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main_conv.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 1 11:35:51 2017
@author: manuel
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 13 16:30:47 2017
@author: manuel
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 23 11:27:20 2017
@author: manuel
"""
import os
# uncomment the following line (or set the environmental variable
# CUDA_VISIBLE_DEVICES to an empty string when calling the script) to
# force TensorFlow to ignore GPUs
#
#os.environ["CUDA_VISIBLE_DEVICES"] = ""
import pprint
from model_conv import WGAN_conv
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from tflib import analysis, retinal_data, visualize_filters_and_units, data_provider, sim_pop_activity, maxent_data
flags = tf.app.flags
flags.DEFINE_string("architecture", "conv", "semi-conv (conv) or fully connected (fc)")
flags.DEFINE_integer("num_iter", 300000, "Epoch to train [50]")
flags.DEFINE_float("learning_rate", 1e-4, "Learning rate for adam [1e-4]")
flags.DEFINE_float("beta1", 0., "Momentum term of adam [0.]")
flags.DEFINE_float("beta2", 0.9, "Momentum term of adam [0.9]")
flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]")
flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("recovery_dir", "", "in case the real samples are already stored in another folder")
flags.DEFINE_boolean("is_train", False, "True for training, False for testing [False]")
flags.DEFINE_integer("training_step", 200, "number of batches between weigths and performance saving")
flags.DEFINE_string("training_stage", '', "stage of the training used for the GAN")
flags.DEFINE_integer("num_layers", 2, "number of convolutional layers [4]")
flags.DEFINE_integer("num_features", 4, "features in first layers [4]")
flags.DEFINE_integer("kernel_width", 5, "width of kernel [4]")
flags.DEFINE_integer("num_units", 512, "num units per layer in the fc GAN")
flags.DEFINE_integer("critic_iters", 5, "number of times the discriminator will be updated")
flags.DEFINE_float("lambd", 10, "parameter gradient penalization")
#parameter set specifiying data
flags.DEFINE_string("sample_dir", "", "where the samples will be saved. This is automatically defined below, here I just initialize the field.")
flags.DEFINE_string("dataset", "uniform", "type of neural activity. It can be simulated or retina")
flags.DEFINE_string("data_instance", "1", "if data==retina, this allows chosing the data instance")
flags.DEFINE_integer("num_samples", 2**13, "number of samples used for various diagnostics")
flags.DEFINE_integer("num_neurons", 4, "number of neurons in the population")
flags.DEFINE_float("packet_prob", 0.05, "probability of packets")
flags.DEFINE_integer("num_bins", 32, "number of bins (ms) for each sample")
flags.DEFINE_string("iteration", "0", "in case several instances are run with the same parameters")
flags.DEFINE_integer("ref_period", 2, "minimum number of ms between spikes (if < 0, no refractory period is imposed)")
flags.DEFINE_float("firing_rate", 0.25, "maximum firing rate of the simulated responses")
flags.DEFINE_float("correlation", 0.9, "correlation between neurons")
flags.DEFINE_integer("group_size", 4, "size of the correlated groups (e.g. 2 means pairwise correlations)")
flags.DEFINE_float("noise_in_packet", 0, "std of gaussian noise added to packets")
flags.DEFINE_integer("number_of_modes", 1, "[1,2] Number of different responses in the packet simulation. If =2 each type \
of packet will happen only once in the sample and one of two possible set of neurons will be chosen with equal prob for each packet.")
FLAGS = flags.FLAGS
pp = pprint.PrettyPrinter()
def main(_):
#print parameters
pp.pprint(tf.app.flags.FLAGS.flag_values_dict())
#folders
if FLAGS.dataset=='uniform':
if FLAGS.architecture=='fc':
FLAGS.sample_dir = 'samples fc/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins)\
+ '_ref_period_' + str(FLAGS.ref_period) + '_firing_rate_' + str(FLAGS.firing_rate) + '_correlation_' + str(FLAGS.correlation) +\
'_group_size_' + str(FLAGS.group_size) + '_critic_iters_' + str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) +\
'_num_units_' + str(FLAGS.num_units) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.architecture=='conv':
FLAGS.sample_dir = 'samples conv/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins)\
+ '_ref_period_' + str(FLAGS.ref_period) + '_firing_rate_' + str(FLAGS.firing_rate) + '_correlation_' + str(FLAGS.correlation) +\
'_group_size_' + str(FLAGS.group_size) + '_critic_iters_' + str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) +\
'_num_layers_' + str(FLAGS.num_layers) + '_num_features_' + str(FLAGS.num_features) + '_kernel_' + str(FLAGS.kernel_width) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.dataset=='packets' and FLAGS.number_of_modes==1:
if FLAGS.architecture=='fc':
FLAGS.sample_dir = 'samples fc/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins) + '_packet_prob_' + str(FLAGS.packet_prob)\
+ '_firing_rate_' + str(FLAGS.firing_rate) + '_group_size_' + str(FLAGS.group_size) + '_critic_iters_' +\
str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) + '_num_units_' + str(FLAGS.num_units) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.architecture=='conv':
FLAGS.sample_dir = 'samples conv/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins) + '_packet_prob_' + str(FLAGS.packet_prob)\
+ '_firing_rate_' + str(FLAGS.firing_rate) + '_group_size_' + str(FLAGS.group_size) + '_critic_iters_' +\
str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) +\
'_num_layers_' + str(FLAGS.num_layers) + '_num_features_' + str(FLAGS.num_features) + '_kernel_' + str(FLAGS.kernel_width) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.dataset=='packets' and FLAGS.number_of_modes==2:
if FLAGS.architecture=='fc':
FLAGS.sample_dir = 'samples fc/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins) + '_packet_prob_' + str(FLAGS.packet_prob)\
+ '_firing_rate_' + str(FLAGS.firing_rate) + '_group_size_' + str(FLAGS.group_size) + '_noise_in_packet_' + str(FLAGS.noise_in_packet) + '_number_of_modes_' + str(FLAGS.number_of_modes) + '_critic_iters_' +\
str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) + '_num_units_' + str(FLAGS.num_units) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.architecture=='conv':
FLAGS.sample_dir = 'samples conv/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins) + '_packet_prob_' + str(FLAGS.packet_prob)\
+ '_firing_rate_' + str(FLAGS.firing_rate) + '_group_size_' + str(FLAGS.group_size) + '_noise_in_packet_' + str(FLAGS.noise_in_packet) + '_number_of_modes_' + str(FLAGS.number_of_modes) + '_critic_iters_' +\
str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) +\
'_num_layers_' + str(FLAGS.num_layers) + '_num_features_' + str(FLAGS.num_features) + '_kernel_' + str(FLAGS.kernel_width) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.dataset=='retina':
if FLAGS.architecture=='fc':
FLAGS.sample_dir = 'samples fc/' + 'dataset_' + FLAGS.dataset +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins)\
+ '_critic_iters_' + str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) +\
'_num_units_' + str(FLAGS.num_units) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.architecture=='conv':
FLAGS.sample_dir = 'samples conv/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins)\
+ '_critic_iters_' + str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) +\
'_num_layers_' + str(FLAGS.num_layers) + '_num_features_' + str(FLAGS.num_features) + '_kernel_' + str(FLAGS.kernel_width) +\
'_iteration_' + FLAGS.iteration + '/'
elif FLAGS.dataset=='maxent':
if FLAGS.architecture=='fc':
FLAGS.sample_dir = 'samples fc/' + 'dataset_' + FLAGS.dataset + '_num_samples_' + str(FLAGS.num_samples) +\
'_num_neurons_' + str(FLAGS.num_neurons) + '_num_bins_' + str(FLAGS.num_bins)\
+ '_critic_iters_' + str(FLAGS.critic_iters) + '_lambda_' + str(FLAGS.lambd) +\
'_num_layers_' + str(FLAGS.num_layers) + '_num_units_' + str(FLAGS.num_units) +\
'_iteration_' + FLAGS.iteration + '/'
FLAGS.checkpoint_dir = FLAGS.sample_dir + 'checkpoint/'
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
if FLAGS.recovery_dir=="" and os.path.exists(FLAGS.sample_dir+'/stats_real.npz'):
FLAGS.recovery_dir = FLAGS.sample_dir
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
wgan = WGAN_conv(sess, architecture=FLAGS.architecture,
num_neurons=FLAGS.num_neurons,
num_bins=FLAGS.num_bins,
num_layers=FLAGS.num_layers, num_units=FLAGS.num_units,
num_features=FLAGS.num_features,
kernel_width=FLAGS.kernel_width,
lambd=FLAGS.lambd,
batch_size=FLAGS.batch_size,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
num_samples_for_diagnostics=FLAGS.num_samples
)
if FLAGS.is_train:
training_samples, dev_samples = data_provider.generate_spike_trains(FLAGS, FLAGS.recovery_dir)
wgan.training_samples = training_samples
wgan.dev_samples = dev_samples
print('data loaded')
wgan.train(FLAGS)
else:
if not wgan.load(FLAGS.training_stage):
raise Exception("[!] Train a model first, then run test mode")
#LOAD TRAINING DATASET (and its statistics)
original_dataset = np.load(FLAGS.sample_dir+ '/stats_real.npz')
#PLOT FILTERS
if FLAGS.dataset in ['retina', 'maxent']:
index = np.arange(FLAGS.num_neurons)
else:
index = np.argsort(original_dataset['shuffled_index'])
if FLAGS.architecture=='conv':
print('get filters -----------------------------------')
filters = wgan.get_filters(num_samples=64)
visualize_filters_and_units.plot_filters(filters, sess, FLAGS, index)
#GET GENERATED SAMPLES AND COMPUTE THEIR STATISTICS
print('compute stats -----------------------------------')
if 'samples' not in original_dataset:
if FLAGS.dataset=='retina':
real_samples = retinal_data.get_samples(num_bins=FLAGS.num_bins, num_neurons=FLAGS.num_neurons, instance=FLAGS.data_instance, folder=os.getcwd()+'/data/retinal data/')
elif FLAGS.dataset=='maxent':
real_samples = maxent_data.get_samples()
else:
real_samples = original_dataset['samples']
sim_pop_activity.plot_samples(real_samples, FLAGS.num_neurons, FLAGS.sample_dir, 'real')
fake_samples = wgan.get_samples(num_samples=FLAGS.num_samples)
fake_samples = fake_samples.eval(session=sess)
sim_pop_activity.plot_samples(fake_samples.T, FLAGS.num_neurons, FLAGS.sample_dir, 'fake')
_,_,_,_,_ = analysis.get_stats(X=fake_samples.T, num_neurons=FLAGS.num_neurons, num_bins= FLAGS.num_bins, folder=FLAGS.sample_dir, name='fake', instance=FLAGS.data_instance)
#EVALUATE HIGH ORDER FEATURES (only when dimensionality is low)
if FLAGS.dataset=='uniform' and FLAGS.num_bins*FLAGS.num_neurons<40:
print('compute high order statistics-----------------------------------')
num_trials = int(2**8)
num_samples_per_trial = 2**13
fake_samples_mat = np.zeros((num_trials*num_samples_per_trial,FLAGS.num_neurons*FLAGS.num_bins))
for ind_tr in range(num_trials):
fake_samples = wgan.sess.run([wgan.ex_samples])[0]
fake_samples_mat[ind_tr*num_samples_per_trial:(ind_tr+1)*num_samples_per_trial,:] = fake_samples
analysis.evaluate_approx_distribution(X=fake_samples_mat.T, folder=FLAGS.sample_dir, num_samples_theoretical_distr=2**21,num_bins=FLAGS.num_bins, num_neurons=FLAGS.num_neurons,\
group_size=FLAGS.group_size,refr_per=FLAGS.ref_period)
#COMPARISON WITH K-PAIRWISE AND DG MODELS (only for retinal data)
if FLAGS.dataset=='retina':
print('nearest sample analysis -----------------------------------')
num_samples = 100 #this is for the 'nearest sample' analysis (Fig. S5)
print('real_samples')
analysis.nearest_sample(X_real=real_samples, fake_samples=real_samples, num_neurons=FLAGS.num_neurons, num_bins=FLAGS.num_bins, folder=FLAGS.sample_dir, name='real', num_samples=num_samples)
###################
print('fake_samples')
analysis.nearest_sample(X_real=real_samples, fake_samples=fake_samples.T, num_neurons=FLAGS.num_neurons, num_bins=FLAGS.num_bins, folder=FLAGS.sample_dir, name='spikeGAN', num_samples=num_samples)
###################
k_pairwise_samples = retinal_data.load_samples_from_k_pairwise_model(num_samples=FLAGS.num_samples, num_bins=FLAGS.num_bins, num_neurons=FLAGS.num_neurons, instance=FLAGS.data_instance, folder=os.getcwd()+'/data/retinal data/')
print('k_pairwise_samples')
_,_,_,_ ,_ = analysis.get_stats(X=k_pairwise_samples, num_neurons=FLAGS.num_neurons, num_bins= FLAGS.num_bins, folder=FLAGS.sample_dir, name='k_pairwise', instance=FLAGS.data_instance)
analysis.nearest_sample(X_real=real_samples, fake_samples=k_pairwise_samples, num_neurons=FLAGS.num_neurons, num_bins=FLAGS.num_bins, folder=FLAGS.sample_dir, name='k_pairwise', num_samples=num_samples)
###################
DDG_samples = retinal_data.load_samples_from_DDG_model(num_samples=FLAGS.num_samples, num_bins=FLAGS.num_bins, num_neurons=FLAGS.num_neurons, instance=FLAGS.data_instance, folder=os.getcwd()+'/data/retinal data/')
print('DDG_samples')
_,_,_,_ ,_ = analysis.get_stats(X=DDG_samples, num_neurons=FLAGS.num_neurons, num_bins= FLAGS.num_bins, folder=FLAGS.sample_dir, name='DDG', instance=FLAGS.data_instance)
analysis.nearest_sample(X_real=real_samples, fake_samples=DDG_samples, num_neurons=FLAGS.num_neurons, num_bins=FLAGS.num_bins, folder=FLAGS.sample_dir, name='DDG', num_samples=num_samples)
if __name__ == '__main__':
tf.app.run()