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simulator_yinyang.py
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import numpy as np
import matplotlib.pyplot as plt
from pygenn import genn_model
from pygenn.genn_wrapper import NO_DELAY
from Dataset import YinYangDataset
from models import *
import os
from time import time
# ----------------------------------------------------------------------------
# Parameters
# ----------------------------------------------------------------------------
p= {}
p["OUT_DIR"]= "."
p["NAME"] = "test"
p["DT_MS"] = 0.1
p["BUILD"] = True
p["TIMING"] = True
p["TRAIN_DATA_SEED"]= 123
p["TEST_DATA_SEED"]= 456
# Experiment parameters
p["TRIAL_MS"]= 30.0
p["N_MAX_SPIKE"]= 400 # make buffers for maximally 400 spikes (200 in a 30 ms trial), probably over-kill
p["N_BATCH"]= 32
p["N_TRAIN"]= p["N_BATCH"]*1000
p["N_EPOCH"]= 10
p["N_TEST"]= p["N_BATCH"]*25
N_CLASS= 3 # hard-coded as an immutable part of the YinYang benchmark
# Network structure
NUM_INPUT = 5 # hard-coded as an immutable part of the YinYang benchmark
NUM_OUTPUT = N_CLASS # hard-coded as an immutable part of the YinYang benchmark
p["NUM_HIDDEN"] = 100
# Model parameters
p["TAU_SYN"] = 5.0
p["TAU_MEM"] = 20.0
p["V_THRESH"] = 1.0
p["V_RESET"] = 0.0
p["TAU_0"]= 0.5
p["TAU_1"]= 6.4
p["ALPHA"]= 3e-3
p["INPUT_HIDDEN_MEAN"]= 1.5
p["INPUT_HIDDEN_STD"]= 0.78
p["HIDDEN_OUTPUT_MEAN"]= 0.93
p["HIDDEN_OUTPUT_STD"]= 0.1
# Learning parameters
p["ETA"]= 5e-3
p["ADAM_BETA1"]= 0.9
p["ADAM_BETA2"]= 0.999
p["ADAM_EPS"]= 1e-8
# applied every epoch
p["ETA_DECAY"]= 0.95
# recording things
p["SPK_REC_STEPS"]= int(p["TRIAL_MS"]/p["DT_MS"])
p["REC_SPIKES"] = []
p["REC_NEURONS"] = []
p["REC_SYNAPSES"] = []
p["WRITE_TO_DISK"]= True
p["TRAINING_PLOT"]= False
p["TRAINING_PLOT_INTERVAL"]= 10
p["FANCY_PLOTS"]= False
p["LOAD_LAST"]= False
p["W_REPORT_INTERVAL"] = 100
p["W_EPOCH_INTERVAL"] = 10
# ----------------------------------------------------------------------------
# Helper functions
# ----------------------------------------------------------------------------
def get_input_start(input_end):
input_start = np.empty_like(input_end)
if input_end.ndim == 1:
input_start[0] = 0
input_start[1:] = input_end[:-1]
else:
input_start[0,0] = 0
input_start[1:,0] = input_end[:-1,-1]
input_start[:,1:] = input_end[:,:-1]
return input_start
def update_adam(learning_rate, adam_step, optimiser_custom_updates):
first_moment_scale = 1.0 / (1.0 - (p["ADAM_BETA1"] ** adam_step))
second_moment_scale = 1.0 / (1.0 - (p["ADAM_BETA2"] ** adam_step))
# Loop through optimisers and set
for o in optimiser_custom_updates:
o.extra_global_params["alpha"].view[:] = learning_rate
o.extra_global_params["firstMomentScale"].view[:] = first_moment_scale
o.extra_global_params["secondMomentScale"].view[:] = second_moment_scale
def loss_func(nfst, Y, trial):
#print("new first spikes: {}".format(nfst))
t= nfst-trial*p["TRIAL_MS"]
t[t < 0.0]= p["TRIAL_MS"]
expsum= np.sum(np.exp(-t/p["TAU_0"]),axis=-1)
pred= np.argmin(t,axis=-1)
selected= np.array([ t[i,pred[i]] for i in range(pred.shape[0])])
#print("expsum: {}, pred: {}, selected: {}".format(expsum,pred,selected))
#loss= -np.sum(np.log(np.exp(-selected/p["TAU_0"])/expsum)-p["ALPHA"]*(np.exp(selected/p["TAU_1"])-1))
loss= -np.sum(np.log(np.exp(-selected/p["TAU_0"])/expsum)-p["ALPHA"]/(1.01*p["TRIAL_MS"]-selected))
#loss= -np.sum(np.log(np.exp(-selected/p["TAU_0"])/expsum))
#print(np.sum(p["ALPHA"]/(1.05*p["TRIAL_MS"]-selected)))
loss/= p["N_BATCH"]
return loss
class yinyang:
def __init__(self, p):
self.generate_training_data(p)
self.generate_testing_data(p)
def generate_training_data(self, p):
if p["TRAIN_DATA_SEED"] is not None:
np.random.seed(p["TRAIN_DATA_SEED"])
X, self.Y_train = YinYangDataset(size=p["N_TRAIN"]*N_CLASS, flipped_coords=True, seed=p["TRAIN_DATA_SEED"])[:]
self.X_train_orig= X[:,0:2]
np.save("training_labels.npy", self.Y_train)
X= X.T
z= np.zeros(p["N_TRAIN"] * N_CLASS)
X= np.vstack([z, X])
X= np.vstack([ X[:,i:p["N_TRAIN"]*N_CLASS:p["N_BATCH"]] for i in range(p["N_BATCH"])])
chunk= (p["N_TRAIN"] * N_CLASS) // p["N_BATCH"]
offset= np.reshape(np.arange(0,chunk * p["TRIAL_MS"], p["TRIAL_MS"]),(1,chunk))
offset= np.repeat(offset,NUM_INPUT*p["N_BATCH"],axis=0)
X= X*(p["TRIAL_MS"]-4*p["DT_MS"])+offset+2*p["DT_MS"]
self.X_train= X.flatten()
self.input_end_train = np.arange(chunk,NUM_INPUT*p["N_BATCH"]*chunk+1, chunk)
self.input_end_train = np.reshape(self.input_end_train, (p["N_BATCH"], NUM_INPUT))
self.input_start_train = get_input_start(self.input_end_train)
def generate_testing_data(self, p):
if p["TEST_DATA_SEED"] is not None:
np.random.seed(p["TEST_DATA_SEED"])
X, self.Y_test = YinYangDataset(size=p["N_TEST"]*N_CLASS, flipped_coords=True, seed=p["TEST_DATA_SEED"])[:]
self.X_test_orig= X[:,0:2]
X= X.T
z= np.zeros(p["N_TEST"] * N_CLASS)
X= np.vstack([z, X])
X= np.vstack([ X[:,i:p["N_TEST"]*N_CLASS:p["N_BATCH"]] for i in range(p["N_BATCH"])])
chunk= (p["N_TEST"] * N_CLASS) // p["N_BATCH"]
offset= np.reshape(np.arange(0,chunk * p["TRIAL_MS"], p["TRIAL_MS"]),(1,chunk))
offset= np.repeat(offset,NUM_INPUT*p["N_BATCH"],axis=0)
X= X*(p["TRIAL_MS"]-4*p["DT_MS"])+offset+2*p["DT_MS"]
self.X_test= X.flatten()
self.input_end_test = np.arange(chunk,NUM_INPUT*p["N_BATCH"]*chunk+1, chunk)
self.input_end_test = np.reshape(self.input_end_test, (p["N_BATCH"], NUM_INPUT))
self.input_start_test = get_input_start(self.input_end_test)
def define_model(self, input_spike_times, output_labels, p):
input_params= {}
hidden_params= {"tau_m": p["TAU_MEM"],
"V_thresh": p["V_THRESH"],
"V_reset": p["V_RESET"],
"N_neurons": p["NUM_HIDDEN"],
"N_max_spike": p["N_MAX_SPIKE"],
}
self.hidden_init_vars= {"V": p["V_RESET"],
"lambda_V": 0.0,
"lambda_I": 0.0,
"rev_t": 0.0,
"rp_ImV": p["N_MAX_SPIKE"]-1,
"wp_ImV": 0,
"fwd_start": p["N_MAX_SPIKE"]-1,
"new_fwd_start": p["N_MAX_SPIKE"]-1,
"back_spike": 0,
"tau_syn": p["TAU_SYN"],
}
output_params= {"V_thresh": p["V_THRESH"],
"V_reset": p["V_RESET"],
"N_neurons": NUM_OUTPUT,
"N_max_spike": p["N_MAX_SPIKE"],
"trial_t": p["TRIAL_MS"],
"tau0": p["TAU_0"],
"tau1": p["TAU_1"],
"alpha": p["ALPHA"],
"N_batch": p["N_BATCH"],
}
self.output_init_vars= {"V": p["V_RESET"],
"lambda_V": 0.0,
"lambda_I": 0.0,
"rev_t": 0.0,
"rp_ImV": 0,
"wp_ImV": 0,
"back_spike": 0,
"first_spike_t": -1e5,
"new_first_spike_t": -1e5,
"exp_st": 0.0,
"expsum": 1.0,
"trial": 0,
"tau_m": p["TAU_MEM"],
"tau_syn": p["TAU_SYN"],
}
# ----------------------------------------------------------------------------
# Synapse initialisation
# ----------------------------------------------------------------------------
self.in_to_hid_init_vars= {"dw": 0}
self.in_to_hid_init_vars["w"]= genn_model.init_var("Normal", {"mean": p["INPUT_HIDDEN_MEAN"], "sd": p["INPUT_HIDDEN_STD"]})
self.hid_to_out_init_vars= {"dw": 0}
self.hid_to_out_init_vars["w"]= genn_model.init_var("Normal", {"mean": p["HIDDEN_OUTPUT_MEAN"], "sd": p["HIDDEN_OUTPUT_STD"]})
# ----------------------------------------------------------------------------
# Optimiser initialisation
# ----------------------------------------------------------------------------
adam_params = {"beta1": p["ADAM_BETA1"], "beta2": p["ADAM_BETA2"], "epsilon": p["ADAM_EPS"],}
self.adam_init_vars = {"m": 0.0, "v": 0.0}
# ----------------------------------------------------------------------------
# Model description
# ----------------------------------------------------------------------------
self.model = genn_model.GeNNModel("float", "eventprop_yinyang", generateLineInfo=True, time_precision="double")
self.model.dT = p["DT_MS"]
self.model.timing_enabled = p["TIMING"]
self.model.batch_size = p["N_BATCH"]
#self.model._model.set_seed(p["DATA_SEED"])
# Add neuron populations
self.input = self.model.add_neuron_population("input", NUM_INPUT, EVP_SSA,
{}, self.input_init_vars)
self.hidden= self.model.add_neuron_population("hidden", p["NUM_HIDDEN"], EVP_LIF, hidden_params, self.hidden_init_vars)
self.hidden.set_extra_global_param("t_k", -1e5*np.ones(p["N_BATCH"]*p["NUM_HIDDEN"]*p["N_MAX_SPIKE"], dtype=np.float32))
self.hidden.set_extra_global_param("ImV", np.zeros(p["N_BATCH"]*p["NUM_HIDDEN"]*p["N_MAX_SPIKE"], dtype=np.float32))
self.hidden.set_extra_global_param("pDrop",p["PDROP"])
self.output= self.model.add_neuron_population("output", NUM_OUTPUT, EVP_LIF_output_first_spike, output_params, self.output_init_vars)
self.output.set_extra_global_param("t_k", -1e5*np.ones(p["N_BATCH"]*NUM_OUTPUT*p["N_MAX_SPIKE"], dtype=np.float32))
self.output.set_extra_global_param("ImV", np.zeros(p["N_BATCH"]*NUM_OUTPUT*p["N_MAX_SPIKE"], dtype=np.float32))
self.input.set_extra_global_param("spikeTimes", input_spike_times)
self.output.set_extra_global_param("label", output_labels)
input_var_refs= {"startSpike": genn_model.create_var_ref(self.input, "startSpike"),
"last_startSpike": genn_model.create_var_ref(self.input, "last_startSpike"),
"back_startSpike": genn_model.create_var_ref(self.input, "back_startSpike"),
"back_endSpike": genn_model.create_var_ref(self.input, "back_endSpike"),
"back_spike": genn_model.create_var_ref(self.input, "back_spike"),
"rev_t": genn_model.create_var_ref(self.input, "rev_t")
}
self.input_reset= self.model.add_custom_update("input_reset","neuronReset", EVP_input_reset, {}, {}, input_var_refs)
hidden_var_refs= {"rp_ImV": genn_model.create_var_ref(self.hidden, "rp_ImV"),
"wp_ImV": genn_model.create_var_ref(self.hidden, "wp_ImV"),
"V": genn_model.create_var_ref(self.hidden, "V"),
"lambda_V": genn_model.create_var_ref(self.hidden, "lambda_V"),
"lambda_I": genn_model.create_var_ref(self.hidden, "lambda_I"),
"rev_t": genn_model.create_var_ref(self.hidden, "rev_t"),
"fwd_start": genn_model.create_var_ref(self.hidden, "fwd_start"),
"new_fwd_start": genn_model.create_var_ref(self.hidden, "new_fwd_start"),
"back_spike": genn_model.create_var_ref(self.hidden, "back_spike"),
}
self.hidden_reset= self.model.add_custom_update("hidden_reset","neuronReset", EVP_neuron_reset, {"V_reset": p["V_RESET"], "N_max_spike": p["N_MAX_SPIKE"]}, {}, hidden_var_refs)
output_reset_params= {"V_reset": p["V_RESET"],
"N_class": N_CLASS,
"N_max_spike": p["N_MAX_SPIKE"],
"tau0": p["TAU_0"],
"tau1": p["TAU_1"]
}
output_var_refs= {"rp_ImV": genn_model.create_var_ref(self.output, "rp_ImV"),
"wp_ImV": genn_model.create_var_ref(self.output, "wp_ImV"),
"V": genn_model.create_var_ref(self.output, "V"),
"lambda_V": genn_model.create_var_ref(self.output, "lambda_V"),
"lambda_I": genn_model.create_var_ref(self.output, "lambda_I"),
"rev_t": genn_model.create_var_ref(self.output, "rev_t"),
"back_spike": genn_model.create_var_ref(self.output, "back_spike"),
"first_spike_t": genn_model.create_var_ref(self.output, "first_spike_t"),
"new_first_spike_t": genn_model.create_var_ref(self.output, "new_first_spike_t"),
"exp_st": genn_model.create_var_ref(self.output, "exp_st"),
"expsum": genn_model.create_var_ref(self.output, "expsum"),
"trial": genn_model.create_var_ref(self.output, "trial")
}
self.output_reset= self.model.add_custom_update("output_reset","neuronReset", EVP_neuron_reset_output_yinyang_first_spike, output_reset_params, {}, output_var_refs)
# synapse populations
self.in_to_hid= self.model.add_synapse_population("in_to_hid", "DENSE_INDIVIDUALG", NO_DELAY, self.input, self.hidden, EVP_input_synapse,
{}, self.in_to_hid_init_vars, {}, {}, my_Exp_Curr, {}, {}
)
self.hid_to_out= self.model.add_synapse_population("hid_to_out", "DENSE_INDIVIDUALG", NO_DELAY, self.hidden, self.output, EVP_synapse,
{}, self.hid_to_out_init_vars, {}, {}, my_Exp_Curr, {}, {}
)
var_refs = {"dw": genn_model.create_wu_var_ref(self.in_to_hid, "dw")}
self.in_to_hid_reduce= self.model.add_custom_update("in_to_hid_reduce","EVPReduce", EVP_grad_reduce, {}, {"reduced_dw": 0.0}, var_refs)
var_refs = {"gradient": genn_model.create_wu_var_ref(self.in_to_hid_reduce, "reduced_dw"),
"variable": genn_model.create_wu_var_ref(self.in_to_hid, "w")}
self.in_to_hid_learn= self.model.add_custom_update("in_to_hid_learn","EVPLearn", adam_optimizer_model, adam_params, self.adam_init_vars, var_refs)
var_refs = {"dw": genn_model.create_wu_var_ref(self.hid_to_out, "dw")}
self.hid_to_out_reduce= self.model.add_custom_update("hid_to_out_reduce","EVPReduce", EVP_grad_reduce, {}, {"reduced_dw": 0.0}, var_refs)
var_refs = {"gradient": genn_model.create_wu_var_ref(self.hid_to_out_reduce, "reduced_dw"),
"variable": genn_model.create_wu_var_ref(self.hid_to_out, "w")}
self.hid_to_out_learn= self.model.add_custom_update("hid_to_out_learn","EVPLearn", adam_optimizer_model, adam_params, self.adam_init_vars, var_refs)
self.hid_to_out.pre_target_var= "revIsyn"
self.optimisers= [self.in_to_hid_learn, self.hid_to_out_learn]
# enable buffered spike recording where desired
for pop in p["REC_SPIKES"]:
self.model.neuron_populations[pop].spike_recording_enabled= True
"""
----------------------------------------------------------------------------
Run the model
----------------------------------------------------------------------------
"""
def run_model(self, number_epochs, learning, labels, X_t_orig, N_trial, p):
if p["LOAD_LAST"]:
self.in_to_hid.vars["w"].view[:]= np.load(os.path.join(p["OUT_DIR"], "w_input_hidden_last.npy"))
self.in_to_hid.push_var_to_device("w")
self.hid_to_out.vars["w"].view[:]= np.load(os.path.join(p["OUT_DIR"], "w_hidden_output_last.npy"))
self.hid_to_out.push_var_to_device("w")
nfst= self.output.vars["new_first_spike_t"].view
all_nfst= []
adam_step= 1
learning_rate= p["ETA"]
# set up recording if required
spike_t= {}
spike_ID= {}
for pop in p["REC_SPIKES"]:
spike_t[pop]= []
spike_ID[pop]= []
rec_vars_n= {}
for pop, var in p["REC_NEURONS"]:
rec_vars_n[var+pop]= []
rec_vars_s= {}
for pop, var in p["REC_SYNAPSES"]:
rec_vars_s[var+pop]= []
start_time= time()
for epoch in range(number_epochs):
if learning:
learning_rate *= p["ETA_DECAY"]
predict= []
the_loss= []
good= 0.0
self.model.t= 0.0
self.model.timestep= 0
for var, val in self.input_init_vars.items():
self.input.vars[var].view[:]= val
self.input.push_state_to_device()
for var, val in self.hidden_init_vars.items():
self.hidden.vars[var].view[:]= val
self.hidden.push_state_to_device()
for var, val in self.output_init_vars.items():
self.output.vars[var].view[:]= val
self.output.push_state_to_device()
for trial in range(N_trial):
trial_end= (trial+1)*p["TRIAL_MS"]
int_t= 0
while (self.model.t < trial_end-1e-3*p["DT_MS"]):
self.model.step_time()
int_t += 1
if len(p["REC_SPIKES"]) > 0:
if int_t%p["SPK_REC_STEPS"] == 0:
self.model.pull_recording_buffers_from_device()
for pop in p["REC_SPIKES"]:
the_pop= self.model.neuron_populations[pop]
if p["N_BATCH"] > 1:
spike_t[pop].append(the_pop.spike_recording_data[0][0]+epoch*N_trial*p["TRIAL_MS"])
spike_ID[pop].append(the_pop.spike_recording_data[0][1])
else:
spike_t[pop].append(the_pop.spike_recording_data[0]+epoch*N_trial*p["TRIAL_MS"])
spike_ID[pop].append(the_pop.spike_recording_data[1])
for pop, var in p["REC_NEURONS"]:
the_pop= self.model.neuron_populations[pop]
the_pop.pull_var_from_device(var)
rec_vars_n[var+pop].append(the_pop.vars[var].view[0].copy())
for pop, var in p["REC_SYNAPSES"]:
the_pop= self.model.synapse_populations[pop]
if var == "in_syn":
the_pop.pull_in_syn_from_device()
rec_vars_s[var+pop].append(the_pop.in_syn[0].copy())
else:
the_pop.pull_var_from_device(var)
rec_vars_s[var+pop].append(the_pop.vars[var].view.copy())
# clamp in_syn to 0 one timestep before trial end to avoid bleeding spikes into the next trial
if np.abs(self.model.t + p["DT_MS"] - trial_end) < 1e-1*p["DT_MS"]:
self.in_to_hid.in_syn[:]= 0.0
self.in_to_hid.push_in_syn_to_device()
self.hid_to_out.in_syn[:]= 0.0
self.hid_to_out.push_in_syn_to_device()
self.output.pull_var_from_device("new_first_spike_t");
all_nfst.append(nfst.copy())
st= nfst.copy()
valid= np.max(st, axis=-1) >= 0.0
st[nfst < 0.0]= self.model.t+p["TRIAL_MS"] # neurons that did not spike set to spike time in the future
pred= np.argmin(st, axis=-1)
predict.append(pred)
the_loss.append(loss_func(nfst, labels[trial*p["N_BATCH"]:(trial+1)*p["N_BATCH"]], trial))
good += np.sum(pred[valid] == labels[trial*p["N_BATCH"]:(trial+1)*p["N_BATCH"]][valid])
if learning:
# record training loss and error
update_adam(learning_rate, adam_step, self.optimisers)
adam_step += 1
self.model.custom_update("EVPReduce")
#if trial%2 == 1:
self.model.custom_update("EVPLearn")
self.in_to_hid.in_syn[:]= 0.0
self.in_to_hid.push_in_syn_to_device()
self.hid_to_out.in_syn[:]= 0.0
self.hid_to_out.push_in_syn_to_device()
self.model.custom_update("neuronReset")
if (epoch % p["W_EPOCH_INTERVAL"] == 0) and (trial % p["W_REPORT_INTERVAL"] == 0):
self.in_to_hid.pull_var_from_device("w")
np.save(os.path.join(p["OUT_DIR"], "w_input_hidden_e{}_t{}.npy".format(epoch,trial)), self.in_to_hid.vars["w"].view.copy())
self.hid_to_out.pull_var_from_device("w")
np.save(os.path.join(p["OUT_DIR"], "w_hidden_output_e{}_t{}.npy".format(epoch,trial)), self.hid_to_out.vars["w"].view.copy())
print("{} Correct: {}, Loss: {}".format(epoch, good/(N_trial*p["N_BATCH"]),np.mean(the_loss)))
with open(os.path.join(p["OUT_DIR"], p["NAME"]+"_results.txt"),"a") as f:
f.write(f"{epoch} {good/(N_trial*p['N_BATCH'])} {np.mean(the_loss)} {time()-start_time}\n")
start_time = time()
f.close()
predict= np.hstack(predict)
if p["TRAINING_PLOT"] and epoch%p["TRAINING_PLOT_INTERVAL"] == 0:
plt.figure()
plt.scatter(X_t_orig[:,0],X_t_orig[:,1],c=predict,s=2)
plt.show()
if p["FANCY_PLOTS"]:
pltnfst= np.vstack(all_nfst)
for i in range(3):
plt.figure()
plt.set_cmap('hot')
plt.scatter(X_t_orig[np.logical_and(predict == i, pltnfst[:,i] > 0.0),0],X_t_orig[np.logical_and(predict == i,pltnfst[:,i] > 0.0),1],s=30, color=[ 1, 1, 0.7],edgecolors= "black")
plt.xlabel("x")
plt.ylabel("y")
plt.scatter(X_t_orig[pltnfst[:,i] < 0.0,0],X_t_orig[pltnfst[:,i] < 0.0,1],s=30, c='g',marker='x')
plt.scatter(X_t_orig[np.logical_and(predict != i, pltnfst[:,i] > 0.0),0],X_t_orig[np.logical_and(predict != i, pltnfst[:,i] > 0.0),1],s=30, c=pltnfst[np.logical_and(predict != i, pltnfst[:,i] > 0.0),i],marker='x')
plt.colorbar()
plt.clim(0, 180)
plt.show()
for pop in p["REC_SPIKES"]:
spike_t[pop]= np.hstack(spike_t[pop])
spike_ID[pop]= np.hstack(spike_ID[pop])
for pop, var in p["REC_NEURONS"]:
rec_vars_n[var+pop]= np.vstack(rec_vars_n[var+pop])
for pop, var in p["REC_SYNAPSES"]:
rec_vars_s[var+pop]= np.vstack(rec_vars_s[var+pop])
# Saving results
if p["WRITE_TO_DISK"]:
for pop in p["REC_SPIKES"]:
np.save(p["OUT_DIR"]+"/"+pop+"_spike_t", spike_t[pop])
np.save(p["OUT_DIR"]+"/"+pop+"_spike_ID", spike_ID[pop])
for pop, var in p["REC_NEURONS"]:
np.save(p["OUT_DIR"]+"/"+var+pop, rec_vars_n[var+pop])
for pop, var in p["REC_SYNAPSES"]:
np.save(p["OUT_DIR"]+"/"+var+pop, rec_vars_s[var+pop])
np.save("all_nfst.npy",np.array(all_nfst))
self.in_to_hid.pull_var_from_device("w")
self.hid_to_out.pull_var_from_device("w")
np.save(os.path.join(p["OUT_DIR"], "w_input_hidden_last.npy"), self.in_to_hid.vars["w"].view.copy())
np.save(os.path.join(p["OUT_DIR"], "w_hidden_output_last.npy"), self.hid_to_out.vars["w"].view.copy())
return (spike_t, spike_ID, rec_vars_n, rec_vars_s, good/(N_trial*p["N_BATCH"]))
def train(self, p):
self.input_init_vars= {"startSpike": self.input_start_train,
"endSpike": self.input_end_train,
"last_startSpike": self.input_start_train,
"back_startSpike": self.input_start_train,
"back_endSpike":self. input_start_train,
"back_spike": 0,
"rev_t": 0.0}
self.define_model(self.X_train, self.Y_train, p)
if p["BUILD"]:
self.model.build()
self.model.load(num_recording_timesteps= p["SPK_REC_STEPS"])
N_trial= (p["N_TRAIN"] * N_CLASS) // p["N_BATCH"]
return self.run_model(p["N_EPOCH"], True, self.Y_train, self.X_train_orig, N_trial, p)
def test(self, p):
self.input_init_vars= {"startSpike": self.input_start_test,
"endSpike": self.input_end_test,
"last_startSpike": self.input_start_test,
"back_startSpike": self.input_start_test,
"back_endSpike": self.input_start_test,
"back_spike": 0,
"rev_t": 0.0}
self.define_model(self.X_test, self.Y_test, p)
if p["BUILD"]:
self.model.build()
self.model.load(num_recording_timesteps= p["SPK_REC_STEPS"])
N_trial= (p["N_TEST"] * N_CLASS) // p["N_BATCH"]
return self.run_model(1, False, self.Y_test, self.X_test_orig, N_trial, p)