch.bmm(source_x2, target_s), dim=0) del source_x2, target_s diff --git a/examples/mnist/details_exp_12.pt b/examples/mnist/details_exp_12.pt new file mode 100644 index 0000000000000000000000000000000000000000..d230d3f4ed17522e7d68932fcfe8c3e035f37a93 GIT binary patch literal 9734 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b/examples/mnist/salah_example.py @@ -49,30 +49,8 @@ args = parser.parse_args() print(args) -save_as = "exp_11" +save_as = "exp_14" -#modified -# Salah model has been modified -seed = args.seed -n_neurons = args.n_neurons -n_epochs = args.n_epochs -n_test = args.n_test -n_train = 30000 -n_workers = args.n_workers -exc = args.exc -inh = args.inh -theta_plus = 0.1 #args.theta_plus -time = args.time -dt = args.dt -intensity = args.intensity -progress_interval = args.progress_interval -update_interval = args.update_interval -train = args.train -plot = args.plot -gpu = args.gpu - -''' -#Original seed = args.seed n_neurons = args.n_neurons n_epochs = args.n_epochs @@ -90,7 +68,6 @@ train = args.train plot = args.plot gpu = args.gpu -''' # Sets up Gpu use From 7cbedcfaf04588f60345669c290d6f30641ff3cf Mon Sep 17 00:00:00 2001 From: salah-daddi-nounou Date: Wed, 25 Oct 2023 17:04:00 +0200 Subject: [PATCH 14/24] last commit --- examples/mnist/assign_labels.pt | Bin 10049 -> 0 bytes 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