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Copy pathLSM_APSTDP_CONSTRUCTOR_MNIST.py
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LSM_APSTDP_CONSTRUCTOR_MNIST.py
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import numpy as np
import time
import NALSM_LIF_v1 as neuron
import LSM_APSTDP_STDP as plasticity
import NALSM_SIM_SUPPORT as runSupport
import NALSM_OUTPUT_LAYER as out_lin_csfr
import NALSM_GEN_SUPPORT as sup
class simulation_constructor:
def __init__(self
, network_name
, network_path
, source_path
, save_path
, W_RES_EXC_MIN_MAX_IN
, W_RES_INH_MIN_MAX_IN
, W_RES_INP_MIN_MAX_IN
, W_OUT_RES_MIN_MAX_IN
, SAMPLE_INPUT_DURATION_MS_IN
, BATCH_SIZE_IN
, record_samples_in_batch_l_IN
, STDP_POTENTIATION_LR_IN
, STDP_LTP_C_MAX_IN
, STDP_LTP_C_MIN_IN
, STDP_LTD_C_MAX_IN
, STDP_LTD_C_MIN_IN
, RES_LR_DECAY_IN
, INPUT_CURRENT_IN
, READOUT_LR_IN = 0.1
, RES_INI_COST_W_SCLR_IN = -1
, SPIKE_STORE = True
, INITIALIZE_ASTRO = True
, INITIALIZE_STDP = True
, INITIALIZE_OUTPUT_LAYER=True
, INITIALIZE_RES_OPS=True
):
TAU_C_VAL = 1000.0
self.source_path = source_path
self.save_path = save_path
# _____############################################################################################______
# ####################### INITIALIZE STRUCTURES ############################################## START
# _____############################################################################################______
lifs = neuron.CUBA_LIF_network(number_of_networks_IN=BATCH_SIZE_IN)
run_sup = runSupport.run_support(number_of_networks_IN=BATCH_SIZE_IN)
# if INITIALIZE_ASTRO == True:
# astrs = astrocyte.ASTRO_LR_CONTROLLER(astro_LR_decay_factor_IN=RES_LR_DECAY_IN
# , number_of_networks_IN=BATCH_SIZE_IN
# )
if INITIALIZE_STDP == True:
stdp = plasticity.stdp(stdp_LR_fixed_potentiation=STDP_POTENTIATION_LR_IN
, res_LR_decay_factor=RES_LR_DECAY_IN
, number_of_networks_IN=BATCH_SIZE_IN
, w_res_exc_min_max=W_RES_EXC_MIN_MAX_IN
, w_res_inh_min_max=W_RES_INH_MIN_MAX_IN
, w_res_inp_min_max=W_RES_INP_MIN_MAX_IN
, w_out_res_min_max=W_OUT_RES_MIN_MAX_IN
, LTP_C_max=STDP_LTP_C_MAX_IN
, LTP_C_min=STDP_LTP_C_MIN_IN
, LTD_C_max=STDP_LTD_C_MAX_IN
, LTD_C_min=STDP_LTD_C_MIN_IN
)
if INITIALIZE_OUTPUT_LAYER==True:
olc = out_lin_csfr.output_linear_layer(output_layer_size_IN=10)
# ASSUMES MAXIMAL WEIGHT INITIALIZATION
if RES_INI_COST_W_SCLR_IN == -1:
print('INITIALIZATION NETWORKS WITH MAXIMAL WEIGHTS')
names_TF, data_TF, num_of_neurons, orig_names, orig_data = lifs.open_network_stuctures_w_custom_W_set(
net_name=network_name,
netPath=network_path,
set_w_ee_ei_ie_ii_inp_IN=[W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1]])
else:
print('INITIALIZATION NETWORKS WITH SCALR ABS W: '+str(RES_INI_COST_W_SCLR_IN))
names_TF, data_TF, num_of_neurons, orig_names, orig_data = lifs.open_network_stuctures_w_custom_W_set(
net_name=network_name,
netPath=network_path,
set_w_ee_ei_ie_ii_inp_IN=[RES_INI_COST_W_SCLR_IN, RES_INI_COST_W_SCLR_IN, RES_INI_COST_W_SCLR_IN,
RES_INI_COST_W_SCLR_IN, RES_INI_COST_W_SCLR_IN])
self.TAU_U_DENSE = data_TF[names_TF.index('TAU_U_DENSE')]
self.W_DENSE = data_TF[names_TF.index('W_DENSE')]
self.SS_T_STATE = data_TF[names_TF.index('SS_T_STATE')]
self.TAU_V = data_TF[names_TF.index('TAU_V')]
self.V_THRSH = data_TF[names_TF.index('V_THRSH')]
self.B = data_TF[names_TF.index('B')]
self.V = data_TF[names_TF.index('V')]
self.S = data_TF[names_TF.index('S')]
self.T_RFR = data_TF[names_TF.index('T_RFR')]
self.T_RFR_STATE = data_TF[names_TF.index('T_RFR_STATE')]
self.I_PH = data_TF[names_TF.index('I_PH')]
self.INPUT_POISSON_VALS = data_TF[names_TF.index('INPUT_POISSON_VALS')]
self.ss_t_state_reset_scalar = data_TF[names_TF.index('ss_t_state_reset_scalar')]
neuron_ranges_dict = dict(orig_data[orig_names.index('neuron_ranges')])
self.res_range = neuron_ranges_dict['res_range']
self.res_exc_range = neuron_ranges_dict['res_exc_range']
self.res_inh_range = neuron_ranges_dict['res_inh_range']
self.inp_range = neuron_ranges_dict['inp_range']
self.out_range = neuron_ranges_dict['out_range']
self.w_mask_np = orig_data[orig_names.index('W_mask')]
self.num_total_neurons = np.shape(self.w_mask_np)[0]
# num_output_neurons = out_range[1] - out_range[0]
self.num_res_neurons = self.res_range[1] - self.res_range[0]
self.num_inp_neurons = self.inp_range[1] - self.inp_range[0]
self.num_total_syns = len(np.where(self.w_mask_np == 1.0)[0])
print('num of neurons in network: ' + str(self.num_total_neurons))
print('num of res neurons in network: ' + str(self.num_res_neurons))
print('num of input neurons in network: ' + str(self.num_inp_neurons))
print('num of syns in network: ' + str(self.num_total_syns))
ini_names, ini_data = run_sup.simulation_initializer(inp_range_IN=self.inp_range
, res_exc_range_IN=self.res_exc_range
, res_inh_range_IN=self.res_inh_range
, res_range_IN=self.res_range
, out_range_IN=self.out_range
, w_mask_sparse_single_net_np_IN=self.w_mask_np
, record_nets_l_IN=record_samples_in_batch_l_IN
, num_neurons_in_network_IN=self.num_total_neurons
, input_sample_duration_IN=SAMPLE_INPUT_DURATION_MS_IN
)
scatter_idx_w_dense_to_s_out_main_tf = ini_data[ini_names.index('scatter_idx_w_dense_to_s_out_main_tf')]
scatter_S_to_S_SUBSET_inputNeurons_tf = ini_data[ini_names.index('scatter_S_to_S_SUBSET_inputNeurons_tf')]
gather_idx_Sin_to_Wdense_tf = ini_data[ini_names.index('gather_idx_Sin_to_Wdense_tf')]
# save_spikes_gath_ind_tf = ini_data[ini_names.index('save_spikes_gath_ind_tf')]
# save_spikes_scatter_inds_l_of_tfs = ini_data[ini_names.index('save_spikes_scatter_inds_l_of_tfs')]
gather_idx_Sout_to_Wdense_tf = ini_data[ini_names.index('gather_idx_Sout_to_Wdense_tf')]
S_exc_and_inp_mask_np = ini_data[ini_names.index('S_exc_and_inp_mask_np')]
S_inh_mask_np = ini_data[ini_names.index('S_inh_mask_np')]
w_dense_exc_to_res_mask_np = ini_data[ini_names.index('w_dense_exc_to_res_mask_np')]
w_dense_inh_to_res_mask_np = ini_data[ini_names.index('w_dense_inh_to_res_mask_np')]
w_dense_inp_to_res_mask_np = ini_data[ini_names.index('w_dense_inp_to_res_mask_np')]
w_dense_res_to_out_mask_np = ini_data[ini_names.index('w_dense_res_to_out_mask_np')]
net_masks_names, net_masks_data = lifs.initialize_network_masks(num_total_neurons_IN=self.num_total_neurons
, num_synapses_in_network_IN=self.num_total_syns
, S_mask_exc_inp_np_IN=S_exc_and_inp_mask_np
, S_mask_inh_np_IN=S_inh_mask_np
,
W_dense_mask_exc_to_res_np_IN=w_dense_exc_to_res_mask_np
,
W_dense_mask_inh_to_res_np_IN=w_dense_inh_to_res_mask_np
,
W_dense_mask_inp_to_res_np_IN=w_dense_inp_to_res_mask_np
,
W_dense_mask_res_to_out_np_IN=w_dense_res_to_out_mask_np
)
S_MASK_EXC_INP = net_masks_data[net_masks_names.index('S_MASK_EXC_INP')]
S_MASK_INH = net_masks_data[net_masks_names.index('S_MASK_INH')]
W_DENSE_MASK_EXC_TO_RES = net_masks_data[net_masks_names.index('W_DENSE_MASK_EXC_TO_RES')]
W_DENSE_MASK_INH_TO_RES = net_masks_data[net_masks_names.index('W_DENSE_MASK_INH_TO_RES')]
W_DENSE_MASK_INP_TO_RES = net_masks_data[net_masks_names.index('W_DENSE_MASK_INP_TO_RES')]
W_DENSE_MASK_RES_TO_OUT = net_masks_data[net_masks_names.index('W_DENSE_MASK_RES_TO_OUT')]
if SPIKE_STORE == True:
spike_save_names, spike_save_data = lifs.initialize_spike_store(s_IN=self.S
, input_duration_IN=SAMPLE_INPUT_DURATION_MS_IN
, record_nets_l_IN=record_samples_in_batch_l_IN
,
num_neurons_in_single_network_IN=self.num_total_neurons
)
self.spike_save_ops = spike_save_data[spike_save_names.index('spike_save_ops')]
self.zero_out_S_STORE_op = spike_save_data[spike_save_names.index('zero_out_S_STORE_op')]
self.condense_spike_store_to_Fn_Ft_op = spike_save_data[spike_save_names.index('condense_spike_store_to_Fn_Ft_op')]
w_store_names, w_store_data = lifs.initialize_W_DENSE_store(W_DENSE_IN=self.W_DENSE
, num_synapses_in_network_IN=self.num_total_syns
)
self.W_DENSE_STORE = w_store_data[w_store_names.index('W_DENSE_STORE')]
self.save_W_op = w_store_data[w_store_names.index('save_W_op')]
self.reset_W_from_saved_state_op = w_store_data[w_store_names.index('reset_W_from_saved_state_op')]
# if INITIALIZE_ASTRO == True:
# astro_names, astro_data = astrs.astro_initializer(num_total_neurons_IN=self.num_total_neurons
# , inp_range_IN=self.inp_range
# , res_range_IN=self.res_range
# , a_bias_offset_percentage_IN=ASTRO_BIAS_OFFSET_PERCENTAGE_IN
# , w_scaling_IN=ASTRO_W_SCALING_IN
# , a_initial_val_IN=STDP_POTENTIATION_LR_IN
# , a_tau_IN=ASTRO_TAU_IN
# )
#
# self.ASTRO_STDP_LR = astro_data[astro_names.index('ASTRO_STDP_LR')]
# self.ASTRO_W = astro_data[astro_names.index('ASTRO_W')]
# self.ASTRO_BIAS = astro_data[astro_names.index('ASTRO_BIAS')]
# self.ASTRO_STDP_LR_STORE = astro_data[astro_names.index('ASTRO_STDP_LR_STORE')]
# self.ASTRO_BIAS_STORE = astro_data[astro_names.index('ASTRO_BIAS_STORE')]
# self.ASTRO_W_STORE = astro_data[astro_names.index('ASTRO_W_STORE')]
# self.ASTRO_W_MASK = astro_data[astro_names.index('ASTRO_W_MASK')]
# self.ASTRO_TAU = astro_data[astro_names.index('ASTRO_TAU')]
if INITIALIZE_STDP == True:
stdp_names, stdp_data = stdp.initialize_learning_parameters_v0(num_total_neurons_IN=self.num_total_neurons)
self.STDP_TRACE = stdp_data[stdp_names.index('STDP_TRACE')]
self.STDP_POTENTIATION_LR = stdp_data[stdp_names.index('STDP_POTENTIATION_LR')]
self.STDP_POTENTIATION_LR_STORE = stdp_data[stdp_names.index('STDP_POTENTIATION_LR_STORE')]
self.C_RES = stdp_data[stdp_names.index('C_RES')]
if INITIALIZE_OUTPUT_LAYER == True:
olc_names, olc_data = olc.initialize_layer_structures(num_res_neurons_IN=self.num_res_neurons
, initial_batch_size_IN=BATCH_SIZE_IN
)
self.olc_label_Inp = olc_data[olc_names.index('label_Inp')]
self.olc_target_output_ph = olc_data[olc_names.index('target_output_ph')]
self.olc_W1_ph = olc_data[olc_names.index('W1_ph')]
self.olc_b1_ph = olc_data[olc_names.index('b1_ph')]
self.olc_W1 = olc_data[olc_names.index('W1')]
self.olc_b1 = olc_data[olc_names.index('b1')]
self.olc_BS = olc_data[olc_names.index('BS')]
self.olc_S_AGG_RES = olc_data[olc_names.index('S_AGG_RES')]
self.olc_S_AGG_RES_PH = olc_data[olc_names.index('S_AGG_RES_PH')]
# _____############################################################################################______
# ####################### INITIALIZE STRUCTURES ############################################## END
# _____############################################################################################______
# _____############################################################################################______
# ####################### INITIALIZE TF OPS ############################################## START
# _____############################################################################################______
# NEURON OPS
if INITIALIZE_RES_OPS == True:
self.LIF_update_neuron_states_op = lifs.update_neuron_states_wo_R(v_IN=self.V
, tau_v_IN=self.TAU_V
, tau_u_dense_IN=self.TAU_U_DENSE
, b_IN=self.B
, ss_t_state_IN=self.SS_T_STATE
, W_dense_IN=self.W_DENSE
, I_poisson_IN=self.INPUT_POISSON_VALS
,
scatter_idx_W_dense_to_Neurons_IN=scatter_idx_w_dense_to_s_out_main_tf
, scatter_shp_IN=[BATCH_SIZE_IN, self.num_total_neurons]
,
scatter_S_to_S_SUBSET_inputNeurons_IN=scatter_S_to_S_SUBSET_inputNeurons_tf
, batch_by_num_input_neurons_l_IN=[BATCH_SIZE_IN,
self.num_inp_neurons]
, input_current_IN=INPUT_CURRENT_IN
)
self.LIF_propagate_spike_op = lifs.propagate_spikes(spikes_IN=self.S
, ss_t_state_IN=self.SS_T_STATE
, gather_idx_Sin_to_Wdense_IN=gather_idx_Sin_to_Wdense_tf)
self.LIF_evolveCurr_op = lifs.evolve_input_currents(ss_t_state_IN=self.SS_T_STATE)
self.LIF_compute_spikes_op = lifs.register_spikes(v_IN=self.V, v_trsh_IN=self.V_THRSH, s_IN=self.S, t_rfr_state_IN=self.T_RFR_STATE)
self.LIF_reset_v_op = lifs.reset_spiked_neurons(s_IN=self.S, v_IN=self.V)
self.LIF_update_refractory_state_op = lifs.update_refractory_var(t_rfr_IN=self.T_RFR, t_rfr_state_IN=self.T_RFR_STATE, s_IN=self.S)
self.LIF_update_input_poisson_vals_op = lifs.set_new_input_batch(I_PH_IN=self.I_PH
, INPUT_POISSON_VALS_IN=self.INPUT_POISSON_VALS)
self.LIF_reset_V_to_zero_op = lifs.zero_out_V(V_IN=self.V)
self.LIF_reset_S_to_zero_op = lifs.zero_out_S(S_IN=self.S)
self.LIF_reset_T_RFR_STATE_to_zero_op = lifs.zero_out_T_RFR_STATE(T_RFR_STATE_IN=self.T_RFR_STATE)
self.LIF_reset_SS_T_STATE_op = lifs.reset_ss_t_state_op(SS_T_STATE_IN=self.SS_T_STATE
, ss_t_state_reset_scalar_IN=self.ss_t_state_reset_scalar)
# ASTRO OPS
# if INITIALIZE_ASTRO == True:
# self.update_astro_op = astrs.update_astro_state(ASTRO_STDP_LR_IN=self.ASTRO_STDP_LR
# , ASTRO_BIAS_IN=self.ASTRO_BIAS
# , ASTRO_W_IN=self.ASTRO_W
# , ASTRO_W_MASK_IN=self.ASTRO_W_MASK
# , S_IN=self.S
# , ASTRO_TAU_IN=self.ASTRO_TAU)
#
# self.ASTRO_decay_astro_res_lr_op = astrs.decay_astro_stdp_lr(astro_lr_IN=self.ASTRO_STDP_LR)
# self.ASTRO_decay_astro_w_op = astrs.decay_astro_w(astro_w_IN=self.ASTRO_W)
# self.ASTRO_decay_astro_bias_op = astrs.decay_astro_bias(astro_bias_IN=self.ASTRO_BIAS)
#
# self.ASTRO_reset_astro_res_lr_op = astrs.reset_astro_stdp_lr(astro_lr_IN=self.ASTRO_STDP_LR,
# astro_lr_store_IN=self.ASTRO_STDP_LR_STORE)
# self.ASTRO_reset_astro_w_op = astrs.reset_astro_w(astro_w_IN=self.ASTRO_W, astro_w_store_IN=self.ASTRO_W_STORE)
# self.ASTRO_reset_astro_bias_op = astrs.reset_astro_bias(astro_bias_IN=self.ASTRO_BIAS,
# astro_bias_store_IN=self.ASTRO_BIAS_STORE)
# STDP OPS
if INITIALIZE_STDP == True:
self.PLASTICITY_update_stdp_only_op = stdp.APSTDP(
W_dense_IN=self.W_DENSE
, S_IN=self.S
, C_RES_IN=self.C_RES
# STDP INPUTS
, trace_IN=self.STDP_TRACE
, W_dense_mask_exc_to_res_IN=W_DENSE_MASK_EXC_TO_RES
, W_dense_mask_inh_to_res_IN=W_DENSE_MASK_INH_TO_RES
, W_dense_mask_inp_to_res_IN=W_DENSE_MASK_INP_TO_RES
, W_dense_mask_res_to_out_IN=W_DENSE_MASK_RES_TO_OUT
, S_mask_exc_and_inp_IN=S_MASK_EXC_INP
, S_mask_inh_IN=S_MASK_INH
# , astro_LR_IN=self.ASTRO_STDP_LR
, gather_idx_Sin_to_Wdense_IN=gather_idx_Sin_to_Wdense_tf
, gather_idx_Sout_to_Wdense_IN=gather_idx_Sout_to_Wdense_tf
, stdp_potentiation_LR_IN=self.STDP_POTENTIATION_LR
)
self.PLASTICITY_update_trace_op = stdp.update_trace(trace_IN=self.STDP_TRACE, S_IN=self.S)
self.PLASTICITY_decay_res_lr_op = stdp.decay_res_lr(res_lr_IN=self.STDP_POTENTIATION_LR)
self.PLASTICITY_reset_res_lr_op = stdp.reset_res_lr(res_lr_IN=self.STDP_POTENTIATION_LR,
res_lr_store_IN=self.STDP_POTENTIATION_LR_STORE)
self.PLASTICITY_update_C_VAR_op = stdp.update_calcium_rate_measures(TAU_C_IN=TAU_C_VAL, C_RES_IN=self.C_RES, S_IN=self.S)
# output layer ops
if INITIALIZE_OUTPUT_LAYER == True:
olc_ops_names, olc_ops_data = olc.initialize_layer_ops(S_AGG_RES_IN=self.olc_S_AGG_RES
, label_Inp_IN=self.olc_label_Inp
, target_output_ph_IN=self.olc_target_output_ph
, W1_IN=self.olc_W1
, b1_IN=self.olc_b1
, BS_IN=self.olc_BS
, lr_IN=READOUT_LR_IN
)
self.OLC_train_step_op = olc_ops_data[olc_ops_names.index('train_step')]
self.OLC_accuracy_eval_per_batch_op = olc_ops_data[olc_ops_names.index('accuracy_eval_per_batch')]
self.OLC_agg_spikes_op = olc.aggregate_spikes_for_output_layer_op(S_AGG_RES_IN=self.olc_S_AGG_RES
, S_IN=self.S
, res_range_IN=self.res_range
)
self.OLC_zero_out_S_AGG_RES_op = olc.zero_out_S_AGG_RES_op(S_AGG_RES_IN=self.olc_S_AGG_RES)
self.OLC_set_S_AGG_to_previously_saved_batch_op = olc.assign_previously_saved_S_AGG(S_AGG_RES_IN=self.olc_S_AGG_RES,S_AGG_RES_PH_IN=self.olc_S_AGG_RES_PH)
w_ini_names, w_ini_data = olc.initialize_saved_output_layer_weights(W_IN=self.olc_W1
, b_IN=self.olc_b1
, new_W_IN=self.olc_W1_ph
, new_b_IN=self.olc_b1_ph
)
self.OLC_set_new_W_op = w_ini_data[w_ini_names.index('set_new_W_op')]
self.OLC_set_new_b_op = w_ini_data[w_ini_names.index('set_new_b_op')]
# _____############################################################################################______
# ####################### INITIALIZE TF OPS ############################################## END
# _____############################################################################################______
#########################################################################################################
############################## INITIALIZE BATCH NETWORK ############################################# END
#########################################################################################################
################### VAL/TST GENERATE OPS ################################################################# START
def generate_validation_or_test_spike_data_STATIC(self
, sess_IN
, saver_object_IN
, valid_or_test_data_INPUT_VEC_IN
, valid_or_test_data_LABEL_VEC_IN
, valid_or_test_data_LABEL_SCALAR_IN
, BATCH_SIZE_IN
, SAMPLE_INPUT_DURATION_MS_IN
, NUM_DATA_SAMPLES_IN
, spike_store_batch_mod_IN
, record_samples_in_batch_l_IN
, root_save_name_IN):
'''
GENERATES VALIDATION/TEST SPIKE COUNT DATA FOR LSM+APSTDP MODEL
:param sess_IN: TF SESSION
:param saver_object_IN: OJECT FOR SAVING DATA
:param valid_or_test_data_idx_l_IN: INPUT DATA INDEXS
:param BATCH_SIZE_IN: SIZE OF BATCH
:param SAMPLE_INPUT_DURATION_MS_IN: TIME OF PRESENTING EACH SAMPLE TO LIQUID
:param NUM_DATA_SAMPLES_IN: TOTAL NUMBER OF TETS OR VALIDATION SAMPLES
:param spike_store_batch_mod_IN: WHICH BATCH TO STORE SPIKE DATA FROM
:param record_samples_in_batch_l_IN: SAMPLES TO STORE SPIKE DATA FROM IN BATCH
:param root_save_name_IN: SAVE NAME
:param data_type_IN: DATA PARTITION (TRAIN, VALIDATION, TEST)
:return:
'''
ITERATIONS = int(np.floor(NUM_DATA_SAMPLES_IN/BATCH_SIZE_IN))
print('VALIDATION/TESTING RUNNING FOR '+str(ITERATIONS)+' ITERATIONS WITH BATCH SIZE: '+str(BATCH_SIZE_IN))
spikes_files_l = []
s_agg_files_l = []
for ITER in range(0, ITERATIONS):
if ITER > 0:
print('VAL/TST Epoch ' + str(ITER - 1) + ' competed in ' + str(time.time() - st_time))
st_time = time.time()
scalar_labels_in_batch_for_spike_data_l = []
batch_samples_agg_temp = []
scalar_labels_in_batch_l = []
target_output_for_linear_layer_labels_in_batch_l = []
for bt in range(0, BATCH_SIZE_IN):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt)
batch_samples_agg_temp.append(valid_or_test_data_INPUT_VEC_IN[sample_idx])
target_output_for_linear_layer_labels_in_batch_l.append(valid_or_test_data_LABEL_VEC_IN[sample_idx])
scalar_labels_in_batch_l.append(valid_or_test_data_LABEL_SCALAR_IN[sample_idx])
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN-1:
for bt_s in range(0,len(record_samples_in_batch_l_IN)):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt_s)
scalar_labels_in_batch_for_spike_data_l.append(valid_or_test_data_LABEL_SCALAR_IN[sample_idx])
sess_IN.run(self.LIF_update_input_poisson_vals_op, feed_dict={self.I_PH: np.vstack(batch_samples_agg_temp)})
sess_IN.run(self.LIF_reset_V_to_zero_op)
sess_IN.run(self.LIF_reset_S_to_zero_op)
sess_IN.run(self.LIF_reset_T_RFR_STATE_to_zero_op)
sess_IN.run(self.LIF_reset_SS_T_STATE_op)
# sess_IN.run(self.ASTRO_reset_astro_res_lr_op)
# sess_IN.run(self.ASTRO_reset_astro_w_op)
# sess_IN.run(self.ASTRO_reset_astro_bias_op)
# sess_IN.run(self.PLASTICITY_reset_res_lr_op)
sess_IN.run(self.reset_W_from_saved_state_op)
sess_IN.run(self.OLC_zero_out_S_AGG_RES_op)
for t in range(0, SAMPLE_INPUT_DURATION_MS_IN):
############### LIF FUNCTIONS ###################### START
sess_IN.run(self.LIF_compute_spikes_op)
sess_IN.run(self.LIF_reset_v_op)
# SPIKE RECORD OP
if ITER%spike_store_batch_mod_IN==spike_store_batch_mod_IN-1:
sess_IN.run(self.spike_save_ops[t])
sess_IN.run(self.OLC_agg_spikes_op)
sess_IN.run(self.LIF_propagate_spike_op)
sess_IN.run(self.LIF_update_neuron_states_op)
sess_IN.run(self.LIF_evolveCurr_op)
sess_IN.run(self.LIF_update_refractory_state_op)
############### MAIN LIF FUNCTIONS ###################### END
############### PLASTICITY FUNCTIONS ###################### START
# sess_IN.run(self.PLASTICITY_update_stdp_only_op)
# sess_IN.run(self.PLASTICITY_update_trace_op)
############### PLASTICITY FUNCTIONS ###################### END
############### ASTRO FUNCTIONS ########################## START
# sess_IN.run(self.update_astro_op)
############### ASTRO FUNCTIONS ############################ END
############### PLASTICITY ASTRO DECAYS ###################### START
# sess_IN.run(self.PLASTICITY_decay_res_lr_op)
# sess_IN.run(self.ASTRO_decay_astro_res_lr_op)
# sess_IN.run(self.ASTRO_decay_astro_w_op)
# sess_IN.run(self.ASTRO_decay_astro_bias_op)
############### PLASTICITY ASTRO DECAYS ###################### END
################### EXTRACT SPIKE DATA ############### START
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN-1:
spike_rec_per_batch = sess_IN.run(self.condense_spike_store_to_Fn_Ft_op)
sess_IN.run(self.zero_out_S_STORE_op)
spikes_save_filename = str(root_save_name_IN) + '_SPIKES_BATCH_' + str(ITER) + '.spikes'
saver_object_IN.save_data(signal=1
, names=['ITER','spike_rec_per_batch', 'scalar_labels_in_batch_for_spike_data_l']
, data=[ITER,spike_rec_per_batch, scalar_labels_in_batch_for_spike_data_l]
, save_filename=spikes_save_filename
)
spike_rec_per_batch = 0
scalar_labels_in_batch_for_spike_data_l.clear()
spikes_files_l.append(spikes_save_filename)
################### EXTRACT SPIKE DATA ############### END
s_agg_save_filename = str(root_save_name_IN) + '_S_AGG_BATCH_' + str(ITER) + '.datasagg'
saver_object_IN.save_data(signal=1
, names=['ITER','olc_S_AGG_RES', 'target_output_for_linear_layer_labels_in_batch_arr','scalar_labels_in_batch_arr']
, data=[ITER,sess_IN.run(self.olc_S_AGG_RES), np.vstack(target_output_for_linear_layer_labels_in_batch_l),np.asarray(scalar_labels_in_batch_l)]
, save_filename=s_agg_save_filename
)
s_agg_files_l.append(s_agg_save_filename)
target_output_for_linear_layer_labels_in_batch_l.clear()
scalar_labels_in_batch_l.clear()
return spikes_files_l,s_agg_files_l
################### VAL/TST GENERATE OPS ################################################################# END
def evaluate_output_layer_on_validation_or_test_s_agg_data(self
, sess_IN
, data_batched_INPUT_VEC_IN
, data_batched_LABEL_VEC_IN
, data_batched_LABEL_SCALAR_IN
):
'''
EVALUATES OUTPUT LAYER ON SINGLE BATCH OF DATA
:param sess_IN: TF SESSION
:param data_batched_INPUT_VEC_IN: INPUT DATA
:param data_batched_LABEL_VEC_IN: OUTPUT DATA ONE HOT FORMAT
:param data_batched_LABEL_SCALAR_IN: SCALAR LABELS
:return:
'''
accuracy_l = []
for batch in range(0,len(data_batched_INPUT_VEC_IN)):
sess_IN.run(self.OLC_set_S_AGG_to_previously_saved_batch_op, feed_dict={self.olc_S_AGG_RES_PH:data_batched_INPUT_VEC_IN[batch]})
accuracy_l.append(sess_IN.run(self.OLC_accuracy_eval_per_batch_op, feed_dict={self.olc_label_Inp:data_batched_LABEL_SCALAR_IN[batch]}))
return accuracy_l
def evaluate_output_layer_on_FULL_validation_or_test_s_agg_data(self
, sess_IN
, saver_object_IN
, list_of_data_files_IN
, num_of_simultaneous_open_files=2000):
'''
EVALUATES PERFORMANCE OF OUTPUT LAYER ON VALIDATION OR TEST DATA
:param sess_IN: TF SESSION
:param saver_object_IN: OBJECT FOR SAVING DATA
:param list_of_data_files_IN: SPECIFIES DATA TO TEST/VALIDATE ON
:param num_of_simultaneous_open_files: NUMBER OF BATCHES TO LOAD AT ONE TIME
:return:
'''
accuracy_list_full = []
data_batched_INPUT_VEC_l = []
data_batched_LABEL_VEC_l = []
data_batched_LABEL_SCALAR_l = []
for fn in range(0,len(list_of_data_files_IN)):
names, data = sup.unpack_file(filename=list_of_data_files_IN[fn],dataPath=self.source_path)
data_batched_INPUT_VEC_l.append(data[names.index('olc_S_AGG_RES')])
data_batched_LABEL_VEC_l.append(data[names.index('target_output_for_linear_layer_labels_in_batch_arr')])
data_batched_LABEL_SCALAR_l.append(data[names.index('scalar_labels_in_batch_arr')])
if len(data_batched_INPUT_VEC_l)==num_of_simultaneous_open_files or (fn==(len(list_of_data_files_IN)-1) and len(data_batched_INPUT_VEC_l)>0):
accuracy_list_full.extend(self.evaluate_output_layer_on_validation_or_test_s_agg_data(sess_IN=sess_IN
, data_batched_INPUT_VEC_IN=data_batched_INPUT_VEC_l
, data_batched_LABEL_VEC_IN=data_batched_LABEL_VEC_l
, data_batched_LABEL_SCALAR_IN=data_batched_LABEL_SCALAR_l
))
data_batched_INPUT_VEC_l.clear()
data_batched_LABEL_VEC_l.clear()
data_batched_LABEL_SCALAR_l.clear()
if len(accuracy_list_full)==len(list_of_data_files_IN):
print('ALL EVALUATION FILES PROCESSED AND ACCOUNTED FOR TOTALING: '+str(len(list_of_data_files_IN)))
else:
print('ERROR: NOT ALL EVALUTATION FILES PROCEED AND ACCOUNTED, ACCOUNTED ONLY: '+str(len(accuracy_list_full))+' out of '+str(len(list_of_data_files_IN)))
average_accuracy = np.average(accuracy_list_full)
return average_accuracy
################### TRAINING OPS ######################################################################## START
def train_on_set_number_of_batches_STATIC(self
, sess_IN
, saver_object_IN
, training_iteration_range_IN
, training_dataset_size_IN
, list_of_data_files_IN
, BATCH_SIZE_IN
, root_save_name_IN
, train_data_INPUT_VEC_IN
, train_data_LABEL_VEC_IN
, train_data_LABEL_SCALAR_IN
, spike_store_batch_mod_IN
, record_samples_in_batch_l_IN
, SAMPLE_INPUT_DURATION_MS_IN
, REPEATS_ON_ITER_RANGE_IN=1
):
'''
GENERATES SPIKE COUNT DATA FOR EACH SAMPLE AND TRAINS THE OUTPUT LAYER ONCE ON ALL GENERATED BATCHS
THIS FUNCTION USED FOR LSM+APSTDP MODEL, WITH STATIC/FIXED WEIGHTS
:param sess_IN: TF SESSION
:param saver_object_IN: OBJECT FOR SAVING FILES
:param training_iteration_range_IN: BATCHES TO GENERATE AND TRAIN
:param training_dataset_size_IN: FULL SIZE OF TRAINING SET
:param list_of_data_files_IN: ALREADY GENERATED SPIKE COUNT FILES (S_AGG FILES)
:param BATCH_SIZE_IN: BATCH SIZE
:param root_save_name_IN: CORE SAVE NAME
:param train_data_INPUT_VEC_IN: INPUT DATA
:param train_data_LABEL_VEC_IN: LABELS IN FORMAT FOR TRAINING OUTPUT LAYER(ONE HOT VECTOR)
:param train_data_LABEL_SCALAR_IN: RAW LABELS (0,1,2,3,4..)
:param spike_store_batch_mod_IN: WHEN TO STORE ALL SPIKES FROM LIQUID
:param record_samples_in_batch_l_IN: WHICH SAMPLES TO STORE SPIKE DATA FROM IN BATCH
:param SAMPLE_INPUT_DURATION_MS_IN: DURATION OF SAMPLE INPUT PRESENTED TO LIQUID (250 ms)
:param REPEATS_ON_ITER_RANGE_IN: ALWAYS 1
:return:
'''
max_num_of_save_files = int(training_dataset_size_IN/BATCH_SIZE_IN)
print('max number of training data save files: '+str(max_num_of_save_files))
raw_iters_arr = training_iteration_range_IN[0]+np.arange(training_iteration_range_IN[1]-training_iteration_range_IN[0])
all_iters_arr = np.mod(raw_iters_arr,max_num_of_save_files)
idx = np.where(raw_iters_arr< max_num_of_save_files)[0]
iters_with_no_existing_data_l = raw_iters_arr[idx]
print('-----------------------------------: '+str(raw_iters_arr))
print('------------------------all_iters_l: ' + str(all_iters_arr))
print('-----------idx for no_existing_data: '+str(idx))
print('------iters_with_no_existing_data_l: ' + str(iters_with_no_existing_data_l))
spikes_files_l = []
s_agg_files_l = []
st_time_train_total = time.time()
if len(iters_with_no_existing_data_l)>0:
print('PROCEEDING TO GENERATE NEW S AGG RES DATA')
for ITER in iters_with_no_existing_data_l:
print('SPIKE DATA NEEDS TO BE GENERATED, NOW GENERATING SPIKE DATA FOR BATCH: ' + str(ITER))
# iter_ned is the batch number or the ITER from eval function
# edit the iter time printing....
if ITER > iters_with_no_existing_data_l[0]:
print('Epoch ' + str(ITER - 1) + ' competed in ' + str(time.time() - st_time))
st_time = time.time()
scalar_labels_in_batch_for_spike_data_l = []
batch_samples_agg_temp = []
scalar_labels_in_batch_l = []
target_output_for_linear_layer_labels_in_batch_l = []
for bt in range(0, BATCH_SIZE_IN):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt)
batch_samples_agg_temp.append(train_data_INPUT_VEC_IN[sample_idx])
target_output_for_linear_layer_labels_in_batch_l.append(train_data_LABEL_VEC_IN[sample_idx])
scalar_labels_in_batch_l.append(train_data_LABEL_SCALAR_IN[sample_idx])
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN - 1:
for bt_s in range(0, len(record_samples_in_batch_l_IN)):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt_s)
scalar_labels_in_batch_for_spike_data_l.append(train_data_LABEL_SCALAR_IN[sample_idx])
sess_IN.run(self.LIF_update_input_poisson_vals_op,
feed_dict={self.I_PH: np.vstack(batch_samples_agg_temp)})
sess_IN.run(self.LIF_reset_V_to_zero_op)
sess_IN.run(self.LIF_reset_S_to_zero_op)
sess_IN.run(self.LIF_reset_T_RFR_STATE_to_zero_op)
sess_IN.run(self.LIF_reset_SS_T_STATE_op)
# sess_IN.run(self.ASTRO_reset_astro_res_lr_op)
# sess_IN.run(self.ASTRO_reset_astro_w_op)
# sess_IN.run(self.ASTRO_reset_astro_bias_op)
# sess_IN.run(self.PLASTICITY_reset_res_lr_op)
sess_IN.run(self.reset_W_from_saved_state_op)
sess_IN.run(self.OLC_zero_out_S_AGG_RES_op)
for t in range(0, SAMPLE_INPUT_DURATION_MS_IN):
############### LIF FUNCTIONS ###################### START
sess_IN.run(self.LIF_compute_spikes_op)
sess_IN.run(self.LIF_reset_v_op)
# SPIKE RECORD OP
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN - 1:
sess_IN.run(self.spike_save_ops[t])
sess_IN.run(self.OLC_agg_spikes_op)
sess_IN.run(self.LIF_propagate_spike_op)
sess_IN.run(self.LIF_update_neuron_states_op)
sess_IN.run(self.LIF_evolveCurr_op)
sess_IN.run(self.LIF_update_refractory_state_op)
############### MAIN LIF FUNCTIONS ###################### END
############### PLASTICITY FUNCTIONS ###################### START
# sess_IN.run(self.PLASTICITY_update_stdp_only_op)
# sess_IN.run(self.PLASTICITY_update_trace_op)
############### PLASTICITY FUNCTIONS ###################### END
############### ASTRO FUNCTIONS ########################## START
# sess_IN.run(self.update_astro_op)
############### ASTRO FUNCTIONS ############################ END
############### PLASTICITY ASTRO DECAYS ###################### START
# sess_IN.run(self.PLASTICITY_decay_res_lr_op)
# sess_IN.run(self.ASTRO_decay_astro_res_lr_op)
# sess_IN.run(self.ASTRO_decay_astro_w_op)
# sess_IN.run(self.ASTRO_decay_astro_bias_op)
############### PLASTICITY ASTRO DECAYS ###################### END
################### EXTRACT SPIKE DATA ############### START
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN - 1:
spike_rec_per_batch = sess_IN.run(self.condense_spike_store_to_Fn_Ft_op)
sess_IN.run(self.zero_out_S_STORE_op)
saver_object_IN.save_data(signal=1
, names=['ITER', 'spike_rec_per_batch',
'scalar_labels_in_batch_for_spike_data_l']
, data=[ITER, spike_rec_per_batch,
scalar_labels_in_batch_for_spike_data_l]
, save_filename=str(root_save_name_IN) + '_BATCH_' + str(
ITER) + '.spikes'
)
spike_rec_per_batch = 0
scalar_labels_in_batch_for_spike_data_l.clear()
spikes_files_l.append(str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.spikes')
################### EXTRACT SPIKE DATA ############### END
################### SAVE SPIKE COUNT DATA ############### START
if ITER<iters_with_no_existing_data_l[-1]:
saver_object_IN.save_data(signal=1
, names=['ITER', 'olc_S_AGG_RES',
'target_output_for_linear_layer_labels_in_batch_arr',
'scalar_labels_in_batch_arr']
, data=[ITER, sess_IN.run(self.olc_S_AGG_RES),
np.vstack(target_output_for_linear_layer_labels_in_batch_l),
np.asarray(scalar_labels_in_batch_l)]
,
save_filename=str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg'
)
else:
sup.save_non_tf_data(names=['ITER', 'olc_S_AGG_RES',
'target_output_for_linear_layer_labels_in_batch_arr',
'scalar_labels_in_batch_arr']
, data=[ITER, sess_IN.run(self.olc_S_AGG_RES),
np.vstack(target_output_for_linear_layer_labels_in_batch_l),
np.asarray(scalar_labels_in_batch_l)]
, filename=str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg'
, savePath=self.save_path)
print('LAST S_AGG BATCH OF BLOCK GENERATED AND SAVED TO: '+str(str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg'))
s_agg_files_l.append(str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg')
target_output_for_linear_layer_labels_in_batch_l.clear()
scalar_labels_in_batch_l.clear()
################### SAVE SPIKE COUNT DATA ############### END
################### OPEN AND TRAIN OUTPUT LAYER ON GENERATED BATCHES ############### START
print('TIME TO GENERATE SPIKE NEW: '+str(time.time()-st_time_train_total))
print('PROCEEDING TO ASSEMBLE DATA FOR TRAINING OUTPUT LAYER ON S AGG DATA')
# extract all data files
st_time_train_output_total = time.time()
tp2_target_output_l = []
tp2_S_AGG_RES_input_l = []
for iter_ed in all_iters_arr:
print('SPIKE DATA WAS ALREADY GENEATED, OPENING FILE WITH DATA USING BATCH NUMBER: ' + str(iter_ed))
tp2_names, tp2_data = sup.unpack_file(
filename=str(root_save_name_IN) + '_BATCH_' + str(iter_ed) + '.datasagg',dataPath=self.source_path)
tp2_target_output_l.append(
tp2_data[tp2_names.index('target_output_for_linear_layer_labels_in_batch_arr')])
tp2_S_AGG_RES_input_l.append(tp2_data[tp2_names.index('olc_S_AGG_RES')])
print('ASSEMBLED ' + str(len(tp2_target_output_l)) + ' DATA BATCHES FROM SAVED FILES, PROCEEDING TO TRAIN...')
for rir in range(0,REPEATS_ON_ITER_RANGE_IN):
print('WHOLE_ITER_SUBSET_RANGE TRAINING iteration: '+str(rir))
for train_iter in range(0, len(tp2_target_output_l)):
sess_IN.run(self.OLC_set_S_AGG_to_previously_saved_batch_op,
feed_dict={self.olc_S_AGG_RES_PH: tp2_S_AGG_RES_input_l[train_iter]})
sess_IN.run(self.OLC_train_step_op,
feed_dict={self.olc_target_output_ph: tp2_target_output_l[train_iter]})
tp2_target_output_l.clear()
tp2_S_AGG_RES_input_l.clear()
print('TRAINING OUTPUT LAYER ON BATCH BLOCK COMPLETE, TIME TAKE: '+str(time.time()-st_time_train_output_total))
################### OPEN AND TRAIN OUTPUT LAYER ON GENERATED BATCHES ############### END
return spikes_files_l, s_agg_files_l
################### TRAINING OPS ######################################################################## END
def run_W_DENSE_initialization_loop(self
, sess_IN
, INI_NUM_TRAIN_VAL_TEST_L_IN
, INI_BATCH_SIZE_IN
, INI_initialization_idx_l
, INI_SAMPLE_INPUT_DURATION_MS_IN
, train_data_INPUT_VEC
, train_data_LABEL_SCALAR
):
'''
RUNS INITIALIZATION OF LIQUID WEIGHTS BY PRESENTING SERIES OF SNAPSHOTS TO LIQUID WITH APSTDP
:param sess_IN: TF SESSION
:param INI_NUM_TRAIN_VAL_TEST_L_IN: NUMBER OF TRAINING DATA TO USE
:param INI_BATCH_SIZE_IN: BATCH SIZE = 1
:param INI_initialization_idx_l: INDEXES OF RANDOMLY ORDERED TRAINING SAMPLES
:param INI_SAMPLE_INPUT_DURATION_MS_IN: SNAPSHOT DURATION
:param train_data_INPUT_VEC: INPUT DATA
:param train_data_LABEL_SCALAR: LABELS
:return:
'''
for INI_ITER in range(0,INI_NUM_TRAIN_VAL_TEST_L_IN[0]):
print('INITIALIZATION ITERATION '+str(INI_ITER)+' of '+str(INI_NUM_TRAIN_VAL_TEST_L_IN[0]))
batch_samples_agg_temp = []
scalar_labels_in_batch_l = []
for bt in range(0, INI_BATCH_SIZE_IN):
sample_idx = ((INI_ITER * INI_BATCH_SIZE_IN) + bt)
batch_samples_agg_temp.append(train_data_INPUT_VEC[INI_initialization_idx_l[sample_idx]])
scalar_labels_in_batch_l.append(train_data_LABEL_SCALAR[INI_initialization_idx_l[sample_idx]])
sess_IN.run(self.LIF_update_input_poisson_vals_op, feed_dict={self.I_PH: np.vstack(batch_samples_agg_temp)})
sess_IN.run(self.LIF_reset_V_to_zero_op)
sess_IN.run(self.LIF_reset_S_to_zero_op)
sess_IN.run(self.LIF_reset_T_RFR_STATE_to_zero_op)
sess_IN.run(self.LIF_reset_SS_T_STATE_op)
# sess_IN.run(self.ASTRO_reset_astro_res_lr_op)
# sess_IN.run(self.ASTRO_reset_astro_w_op)
# sess_IN.run(self.ASTRO_reset_astro_bias_op)
# sess_IN.run(self.PLASTICITY_reset_res_lr_op)
for t in range(0,INI_SAMPLE_INPUT_DURATION_MS_IN):
############### LIF FUNCTIONS ###################### START
sess_IN.run(self.LIF_compute_spikes_op)
sess_IN.run(self.LIF_reset_v_op)
# SPIKE RECORD OP
# sess_IN.run(spike_save_ops[t])
# sess_IN.run(OLC_agg_spikes)
sess_IN.run(self.PLASTICITY_update_C_VAR_op)
sess_IN.run(self.LIF_propagate_spike_op)
sess_IN.run(self.LIF_update_neuron_states_op)
sess_IN.run(self.LIF_evolveCurr_op)
sess_IN.run(self.LIF_update_refractory_state_op)
############### MAIN LIF FUNCTIONS ###################### END
############### PLASTICITY FUNCTIONS ###################### START
sess_IN.run(self.PLASTICITY_update_stdp_only_op)
sess_IN.run(self.PLASTICITY_update_trace_op)
############### PLASTICITY FUNCTIONS ###################### END
############### ASTRO FUNCTIONS ########################## START
# sess_IN.run(self.update_astro_op)
############### ASTRO FUNCTIONS ############################ END
############### PLASTICITY ASTRO DECAYS ###################### START
# sess_IN.run(PLASTICITY_decay_res_lr_op)
# sess_IN.run(ASTRO_decay_astro_res_lr_op)
# sess_IN.run(ASTRO_decay_astro_w_op)
# sess_IN.run(ASTRO_decay_astro_bias_op)
############### PLASTICITY ASTRO DECAYS ###################### END
print('W_DENSE INTIALIZATION COMPLETE.')