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LSM_RUN_MAIN_SIM_N_MNIST.py
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import numpy as np
# import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import os
import sys
import time
import NALSM_CONSTRUCTOR_N_MNIST as construct
import NALSM_PARALLEL_SAVE as parasaver
import NALSM_SIM_SUPPORT as runSupport
import NALSM_GEN_SUPPORT as sup
class run_sim:
def __init__(self):
self.main_Path = os.getcwd()
self.dataPath_TRAIN = self.main_Path + '/train_data'
self.netPath = self.main_Path + '/networks'
self.num_save_processes = 4
def train_loop_v0(self
, SAVE_VER_IN
, NET_NUM_IN
, W_RES_EXC_MIN_MAX_IN
, W_RES_INH_MIN_MAX_IN
, W_RES_INP_MIN_MAX_IN
, W_OUT_RES_MIN_MAX_IN
, N1_SAMPLE_INPUT_DURATION_MS_IN
, N_INI_SAMPLE_INPUT_DURATION_MS_IN
, N1_BATCH_SIZE_IN
, N_INI_BATCH_SIZE_IN
, N1_BATCHS_PER_BLOCK_IN
, N1_record_samples_in_batch_l_IN
, N_INI_record_samples_in_batch_l_IN
, STDP_POTENTIATION_LR_IN
, N1_RES_LR_DECAY_IN
, N_INI_RES_LR_DECAY_IN
, ASTRO_BIAS_OFFSET_PERCENTAGE_IN
, ASTRO_W_SCALING_IN
, ASTRO_TAU_IN
, INPUT_CURRENT_IN
, DATASET_SEED_IN
, NP_SEED_IN
, TF_SEED_IN
, INTIAL_ABS_W_SCLR_IN
, N1_SPIKE_STORE_MOD_IN
, N1_NUM_TRAIN_VAL_TEST_L_IN
, N_INI_NUM_TRAIN_VAL_TEST_L_IN
, N_INI_SPIKE_STORE_MOD_IN=1000000000
, NUM_OF_TRAIN_CYLES_ON_DATASET_IN=5000
):
## -SEEDSET- SET RANDOM SEED SEED
if NP_SEED_IN==-1:
np.random.seed()
rnd_sd = np.random.randint(0, 1000000)
np.random.seed(rnd_sd)
else:
rnd_sd=NP_SEED_IN
np.random.seed(rnd_sd)
tf.reset_default_graph()
self.savePath = self.dataPath_TRAIN + '/ver_' + str(SAVE_VER_IN) + '/'
net_name = 'Network_' + str(NET_NUM_IN)
if DATASET_SEED_IN==-1:
print('FULL DATASET BEING USED WITH: ')
N1_NUM_TRAIN_VAL_TEST_L_IN = [50000,10000,10000]
print('TRAINING SAMPLES: '+str(N1_NUM_TRAIN_VAL_TEST_L_IN[0]))
print('VALIDATION SAMPLES: ' + str(N1_NUM_TRAIN_VAL_TEST_L_IN[1]))
print('TRAINING SAMPLES: ' + str(N1_NUM_TRAIN_VAL_TEST_L_IN[2]))
print('SAVING SIMULATION PARAMETERS')
param_names = ['SAVE_VER_IN'
, 'NET_NUM_IN'
, 'W_RES_EXC_MIN_MAX_IN'
, 'W_RES_INH_MIN_MAX_IN'
, 'W_RES_INP_MIN_MAX_IN'
, 'W_OUT_RES_MIN_MAX_IN'
, 'N1_SAMPLE_INPUT_DURATION_MS_IN'
, 'N_INI_SAMPLE_INPUT_DURATION_MS_IN'
, 'N1_BATCH_SIZE_IN'
, 'N_INI_BATCH_SIZE_IN'
, 'N1_BATCHS_PER_BLOCK_IN'
, 'N1_record_samples_in_batch_l_IN'
, 'N_INI_record_samples_in_batch_l_IN'
, 'STDP_POTENTIATION_LR_IN'
, 'N1_RES_LR_DECAY_IN'
, 'N_INI_RES_LR_DECAY_IN'
, 'ASTRO_BIAS_OFFSET_PERCENTAGE_IN'
, 'ASTRO_W_SCALING_IN'
, 'ASTRO_TAU_IN'
, 'INPUT_CURRENT_IN'
, 'DATASET_SEED_IN'
, 'NP_SEED_IN'
, 'TF_SEED_IN'
, 'INTIAL_ABS_W_SCLR_IN'
, 'N1_SPIKE_STORE_MOD_IN'
, 'N1_NUM_TRAIN_VAL_TEST_L_IN'
, 'N_INI_NUM_TRAIN_VAL_TEST_L_IN'
, 'N_INI_SPIKE_STORE_MOD_IN'
, 'NUM_OF_TRAIN_CYLES_ON_DATASET_IN']
param_values = [SAVE_VER_IN
, NET_NUM_IN
, W_RES_EXC_MIN_MAX_IN
, W_RES_INH_MIN_MAX_IN
, W_RES_INP_MIN_MAX_IN
, W_OUT_RES_MIN_MAX_IN
, N1_SAMPLE_INPUT_DURATION_MS_IN
, N_INI_SAMPLE_INPUT_DURATION_MS_IN
, N1_BATCH_SIZE_IN
, N_INI_BATCH_SIZE_IN
, N1_BATCHS_PER_BLOCK_IN
, N1_record_samples_in_batch_l_IN
, N_INI_record_samples_in_batch_l_IN
, STDP_POTENTIATION_LR_IN
, N1_RES_LR_DECAY_IN
, N_INI_RES_LR_DECAY_IN
, ASTRO_BIAS_OFFSET_PERCENTAGE_IN
, ASTRO_W_SCALING_IN
, ASTRO_TAU_IN
, INPUT_CURRENT_IN
, DATASET_SEED_IN
, NP_SEED_IN
, TF_SEED_IN
, INTIAL_ABS_W_SCLR_IN
, N1_SPIKE_STORE_MOD_IN
, N1_NUM_TRAIN_VAL_TEST_L_IN
, N_INI_NUM_TRAIN_VAL_TEST_L_IN
, N_INI_SPIKE_STORE_MOD_IN
, NUM_OF_TRAIN_CYLES_ON_DATASET_IN]
sup.save_log_file_of_parameters(root_savename='VER_'+str(SAVE_VER_IN)+'_params',savepath=self.savePath,parameter_names=param_names,parameters_values=param_values)
NUM_BATCHES_IN_FULL_TRAIN_DATASET = int(N1_NUM_TRAIN_VAL_TEST_L_IN[0] / N1_BATCH_SIZE_IN)
if int(NUM_BATCHES_IN_FULL_TRAIN_DATASET / N1_BATCHS_PER_BLOCK_IN) == (
NUM_BATCHES_IN_FULL_TRAIN_DATASET / N1_BATCHS_PER_BLOCK_IN):
NUM_BLOCKS_OF_BATCHES_IN_FULL_DATASET = int(NUM_BATCHES_IN_FULL_TRAIN_DATASET / N1_BATCHS_PER_BLOCK_IN)
print('NUM_BLOCKS_OF_BATCHES_IN_FULL_DATASET',NUM_BLOCKS_OF_BATCHES_IN_FULL_DATASET)
else:
print('BATCH BLOCK SIZE DOES NOT MULTIPLY INTO NUM OF BATCHES IN DATASET WHOLLY, QUITTING...')
sys.exit(2)
# INITIALIZE MODEL
N1 = construct.simulation_constructor(
network_name=net_name
,network_path=self.netPath
, source_path=self.savePath
, save_path=self.savePath
, W_RES_EXC_MIN_MAX_IN=W_RES_EXC_MIN_MAX_IN
, W_RES_INH_MIN_MAX_IN=W_RES_INH_MIN_MAX_IN
, W_RES_INP_MIN_MAX_IN=W_RES_INP_MIN_MAX_IN
, W_OUT_RES_MIN_MAX_IN=W_OUT_RES_MIN_MAX_IN
, SAMPLE_INPUT_DURATION_MS_IN=N1_SAMPLE_INPUT_DURATION_MS_IN
, BATCH_SIZE_IN=N1_BATCH_SIZE_IN
, record_samples_in_batch_l_IN=N1_record_samples_in_batch_l_IN
, STDP_POTENTIATION_LR_IN=STDP_POTENTIATION_LR_IN
, RES_LR_DECAY_IN=N1_RES_LR_DECAY_IN
, ASTRO_BIAS_OFFSET_PERCENTAGE_IN=ASTRO_BIAS_OFFSET_PERCENTAGE_IN
, ASTRO_W_SCALING_IN=ASTRO_W_SCALING_IN
, ASTRO_TAU_IN=ASTRO_TAU_IN
, INPUT_CURRENT_IN=INPUT_CURRENT_IN
, RES_INI_COST_W_SCLR_IN = INTIAL_ABS_W_SCLR_IN
, INITIALIZE_ASTRO=False
, INITIALIZE_STDP=False
)
print('ASSEMBLING DATA FOR N1...')
# GET DATA
run_sup = runSupport.run_support(number_of_networks_IN=N1_BATCH_SIZE_IN)
data_list_train_l = run_sup.get_data_lists_labels(data_type_IN='train_all')
data_list_valid_l = run_sup.get_data_lists_labels(data_type_IN='valid_all')
data_list_test_l = run_sup.get_data_lists_labels(data_type_IN='test_all')
print('INITIALIZING PARALLEL SAVER...')
### INITIALIZE PARALLEL SAVER ###
smart_saver = parasaver.multi_process(num_processes=self.num_save_processes, save_path=self.savePath)
st = time.time()
## -SEEDSET-
if TF_SEED_IN==-1:
rnd_sd_TF = np.random.randint(0, 100000)
tf.set_random_seed(rnd_sd_TF)
else:
rnd_sd_TF = TF_SEED_IN
tf.set_random_seed(rnd_sd_TF)
# START TENSORFLOW SESSION
sess = tf.Session()
sess.run(tf.global_variables_initializer())
list_of_uniq_weights = np.unique(sess.run(N1.W_DENSE))
print(list_of_uniq_weights)
print('VALIDATION DATA: GENERATING S AGG DATA')
val_spike_files_l,val_s_agg_files_l = N1.generate_validation_or_test_spike_data_STATIC(sess_IN=sess
, saver_object_IN=smart_saver
, valid_or_test_data_idx_l_IN=data_list_valid_l
, BATCH_SIZE_IN=N1_BATCH_SIZE_IN
, SAMPLE_INPUT_DURATION_MS_IN=N1_SAMPLE_INPUT_DURATION_MS_IN
, NUM_DATA_SAMPLES_IN=N1_NUM_TRAIN_VAL_TEST_L_IN[1]
, spike_store_batch_mod_IN=N1_SPIKE_STORE_MOD_IN
, record_samples_in_batch_l_IN=N1_record_samples_in_batch_l_IN
, root_save_name_IN=str('VALIDATION_' + str(SAVE_VER_IN))
, data_type_IN='valid_all'
)
print('TEST DATA: GENERATING S AGG DATA')
tst_spike_files_l, tst_s_agg_files_l = N1.generate_validation_or_test_spike_data_STATIC(sess_IN=sess
, saver_object_IN=smart_saver
, valid_or_test_data_idx_l_IN=data_list_test_l
, BATCH_SIZE_IN=N1_BATCH_SIZE_IN
, SAMPLE_INPUT_DURATION_MS_IN=N1_SAMPLE_INPUT_DURATION_MS_IN
, NUM_DATA_SAMPLES_IN=N1_NUM_TRAIN_VAL_TEST_L_IN[2]
, spike_store_batch_mod_IN=N1_SPIKE_STORE_MOD_IN
, record_samples_in_batch_l_IN=N1_record_samples_in_batch_l_IN
, root_save_name_IN=str('TEST_' + str(SAVE_VER_IN))
, data_type_IN='test_all'
)
ACUM_TRAIN_S_AGG_FILES_l = []
ACUM_TRAIN_SPIKES_FILES_l = []
ACUM_VAL_ACC_l = []
ACUM_VAL_ACC_FILES_l = []
ACUM_TRAIN_ACC_l = []
ACUM_TRAIN_ACC_FILES_l = []
ACUM_TST_ACC_l = []
ACUM_TST_ACC_FILES_l = []
ACUM_VAL_W1_l = []
ACUM_VAL_b1_l = []
print('TRAIN DATA: GENERATING S AGG DATA and TRAINING ON IT.')
for TRAIN_FULL_DATA_ITER in range(0,NUM_OF_TRAIN_CYLES_ON_DATASET_IN):
print('TRAINING OVER DATASET ITERATION: '+str(TRAIN_FULL_DATA_ITER)+' / '+str(NUM_OF_TRAIN_CYLES_ON_DATASET_IN))
for ITER_OVER_BLOCKS_OF_BATCHS in range(0,NUM_BLOCKS_OF_BATCHES_IN_FULL_DATASET):
print('TRAINING OVER BLOCK OF BATCHES ITERATION: ' + str(ITER_OVER_BLOCKS_OF_BATCHS)+' / '+str(NUM_BLOCKS_OF_BATCHES_IN_FULL_DATASET))
train_spikes_files_l, train_s_agg_files_l = N1.train_on_set_number_of_batches_STATIC(sess_IN=sess
, saver_object_IN=smart_saver
, training_iteration_range_IN=[(NUM_BLOCKS_OF_BATCHES_IN_FULL_DATASET*N1_BATCHS_PER_BLOCK_IN*TRAIN_FULL_DATA_ITER)+ITER_OVER_BLOCKS_OF_BATCHS*N1_BATCHS_PER_BLOCK_IN,(NUM_BLOCKS_OF_BATCHES_IN_FULL_DATASET*N1_BATCHS_PER_BLOCK_IN*TRAIN_FULL_DATA_ITER)+(ITER_OVER_BLOCKS_OF_BATCHS+1)*N1_BATCHS_PER_BLOCK_IN]
, training_dataset_size_IN=N1_NUM_TRAIN_VAL_TEST_L_IN[0]
, list_of_data_files_IN=ACUM_TRAIN_S_AGG_FILES_l
, BATCH_SIZE_IN=N1_BATCH_SIZE_IN
, root_save_name_IN='TRAIN_S_AGG_'+str(SAVE_VER_IN)
, train_data_idx_l_IN=data_list_train_l
, data_type_IN='train_all'
, spike_store_batch_mod_IN=N1_SPIKE_STORE_MOD_IN
, record_samples_in_batch_l_IN=N1_record_samples_in_batch_l_IN
, SAMPLE_INPUT_DURATION_MS_IN=N1_SAMPLE_INPUT_DURATION_MS_IN
)
ACUM_TRAIN_S_AGG_FILES_l.extend(train_s_agg_files_l)
ACUM_TRAIN_SPIKES_FILES_l.extend(train_spikes_files_l)
## EVALUATES MODEL ON VALIDATION DATASET
val_acc = N1.evaluate_output_layer_on_FULL_validation_or_test_s_agg_data(
sess_IN = sess
, saver_object_IN=smart_saver
, list_of_data_files_IN=val_s_agg_files_l
)
ACUM_VAL_ACC_l.append(val_acc)
## EVALUATES MODEL ON TRAINING DATASET
train_acc = N1.evaluate_output_layer_on_FULL_validation_or_test_s_agg_data(
sess_IN=sess
, saver_object_IN=smart_saver
, list_of_data_files_IN=ACUM_TRAIN_S_AGG_FILES_l
)
ACUM_TRAIN_ACC_l.append(train_acc)
## EVALUATES MODEL ON TEST DATASET
tst_acc = N1.evaluate_output_layer_on_FULL_validation_or_test_s_agg_data(
sess_IN=sess
, saver_object_IN=smart_saver
, list_of_data_files_IN=tst_s_agg_files_l
)
ACUM_TST_ACC_l.append(tst_acc)
ACUM_VAL_W1_l.append(sess.run(N1.olc_W1))
ACUM_VAL_b1_l.append(sess.run(N1.olc_b1))
# SAVE FILE LISTS, ACCURACY VALUES, OUTPUT LAYER WEIGHTS AND BIASES
smart_saver.save_data(signal=1
, names=['val_spike_files_l', 'val_s_agg_files_l'
, 'tst_spike_files_l', 'tst_s_agg_files_l'
, 'ACUM_TRAIN_SPIKES_FILES_l', 'ACUM_TRAIN_S_AGG_FILES_l'
, 'ACUM_VAL_ACC_l', 'ACUM_VAL_ACC_FILES_l'
, 'ACUM_TRAIN_ACC_l', 'ACUM_TRAIN_ACC_FILES_l'
, 'ACUM_TST_ACC_l', 'ACUM_TST_ACC_FILES_l'
, 'rnd_sd', 'rnd_sd_TF', 'list_of_uniq_weights'
]
, data=[val_spike_files_l, val_s_agg_files_l
, tst_spike_files_l, tst_s_agg_files_l
, ACUM_TRAIN_SPIKES_FILES_l, ACUM_TRAIN_S_AGG_FILES_l
, ACUM_VAL_ACC_l, ACUM_VAL_ACC_FILES_l
, ACUM_TRAIN_ACC_l, ACUM_TRAIN_ACC_FILES_l
, ACUM_TST_ACC_l, ACUM_TST_ACC_FILES_l
, rnd_sd, rnd_sd_TF, list_of_uniq_weights
]
, save_filename='FILE_L_VAL_ACC_L.files'
)
smart_saver.save_data(signal=1
, names=['ACUM_VAL_W1_l', 'ACUM_VAL_b1_l']
, data=[ACUM_VAL_W1_l, ACUM_VAL_b1_l]
, save_filename='W_b_by_ITER.evaldata'
)
smart_saver.kill_workers(process_count=self.num_save_processes)
print('TOTAL SIMULATION TIME: '+str(time.time()-st))
sess.close()
if __name__ == "__main__":
GLOBAL_VAR__GPU_NUM = input('GPU? ')
# GLOBAL_VAR__GPU_NUM = 1
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(GLOBAL_VAR__GPU_NUM)
print('GPU: ' + str(os.environ["CUDA_VISIBLE_DEVICES"]))
SAVE_VER_VAR_INP = input('VERSION? [int]: ')
SAVE_VER_VAR = int(SAVE_VER_VAR_INP)
NET_NUM_VAR_INP = input('NET_NUM_VAR? [int]: ')
NET_NUM_VAR = int(NET_NUM_VAR_INP)
N1_BATCH_SIZE_INP = input('BATCH_SIZE? [int](250 for smaller nets(1000 neurons)/ 50 for larger nets(8000 neurons): ')
N1_BATCH_SIZE_VAR = int(N1_BATCH_SIZE_INP)
N1_BATCHS_PER_BLOCK_INP = input('BATCHS_PER_BLOCK? [int](10 for smaller nets(1000 neurons)/ 50 for larger nets(8000 neurons): ')
N1_BATCHS_PER_BLOCK_VAR = int(N1_BATCHS_PER_BLOCK_INP)
# N1_SAMPLE_INPUT_DURATION_MS_INP = input('N1_SAMPLE_INPUT_DURATION_MS? [int]: ')
# N1_SAMPLE_INPUT_DURATION_MS_VAR = int(N1_SAMPLE_INPUT_DURATION_MS_INP)
N1_SAMPLE_INPUT_DURATION_MS_VAR = int(250)
# ASTRO_BIAS_OFFSET_PERCENTAGE_INP = input('ASTRO_BIAS_OFFSET_PERCENTAGE? [float]: ')
# ASTRO_BIAS_OFFSET_PERCENTAGE_VAR = float(ASTRO_BIAS_OFFSET_PERCENTAGE_INP)
INTIAL_ABS_W_SCLR_INP = input('INTIAL_ABS_W_SCLR? [float]: ')
INTIAL_ABS_W_SCLR_VAR = float(INTIAL_ABS_W_SCLR_INP)
# DATASET_SEED_INP = input('DATASET_SEED? [int]: ')
# DATASET_SEED_VAR = int(DATASET_SEED_INP)
DATASET_SEED_VAR = int(-1) # will use full dataset
# NP_SEED_INP = input('NP_SEED? [int]: ')
# NP_SEED_VAR = int(NP_SEED_INP)
NP_SEED_VAR = int(-1) # will generate random seed
# TF_SEED_INP = input('TF_SEED? [int]: ')
# TF_SEED_VAR = int(TF_SEED_INP)
TF_SEED_VAR = int(-1) # will generate random seed
N1_SPIKE_STORE_MOD_VAR = int(10000/N1_BATCH_SIZE_VAR)
print('SPIKE_STORE_BATCH_MOD: ' + str(N1_SPIKE_STORE_MOD_VAR))
spike_save_idxs = np.asarray(np.round(N1_BATCH_SIZE_VAR * np.divide(np.arange(20), 20), 0), dtype=np.int32)
print('SPIKE_SAVE_IDX LIST: ', spike_save_idxs)
TSL = run_sim()
TSL.train_loop_v0(SAVE_VER_IN=SAVE_VER_VAR
, NET_NUM_IN=NET_NUM_VAR
, W_RES_EXC_MIN_MAX_IN=[0.0,3.0]
, W_RES_INH_MIN_MAX_IN=[-3.0,0.0]
, W_RES_INP_MIN_MAX_IN=[-3.0,3.0]
, W_OUT_RES_MIN_MAX_IN=[0.0,0.0]
, N1_SAMPLE_INPUT_DURATION_MS_IN=N1_SAMPLE_INPUT_DURATION_MS_VAR
, N_INI_SAMPLE_INPUT_DURATION_MS_IN=int(1000)
, N1_BATCH_SIZE_IN=N1_BATCH_SIZE_VAR#int(20)
, N_INI_BATCH_SIZE_IN=1#1
, N1_BATCHS_PER_BLOCK_IN = N1_BATCHS_PER_BLOCK_VAR
, N1_record_samples_in_batch_l_IN=spike_save_idxs
, N_INI_record_samples_in_batch_l_IN=[]
, STDP_POTENTIATION_LR_IN=0.15
, N1_RES_LR_DECAY_IN=0.999
, N_INI_RES_LR_DECAY_IN=1.0
, DATASET_SEED_IN=DATASET_SEED_VAR
, NP_SEED_IN=NP_SEED_VAR
, TF_SEED_IN=TF_SEED_VAR
, INTIAL_ABS_W_SCLR_IN = INTIAL_ABS_W_SCLR_VAR
, ASTRO_BIAS_OFFSET_PERCENTAGE_IN=0.0
, ASTRO_W_SCALING_IN=0.002
, ASTRO_TAU_IN=100.0
, INPUT_CURRENT_IN=100.0
, N1_SPIKE_STORE_MOD_IN=N1_SPIKE_STORE_MOD_VAR #batch mod # TAKES LOT OF TIME(DOUBLE NORMAL) APPLY TO ONLY SINGLE BATCH!!!!! CHANGE LATER, set it to be number of batches in whole dataset
, N1_NUM_TRAIN_VAL_TEST_L_IN=[12000,2000,2000]
, N_INI_NUM_TRAIN_VAL_TEST_L_IN=[100, 0, 0]
)