def main(unused_argv=()):
# Load stimulus-response data
datasets = gfile.ListDirectory(FLAGS.src_dir)
stimuli = {}
responses = []
print(datasets)
for icnt, idataset in enumerate(datasets):
#for icnt, idataset in enumerate([datasets[2]]):
# print('HACK - only one dataset used!!')
fullpath = os.path.join(FLAGS.src_dir, idataset)
if gfile.IsDirectory(fullpath):
key = 'stim_%d' % icnt
op = data_util.get_stimulus_response(FLAGS.src_dir, idataset, key)
stimulus, resp, dimx, dimy, num_cell_types = op
stimuli.update({key: stimulus})
responses += resp
print('# Responses %d' % len(responses))
stimulus = stimuli[responses[FLAGS.taskid]['stimulus_key']]
save_filename = ('linear_taskid_%d_piece_%s.pkl' % (FLAGS.taskid, responses[FLAGS.taskid]['piece']))
print(save_filename)
learn_lin_embedding(stimulus, np.double(responses[FLAGS.taskid]['responses']),
filename=save_filename,
lam_l1=0.00001, beta=10, time_window=30,
lr=0.01)
print('DONE!')
def get_latest_file(save_location, short_filename): # get relevant files
file_list = gfile.ListDirectory(save_location)
print(save_location, short_filename)
save_filename = save_location + short_filename
print('\nLoading: ', save_filename)
bin_files = []
meta_files = []
for file_n in file_list:
if re.search(short_filename + '.', file_n):
if re.search('.meta', file_n):
meta_files += [file_n]
else:
bin_files += [file_n]
# print(bin_files)
print(len(meta_files), len(bin_files), len(file_list))
# get iteration numbers
iterations = np.array([])
for file_name in bin_files:
try:
iterations = np.append(
iterations, int(file_name.split('/')[-1].split('-')[-1]))
except:
print('Could not load filename: ' + file_name)
iterations.sort()
print(iterations)
iter_plot = iterations[-1]
print(int(iter_plot))
restore_file = save_filename + '-' + str(int(iter_plot))
return restore_file
def inputs(name, data_location, batch_size, num_epochs, stim_dim, resp_dim):
# gives a batch of stimulus and responses from a .tfrecords file
# works for .tfrecords file made using CoarseDataUtils.convert_to_TFRecords
# Get filename queue.
# Actual name is either 'name', 'name.tfrecords' or
# folder 'name' with list of .tfrecords files.
with tf.name_scope('input'):
filename = os.path.join(data_location, name)
filename_extension = os.path.join(data_location, name + '.tfrecords')
if gfile.Exists(filename) and not gfile.IsDirectory(filename):
tf.logging.info('%s Exists' % filename)
filenames = [filename]
elif gfile.Exists(filename_extension
) and not gfile.IsDirectory(filename_extension):
tf.logging.info('%s Exists' % filename_extension)
filenames = [filename_extension]
elif gfile.IsDirectory(filename):
tf.logging.info('%s Exists and is a directory' % filename)
filenames_short = gfile.ListDirectory(filename)
filenames = [
os.path.join(filename, ifilenames_short)
for ifilenames_short in filenames_short
]
tf.logging.info(filenames)
filename_queue = tf.train.string_input_producer(filenames,
num_epochs=num_epochs,
capacity=10000)
# Even when reading in multiple threads, share the filename
# queue.
stimulus, response = read_and_decode(filename_queue, stim_dim, resp_dim)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
stimulus_batch, response_batch = tf.train.shuffle_batch(
[stimulus, response],
batch_size=batch_size,
num_threads=30,
capacity=5000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
min_after_dequeue=2000)
'''
stimulus_batch, response_batch = tf.train.batch(
[stimulus, response], batch_size=batch_size, num_threads=30,
capacity = 50000 + 3 * batch_size)
'''
return stimulus_batch, response_batch
def main(unused_argv=()):
# Load stimulus-response data
datasets = gfile.ListDirectory(FLAGS.src_dir)
responses = []
print(datasets)
for icnt, idataset in enumerate([datasets]): #TODO(bhaishahster): HACK.
fullpath = os.path.join(FLAGS.src_dir, idataset)
if gfile.IsDirectory(fullpath):
key = 'stim_%d' % icnt
op = data_util.get_stimulus_response(FLAGS.src_dir, idataset, key)
stimulus, resp, dimx, dimy, num_cell_types = op
responses += resp
for idataset in range(len(responses)):
k, b, ttf = fit_ln_population(responses[idataset]['responses'], stimulus) # Use FLAGS.taskID
save_dict = {'k': k, 'b': b, 'ttf': ttf}
save_analysis_filename = os.path.join(FLAGS.save_folder,
responses[idataset]['piece']
+ '_ln_model.pkl')
pickle.dump(save_dict, gfile.Open(save_analysis_filename, 'w'))
def main(unused_argv=()):
np.random.seed(23)
tf.set_random_seed(1234)
random.seed(50)
# Load stimulus-response data.
# Collect population response across retinas in the list 'responses'.
# Stimulus for each retina is indicated by 'stim_id',
# which is found in 'stimuli' dictionary.
datasets = gfile.ListDirectory(FLAGS.src_dir)
stimuli = {}
responses = []
for icnt, idataset in enumerate(datasets):
fullpath = os.path.join(FLAGS.src_dir, idataset)
if gfile.IsDirectory(fullpath):
key = 'stim_%d' % icnt
op = data_util.get_stimulus_response(FLAGS.src_dir, idataset, key,
boundary=FLAGS.valid_cells_boundary)
stimulus, resp, dimx, dimy, _ = op
stimuli.update({key: stimulus})
responses += resp
taskid = FLAGS.taskid
dat = responses[taskid]
stimulus = stimuli[dat['stimulus_key']]
# parameters
window = 5
# Compute time course and non-linearity as two parameters which might be should be explored in embedded space.
n_cells = dat['responses'].shape[1]
T = np.minimum(stimulus.shape[0], dat['responses'].shape[0])
stim_short = stimulus[:T, :, :]
resp_short = dat['responses'][:T, :].astype(np.float32)
save_dict = {}
# Find time course, non-linearity and RF parameters
########################################################################
# Separation between cell types
########################################################################
save_dict.update({'cell_type': dat['cell_type']})
save_dict.update({'dist_nn_cell_type': dat['dist_nn_cell_type']})
########################################################################
# Find mean firing rate
########################################################################
mean_fr = dat['responses'].mean(0)
mean_fr_1 = np.mean(mean_fr[np.squeeze(dat['cell_type'])==1])
mean_fr_2 = np.mean(mean_fr[np.squeeze(dat['cell_type'])==2])
mean_fr_dict = {'mean_fr': mean_fr,
'mean_fr_1': mean_fr_1, 'mean_fr_2': mean_fr_2}
save_dict.update({'mean_fr_dict': mean_fr_dict})
########################################################################
# compute STAs
########################################################################
stas = np.zeros((n_cells, 80, 40, 30))
for icell in range(n_cells):
print(icell)
center = dat['centers'][icell, :].astype(np.int)
windx = [np.maximum(center[0]-window, 0), np.minimum(center[0]+window, 80-1)]
windy = [np.maximum(center[1]-window, 0), np.minimum(center[1]+window, 40-1)]
stim_cell = np.reshape(stim_short[:, windx[0]: windx[1], windy[0]: windy[1]], [stim_short.shape[0], -1])
for idelay in range(30):
stas[icell, windx[0]: windx[1], windy[0]: windy[1], idelay] = np.reshape(resp_short[idelay:, icell].dot(stim_cell[:T-idelay, :]),
[windx[1] - windx[0], windy[1] - windy[0]]) / np.sum(resp_short[idelay:, icell])
stas_dict = {'stas': stas}
# save_dict.update({'stas_dict': stas_dict})
########################################################################
# Find time courses for each cell
########################################################################
ttf_log = []
for icell in range(n_cells):
center = dat['centers'][icell, :].astype(np.int)
windx = [np.maximum(center[0]-window, 0), np.minimum(center[0]+window, 80-1)]
windy = [np.maximum(center[1]-window, 0), np.minimum(center[1]+window, 40-1)]
ll = stas[icell, windx[0]: windx[1], windy[0]: windy[1], :]
ll_2d = np.reshape(ll, [-1, ll.shape[-1]])
u, s, v = np.linalg.svd(ll_2d)
ttf_log += [v[0, :]]
ttf_log = np.array(ttf_log)
signs = [np.sign(ttf_log[icell, np.argmax(np.abs(ttf_log[icell, :]))]) for icell in range(ttf_log.shape[0])]
ttf_corrected = np.expand_dims(np.array(signs), 1) * ttf_log
ttf_corrected[np.squeeze(dat['cell_type'])==1, :] = ttf_corrected[np.squeeze(dat['cell_type'])==1, :] * -1
ttf_mean_1 = ttf_corrected[np.squeeze(dat['cell_type'])==1, :].mean(0)
ttf_mean_2 = ttf_corrected[np.squeeze(dat['cell_type'])==2, :].mean(0)
ttf_params_1 = get_times(ttf_mean_1)
ttf_params_2 = get_times(ttf_mean_2)
ttf_dict = {'ttf_log': ttf_log,
'ttf_mean_1': ttf_mean_1, 'ttf_mean_2': ttf_mean_2,
'ttf_params_1': ttf_params_1, 'ttf_params_2': ttf_params_2}
save_dict.update({'ttf_dict': ttf_dict})
'''
plt.plot(ttf_corrected[np.squeeze(dat['cell_type'])==1, :].T, 'r', alpha=0.3)
plt.plot(ttf_corrected[np.squeeze(dat['cell_type'])==2, :].T, 'k', alpha=0.3)
plt.plot(ttf_mean_1, 'r--')
plt.plot(ttf_mean_2, 'k--')
'''
########################################################################
## Find non-linearity
########################################################################
f_nl = lambda x, p0, p1, p2, p3: p0 + p1*x + p2* np.power(x, 2) + p3* np.power(x, 3)
nl_params_log = []
stim_resp_log = []
for icell in range(n_cells):
print(icell)
center = dat['centers'][icell, :].astype(np.int)
windx = [np.maximum(center[0]-window, 0), np.minimum(center[0]+window, 80-1)]
windy = [np.maximum(center[1]-window, 0), np.minimum(center[1]+window, 40-1)]
stim_cell = np.reshape(stim_short[:, windx[0]: windx[1], windy[0]: windy[1]], [stim_short.shape[0], -1])
sta_cell = np.reshape(stas[icell, windx[0]: windx[1], windy[0]: windy[1], :], [-1, stas.shape[-1]])
stim_filter = np.zeros(stim_short.shape[0])
for idelay in range(30):
stim_filter[idelay: ] += stim_cell[:T-idelay, :].dot(sta_cell[:, idelay])
# Normalize stim_filter
stim_filter -= np.mean(stim_filter)
stim_filter /= np.sqrt(np.var(stim_filter))
resp_cell = resp_short[:, icell]
stim_nl = []
resp_nl = []
for ipercentile in range(3, 97, 1):
lb = np.percentile(stim_filter, ipercentile-3)
ub = np.percentile(stim_filter, ipercentile+3)
tms = np.logical_and(stim_filter >= lb, stim_filter < ub)
stim_nl += [np.mean(stim_filter[tms])]
resp_nl += [np.mean(resp_cell[tms])]
stim_nl = np.array(stim_nl)
resp_nl = np.array(resp_nl)
popt, pcov = scipy.optimize.curve_fit(f_nl, stim_nl, resp_nl, p0=[1, 0, 0, 0])
nl_params_log += [popt]
stim_resp_log += [[stim_nl, resp_nl]]
nl_params_log = np.array(nl_params_log)
np_params_mean_1 = np.mean(nl_params_log[np.squeeze(dat['cell_type'])==1, :], 0)
np_params_mean_2 = np.mean(nl_params_log[np.squeeze(dat['cell_type'])==2, :], 0)
nl_params_dict = {'nl_params_log': nl_params_log,
'np_params_mean_1': np_params_mean_1,
'np_params_mean_2': np_params_mean_2,
'stim_resp_log': stim_resp_log}
save_dict.update({'nl_params_dict': nl_params_dict})
'''
# Visualize Non-linearities
for icell in range(n_cells):
stim_in = np.arange(-3, 3, 0.1)
fr = f_nl(stim_in, *nl_params_log[icell, :])
if np.squeeze(dat['cell_type'])[icell] == 1:
c = 'r'
else:
c = 'k'
plt.plot(stim_in, fr, c, alpha=0.2)
fr = f_nl(stim_in, *np_params_mean_1)
plt.plot(stim_in, fr, 'r--')
fr = f_nl(stim_in, *np_params_mean_2)
plt.plot(stim_in, fr, 'k--')
'''
pickle.dump(save_dict, gfile.Open(os.path.join(FLAGS.save_folder , dat['piece']), 'w'))
pickle.dump(stas_dict, gfile.Open(os.path.join(FLAGS.save_folder , 'stas' + dat['piece']), 'w'))
def main(argv):
print('\nCode started')
np.random.seed(FLAGS.np_randseed)
random.seed(FLAGS.randseed)
## Load data summary
filename = FLAGS.data_location + 'data_details.mat'
summary_file = gfile.Open(filename, 'r')
data_summary = sio.loadmat(summary_file)
cells = np.squeeze(data_summary['cells'])
if FLAGS.model_id == 'poisson' or FLAGS.model_id == 'logistic':
cells_choose = (cells == 3287) | (cells == 3318) | (cells == 3155) | (
cells == 3066)
if FLAGS.model_id == 'poisson_full':
cells_choose = np.array(np.ones(np.shape(cells)), dtype='bool')
n_cells = np.sum(cells_choose)
tot_spks = np.squeeze(data_summary['tot_spks'])
total_mask = np.squeeze(data_summary['totalMaskAccept_log']).T
tot_spks_chosen_cells = tot_spks[cells_choose]
chosen_mask = np.array(np.sum(total_mask[cells_choose, :], 0) > 0,
dtype='bool')
print(np.shape(chosen_mask))
print(np.sum(chosen_mask))
stim_dim = np.sum(chosen_mask)
print('\ndataset summary loaded')
# use stim_dim, chosen_mask, cells_choose, tot_spks_chosen_cells, n_cells
# decide the number of subunits to fit
n_su = FLAGS.ratio_SU * n_cells
# saving details
#short_filename = 'data_model=ASM_pop_bg'
# short_filename = ('data_model=ASM_pop_batch_sz='+ str(FLAGS.batchsz) + '_n_b_in_c' + str(FLAGS.n_b_in_c) +
# '_step_sz'+ str(FLAGS.step_sz)+'_bg')
# saving details
if FLAGS.model_id == 'poisson':
short_filename = ('data_model=ASM_pop_batch_sz=' + str(FLAGS.batchsz) +
'_n_b_in_c' + str(FLAGS.n_b_in_c) + '_step_sz' +
str(FLAGS.step_sz) + '_bg')
if FLAGS.model_id == 'logistic':
short_filename = ('data_model=' + str(FLAGS.model_id) + '_batch_sz=' +
str(FLAGS.batchsz) + '_n_b_in_c' +
str(FLAGS.n_b_in_c) + '_step_sz' +
str(FLAGS.step_sz) + '_bg')
if FLAGS.model_id == 'poisson_full':
short_filename = ('data_model=' + str(FLAGS.model_id) + '_batch_sz=' +
str(FLAGS.batchsz) + '_n_b_in_c' +
str(FLAGS.n_b_in_c) + '_step_sz' +
str(FLAGS.step_sz) + '_bg')
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
FLAGS.save_location = parent_folder + short_filename + '/'
print(gfile.IsDirectory(FLAGS.save_location))
print(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
with tf.Session() as sess:
# Learn population model!
stim = tf.placeholder(tf.float32, shape=[None, stim_dim], name='stim')
resp = tf.placeholder(tf.float32, name='resp')
data_len = tf.placeholder(tf.float32, name='data_len')
# variables
if FLAGS.model_id == 'poisson' or FLAGS.model_id == 'poisson_full':
w = tf.Variable(
np.array(0.01 * np.random.randn(stim_dim, n_su),
dtype='float32'))
a = tf.Variable(
np.array(0.1 * np.random.rand(n_cells, 1, n_su),
dtype='float32'))
if FLAGS.model_id == 'logistic':
w = tf.Variable(
np.array(0.01 * np.random.randn(stim_dim, n_su),
dtype='float32'))
a = tf.Variable(
np.array(0.01 * np.random.rand(n_su, n_cells),
dtype='float32'))
b_init = np.random.randn(
n_cells
) #np.log((np.sum(response,0))/(response.shape[0]-np.sum(response,0)))
b = tf.Variable(b_init, dtype='float32')
# get relevant files
file_list = gfile.ListDirectory(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
print('\nLoading: ', save_filename)
bin_files = []
meta_files = []
for file_n in file_list:
if re.search(short_filename + '.', file_n):
if re.search('.meta', file_n):
meta_files += [file_n]
else:
bin_files += [file_n]
#print(bin_files)
print(len(meta_files), len(bin_files), len(file_list))
# get iteration numbers
iterations = np.array([])
for file_name in bin_files:
try:
iterations = np.append(
iterations, int(file_name.split('/')[-1].split('-')[-1]))
except:
print('Could not load filename: ' + file_name)
iterations.sort()
print(iterations)
iter_plot = iterations[-1]
print(int(iter_plot))
# load tensorflow variables
saver_var = tf.train.Saver(tf.all_variables())
restore_file = save_filename + '-' + str(int(iter_plot))
saver_var.restore(sess, restore_file)
a_eval = a.eval()
print(np.exp(np.squeeze(a_eval)))
#print(np.shape(a_eval))
# get 2D region to plot
mask2D = np.reshape(chosen_mask, [40, 80])
nz_idx = np.nonzero(mask2D)
np.shape(nz_idx)
print(nz_idx)
ylim = np.array([np.min(nz_idx[0]) - 1, np.max(nz_idx[0]) + 1])
xlim = np.array([np.min(nz_idx[1]) - 1, np.max(nz_idx[1]) + 1])
w_eval = w.eval()
plt.figure()
n_su = w_eval.shape[1]
for isu in np.arange(n_su):
xx = np.zeros((3200))
xx[chosen_mask] = w_eval[:, isu]
fig = plt.subplot(np.ceil(np.sqrt(n_su)), np.ceil(np.sqrt(n_su)),
isu + 1)
plt.imshow(np.reshape(xx, [40, 80]),
interpolation='nearest',
cmap='gray')
plt.ylim(ylim)
plt.xlim(xlim)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
#if FLAGS.model_id == 'logistic' or FLAGS.model_id == 'hinge':
# plt.title(str(a_eval[isu, :]))
#else:
# plt.title(str(np.squeeze(np.exp(a_eval[:, 0, isu]))), fontsize=12)
plt.suptitle('Iteration:' + str(int(iter_plot)) + ' batchSz:' +
str(FLAGS.batchsz) + ' step size:' + str(FLAGS.step_sz),
fontsize=18)
plt.show()
plt.draw()
def main(argv):
#plt.ion() # interactive plotting
window = FLAGS.window
n_pix = (2 * window + 1)**2
dimx = np.floor(1 + ((40 - (2 * window + 1)) / FLAGS.stride)).astype('int')
dimy = np.floor(1 + ((80 - (2 * window + 1)) / FLAGS.stride)).astype('int')
nCells = 107
# load model
# load filename
print(FLAGS.model_id)
with tf.Session() as sess:
if FLAGS.model_id == 'relu':
# lam_c(X) = sum_s(a_cs relu(k_s.x)) , a_cs>0
short_filename = ('data_model=' + str(FLAGS.model_id) + '_lam_w=' +
str(FLAGS.lam_w) + '_lam_a=' + str(FLAGS.lam_a) +
'_ratioSU=' + str(FLAGS.ratio_SU) +
'_grid_spacing=' + str(FLAGS.su_grid_spacing) +
'_normalized_bg')
w = tf.Variable(
np.array(np.random.randn(3200, 749), dtype='float32'))
a = tf.Variable(
np.array(np.random.randn(749, 107), dtype='float32'))
if FLAGS.model_id == 'relu_window':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w = tf.Variable(
np.array(0.1 + 0.05 * np.random.rand(dimx, dimy, n_pix),
dtype='float32')) # exp 5
a = tf.Variable(
np.array(np.random.rand(dimx * dimy, nCells), dtype='float32'))
if FLAGS.model_id == 'relu_window_mother':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w_del = tf.Variable(
np.array(0.1 + 0.05 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'))
w_mother = tf.Variable(
np.array(np.ones((2 * window + 1, 2 * window + 1, 1, 1)),
dtype='float32'))
a = tf.Variable(
np.array(np.random.rand(dimx * dimy, nCells), dtype='float32'))
if FLAGS.model_id == 'relu_window_mother_sfm':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w_del = tf.Variable(
np.array(0.1 + 0.05 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'))
w_mother = tf.Variable(
np.array(np.ones((2 * window + 1, 2 * window + 1, 1, 1)),
dtype='float32'))
a = tf.Variable(
np.array(np.random.rand(dimx * dimy, nCells), dtype='float32'))
if FLAGS.model_id == 'relu_window_mother_sfm_exp':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w_del = tf.Variable(
np.array(0.1 + 0.05 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'))
w_mother = tf.Variable(
np.array(np.ones((2 * window + 1, 2 * window + 1, 1, 1)),
dtype='float32'))
a = tf.Variable(
np.array(np.random.rand(dimx * dimy, nCells), dtype='float32'))
if FLAGS.model_id == 'relu_window_exp':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w = tf.Variable(
np.array(0.01 + 0.005 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'))
a = tf.Variable(
np.array(0.02 + np.random.rand(dimx * dimy, nCells),
dtype='float32'))
if FLAGS.model_id == 'relu_window_mother_exp':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w_del = tf.Variable(
np.array(0.1 + 0.05 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'))
w_mother = tf.Variable(
np.array(np.ones((2 * window + 1, 2 * window + 1, 1, 1)),
dtype='float32'))
a = tf.Variable(
np.array(np.random.rand(dimx * dimy, nCells), dtype='float32'))
if FLAGS.model_id == 'relu_window_a_support':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w = tf.Variable(
np.array(0.001 + 0.0005 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'))
a = tf.Variable(
np.array(0.002 * np.random.rand(dimx * dimy, nCells),
dtype='float32'))
if FLAGS.model_id == 'exp_window_a_support':
short_filename = ('data_model=' + str(FLAGS.model_id) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + '_bg')
w = tf.Variable(
np.array(0.001 + 0.0005 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'))
a = tf.Variable(
np.array(0.002 * np.random.rand(dimx * dimy, nCells),
dtype='float32'))
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
FLAGS.save_location = parent_folder + short_filename + '/'
# get relevant files
file_list = gfile.ListDirectory(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
print('\nLoading: ', save_filename)
bin_files = []
meta_files = []
for file_n in file_list:
if re.search(short_filename + '.', file_n):
if re.search('.meta', file_n):
meta_files += [file_n]
else:
bin_files += [file_n]
#print(bin_files)
print(len(meta_files), len(bin_files), len(file_list))
# get iteration numbers
iterations = np.array([])
for file_name in bin_files:
try:
iterations = np.append(
iterations, int(file_name.split('/')[-1].split('-')[-1]))
except:
print('Could not load filename: ' + file_name)
iterations.sort()
print(iterations)
iter_plot = iterations[-1]
print(int(iter_plot))
# load tensorflow variables
saver_var = tf.train.Saver(tf.all_variables())
restore_file = save_filename + '-' + str(int(iter_plot))
saver_var.restore(sess, restore_file)
# plot subunit - cell connections
plt.figure()
plt.cla()
plt.imshow(a.eval(), cmap='gray', interpolation='nearest')
print(np.shape(a.eval()))
plt.title('Iteration: ' + str(int(iter_plot)))
plt.show()
plt.draw()
# plot all subunits on 40x80 grid
try:
wts = w.eval()
for isu in range(100):
fig = plt.subplot(10, 10, isu + 1)
plt.imshow(np.reshape(wts[:, isu], [40, 80]),
interpolation='nearest',
cmap='gray')
plt.title('Iteration: ' + str(int(iter_plot)))
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
except:
print('w full does not exist? ')
# plot a few subunits - wmother + wdel
try:
wts = w.eval()
print('wts shape:', np.shape(wts))
icnt = 1
for idimx in np.arange(dimx):
for idimy in np.arange(dimy):
fig = plt.subplot(dimx, dimy, icnt)
plt.imshow(np.reshape(np.squeeze(wts[idimx, idimy, :]),
(2 * window + 1, 2 * window + 1)),
interpolation='nearest',
cmap='gray')
icnt = icnt + 1
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.show()
plt.draw()
except:
print('w does not exist?')
# plot wmother
try:
w_mot = np.squeeze(w_mother.eval())
print(w_mot)
plt.imshow(w_mot, interpolation='nearest', cmap='gray')
plt.title('Mother subunit')
plt.show()
plt.draw()
except:
print('w mother does not exist')
# plot wmother + wdel
try:
w_mot = np.squeeze(w_mother.eval())
w_del = np.squeeze(w_del.eval())
wts = np.array(np.random.randn(dimx, dimy, (2 * window + 1)**2))
for idimx in np.arange(dimx):
print(idimx)
for idimy in np.arange(dimy):
wts[idimx,
idimy, :] = np.ndarray.flatten(w_mot) + w_del[idimx,
idimy, :]
except:
print('w mother + w delta do not exist? ')
'''
try:
icnt=1
for idimx in np.arange(dimx):
for idimy in np.arange(dimy):
fig = plt.subplot(dimx, dimy, icnt)
plt.imshow(np.reshape(np.squeeze(wts[idimx, idimy, :]), (2*window+1,2*window+1)), interpolation='nearest', cmap='gray')
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
except:
print('w mother + w delta plotting error? ')
# plot wdel
try:
w_del = np.squeeze(w_del.eval())
icnt=1
for idimx in np.arange(dimx):
for idimy in np.arange(dimy):
fig = plt.subplot(dimx, dimy, icnt)
plt.imshow( np.reshape(w_del[idimx, idimy, :], (2*window+1,2*window+1)), interpolation='nearest', cmap='gray')
icnt = icnt+1
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
except:
print('w delta do not exist? ')
plt.suptitle('Iteration: ' + str(int(iter_plot)))
plt.show()
plt.draw()
'''
# select a cell, and show its subunits.
#try:
## Load data summary, get mask
filename = FLAGS.data_location + 'data_details.mat'
summary_file = gfile.Open(filename, 'r')
data_summary = sio.loadmat(summary_file)
total_mask = np.squeeze(data_summary['totalMaskAccept_log']).T
stas = data_summary['stas']
print(np.shape(total_mask))
# a is 2D
a_eval = a.eval()
print(np.shape(a_eval))
# get softmax numpy
if FLAGS.model_id == 'relu_window_mother_sfm' or FLAGS.model_id == 'relu_window_mother_sfm_exp':
b = np.exp(a_eval) / np.sum(np.exp(a_eval), 0)
else:
b = a_eval
plt.figure()
plt.imshow(b, interpolation='nearest', cmap='gray')
plt.show()
plt.draw()
# plot subunits for multiple cells.
n_cells = 10
n_plots_max = 20
plt.figure()
for icell_cnt, icell in enumerate(np.arange(n_cells)):
mask2D = np.reshape(total_mask[icell, :], [40, 80])
nz_idx = np.nonzero(mask2D)
np.shape(nz_idx)
print(nz_idx)
ylim = np.array([np.min(nz_idx[0]) - 1, np.max(nz_idx[0]) + 1])
xlim = np.array([np.min(nz_idx[1]) - 1, np.max(nz_idx[1]) + 1])
icnt = -1
a_thr = np.percentile(np.abs(b[:, icell]), 99.5)
n_plots = np.sum(np.abs(b[:, icell]) > a_thr)
nx = np.ceil(np.sqrt(n_plots)).astype('int')
ny = np.ceil(np.sqrt(n_plots)).astype('int')
ifig = 0
ww_sum = np.zeros((40, 80))
for idimx in np.arange(dimx):
for idimy in np.arange(dimy):
icnt = icnt + 1
if (np.abs(b[icnt, icell]) > a_thr):
ifig = ifig + 1
fig = plt.subplot(n_cells, n_plots_max,
icell_cnt * n_plots_max + ifig + 2)
ww = np.zeros((40, 80))
ww[idimx * FLAGS.stride:idimx * FLAGS.stride +
(2 * window + 1),
idimy * FLAGS.stride:idimy * FLAGS.stride +
(2 * window + 1)] = b[icnt, icell] * (np.reshape(
wts[idimx, idimy, :],
(2 * window + 1, 2 * window + 1)))
plt.imshow(ww, interpolation='nearest', cmap='gray')
plt.ylim(ylim)
plt.xlim(xlim)
plt.title(b[icnt, icell])
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
ww_sum = ww_sum + ww
fig = plt.subplot(n_cells, n_plots_max,
icell_cnt * n_plots_max + 2)
plt.imshow(ww_sum, interpolation='nearest', cmap='gray')
plt.ylim(ylim)
plt.xlim(xlim)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.title('STA from model')
fig = plt.subplot(n_cells, n_plots_max,
icell_cnt * n_plots_max + 1)
plt.imshow(np.reshape(stas[:, icell], [40, 80]),
interpolation='nearest',
cmap='gray')
plt.ylim(ylim)
plt.xlim(xlim)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.title('True STA')
plt.show()
plt.draw()
#except:
# print('a not 2D?')
# using xlim and ylim, and plot the 'windows' which are relevant with their weights
sq_flat = np.zeros((dimx, dimy))
icnt = 0
for idimx in np.arange(dimx):
for idimy in np.arange(dimy):
sq_flat[idimx, idimy] = icnt
icnt = icnt + 1
n_cells = 1
n_plots_max = 10
plt.figure()
for icell_cnt, icell in enumerate(np.array(
[1, 2, 3, 4, 5])): #enumerate(np.arange(n_cells)):
a_thr = np.percentile(np.abs(b[:, icell]), 99.5)
mask2D = np.reshape(total_mask[icell, :], [40, 80])
nz_idx = np.nonzero(mask2D)
np.shape(nz_idx)
print(nz_idx)
ylim = np.array([np.min(nz_idx[0]) - 1, np.max(nz_idx[0]) + 1])
xlim = np.array([np.min(nz_idx[1]) - 1, np.max(nz_idx[1]) + 1])
print(xlim, ylim)
win_startx = np.ceil((xlim[0] - (2 * window + 1)) / FLAGS.stride)
win_endx = np.floor((xlim[1] - 1) / FLAGS.stride)
win_starty = np.ceil((ylim[0] - (2 * window + 1)) / FLAGS.stride)
win_endy = np.floor((ylim[1] - 1) / FLAGS.stride)
dimx_plot = win_endx - win_startx + 1
dimy_plot = win_endy - win_starty + 1
ww_sum = np.zeros((40, 80))
for irow, idimy in enumerate(np.arange(win_startx, win_endx + 1)):
for icol, idimx in enumerate(
np.arange(win_starty, win_endy + 1)):
fig = plt.subplot(dimx_plot + 1, dimy_plot,
(irow + 1) * dimy_plot + icol + 1)
ww = np.zeros((40, 80))
ww[idimx * FLAGS.stride:idimx * FLAGS.stride +
(2 * window + 1),
idimy * FLAGS.stride:idimy * FLAGS.stride +
(2 * window + 1)] = (np.reshape(
wts[idimx,
idimy, :], (2 * window + 1, 2 * window + 1)))
plt.imshow(ww, interpolation='nearest', cmap='gray')
plt.ylim(ylim)
plt.xlim(xlim)
if b[sq_flat[idimx, idimy], icell] > a_thr:
plt.title(b[sq_flat[idimx, idimy], icell],
fontsize=10,
color='g')
else:
plt.title(b[sq_flat[idimx, idimy], icell],
fontsize=10,
color='r')
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
ww_sum = ww_sum + ww * b[sq_flat[idimx, idimy], icell]
fig = plt.subplot(dimx_plot + 1, dimy_plot, 2)
plt.imshow(ww_sum, interpolation='nearest', cmap='gray')
plt.ylim(ylim)
plt.xlim(xlim)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.title('STA from model')
fig = plt.subplot(dimx_plot + 1, dimy_plot, 1)
plt.imshow(np.reshape(stas[:, icell], [40, 80]),
interpolation='nearest',
cmap='gray')
plt.ylim(ylim)
plt.xlim(xlim)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.title('True STA')
plt.show()
plt.draw()
def main(unused_argv=()):
np.random.seed(23)
tf.set_random_seed(1234)
random.seed(50)
# Load stimulus-response data.
# Collect population response across retinas in the list 'responses'.
# Stimulus for each retina is indicated by 'stim_id',
# which is found in 'stimuli' dictionary.
datasets = gfile.ListDirectory(FLAGS.src_dir)
stimuli = {}
responses = []
for icnt, idataset in enumerate(datasets):
fullpath = os.path.join(FLAGS.src_dir, idataset)
if gfile.IsDirectory(fullpath):
key = 'stim_%d' % icnt
op = data_util.get_stimulus_response(
FLAGS.src_dir,
idataset,
key,
boundary=FLAGS.valid_cells_boundary,
if_get_stim=True)
stimulus, resp, dimx, dimy, _ = op
stimuli.update({key: stimulus})
responses += resp
# Get training and testing partitions.
# Generate partitions
# The partitions for the taskid should be listed in partition_file.
op = partitions.get_partitions(FLAGS.partition_file, FLAGS.taskid)
training_datasets, testing_datasets = op
with tf.Session() as sess:
# Get stimulus-response embedding.
if FLAGS.mode == 0:
is_training = True
if FLAGS.mode == 1:
is_training = True
if FLAGS.mode == 2:
is_training = True
print('NOTE: is_training = True in test')
if FLAGS.mode == 3:
is_training = True
print('NOTE: is_training = True in test')
sample_fcn = sample_datasets
if (FLAGS.sr_model == 'convolutional_embedding'):
embedding = sr_models.convolutional_embedding(
FLAGS.sr_model, sess, is_training, dimx, dimy)
if (FLAGS.sr_model == 'convolutional_embedding_expt'
or FLAGS.sr_model == 'convolutional_embedding_margin_expt' or
FLAGS.sr_model == 'convolutional_embedding_inner_product_expt'
or FLAGS.sr_model == 'convolutional_embedding_gauss_expt'
or FLAGS.sr_model == 'convolutional_embedding_kernel_expt'):
embedding = sr_models_expt.convolutional_embedding_experimental(
FLAGS.sr_model, sess, is_training, dimx, dimy)
if FLAGS.sr_model == 'convolutional_autoembedder':
embedding = sr_models_expt.convolutional_autoembedder(
sess, is_training, dimx, dimy)
if FLAGS.sr_model == 'convolutional_autoembedder_l2':
embedding = sr_models_expt.convolutional_autoembedder(
sess, is_training, dimx, dimy, loss='log_sum_exp')
if FLAGS.sr_model == 'convolutional_encoder' or FLAGS.sr_model == 'convolutional_encoder_2':
embedding = encoding_models_expt.convolutional_encoder(
sess, is_training, dimx, dimy)
if FLAGS.sr_model == 'convolutional_encoder_using_retina_id':
model = encoding_models_expt.convolutional_encoder_using_retina_id
embedding = model(sess, is_training, dimx, dimy, len(responses))
sample_fcn = sample_datasets_2
if (FLAGS.sr_model == 'residual') or (FLAGS.sr_model
== 'residual_inner_product'):
embedding = sr_models_expt.residual_experimental(
FLAGS.sr_model, sess, is_training, dimx, dimy)
if FLAGS.sr_model == 'lin_rank1' or FLAGS.sr_model == 'lin_rank1_blind':
if ((len(training_datasets) != 1)
and (training_datasets != testing_datasets)):
raise ValueError('Identical training/testing data'
' (exactly 1) supported')
n_cells = responses[training_datasets[0]]['responses'].shape[1]
cell_locations = responses[training_datasets[0]]['map_cell_grid']
cell_masks = responses[training_datasets[0]]['mask_cells']
firing_rates = responses[training_datasets[0]]['mean_firing_rate']
cell_type = responses[training_datasets[0]]['cell_type'].squeeze()
model_fn = sr_baseline_models.linear_rank1_models
embedding = model_fn(FLAGS.sr_model,
sess,
dimx,
dimy,
n_cells,
center_locations=cell_locations,
cell_masks=cell_masks,
firing_rates=firing_rates,
cell_type=cell_type,
time_window=30)
# print model graph
PrintModelAnalysis(tf.get_default_graph())
# Get filename, initialize model
file_name = bookkeeping.get_filename(training_datasets,
testing_datasets, FLAGS.beta,
FLAGS.sr_model)
tf.logging.info('Filename: %s' % file_name)
saver_var, start_iter = bookkeeping.initialize_model(
FLAGS.save_folder, file_name, sess)
# Setup summary ops.
# Save separate summary for each retina (both training/testing).
summary_ops = []
for iret in np.arange(len(responses)):
r_list = []
r1 = tf.summary.scalar('loss_%d' % iret, embedding.loss)
r_list += [r1]
if hasattr(embedding, 'accuracy_tf'):
r2 = tf.summary.scalar('accuracy_%d' % iret,
embedding.accuracy_tf)
r_list += [r2]
if FLAGS.sr_model == 'convolutional_autoembedder' or FLAGS.sr_model == 'convolutional_autoembedder_l2':
r3 = tf.summary.scalar('loss_triplet_%d' % iret,
embedding.loss_triplet)
r4 = tf.summary.scalar('loss_stim_decode_from_resp_%d' % iret,
embedding.loss_stim_decode_from_resp)
r5 = tf.summary.scalar('loss_stim_decode_from_stim_%d' % iret,
embedding.loss_stim_decode_from_stim)
r6 = tf.summary.scalar('loss_resp_decode_from_resp_%d' % iret,
embedding.loss_resp_decode_from_resp)
r7 = tf.summary.scalar('loss_resp_decode_from_stim_%d' % iret,
embedding.loss_resp_decode_from_stim)
r_list += [r3, r4, r5, r6, r7]
'''
chosen_stim = 2
bound = FLAGS.valid_cells_boundary
r8 = tf.summary.image('stim_decode_from_stim_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.stim_decode_from_stim[chosen_stim, bound:80-bound, bound:40-bound, 3], 0), 3))
r9 = tf.summary.image('stim_decode_from_resp_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.stim_decode_from_resp[chosen_stim, bound:80-bound, bound:40-bound, 3], 0), 3))
r10 = tf.summary.image('resp_decode_from_stim_chann0_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.resp_decode_from_stim[chosen_stim, bound:80-bound, bound:40-bound, 0], 0), 3))
r11 = tf.summary.image('resp_decode_from_resp_chann0_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.resp_decode_from_resp[chosen_stim, bound:80-bound, bound:40-bound, 0], 0), 3))
r12 = tf.summary.image('resp_decode_from_stim_chann1_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.resp_decode_from_stim[chosen_stim, bound:80-bound, bound:40-bound, 1], 0), 3))
r13 = tf.summary.image('resp_decode_from_resp_chann1_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.resp_decode_from_resp[chosen_stim, bound:80-bound, bound:40-bound, 1], 0), 3))
r14 = tf.summary.image('resp_chann0_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.anchor_model.responses_embed_1[chosen_stim, bound:80-bound, bound:40-bound, 0], 0), 3))
r15 = tf.summary.image('resp_chann1_%d' % iret,
tf.expand_dims(tf.expand_dims(embedding.anchor_model.responses_embed_1[chosen_stim, bound:80-bound, bound:40-bound, 1], 0), 3))
r_list += [r8, r9, r10, r11, r12, r13, r14, r15]
'''
summary_ops += [tf.summary.merge(r_list)]
# Setup summary writers.
summary_writers = []
for loc in ['train', 'test']:
summary_location = os.path.join(FLAGS.save_folder, file_name,
'summary_' + loc)
summary_writer = tf.summary.FileWriter(summary_location,
sess.graph)
summary_writers += [summary_writer]
# Separate tests for encoding or metric learning,
# prosthesis usage or just neuroscience usage.
if FLAGS.mode == 3:
testing.test_encoding(training_datasets, testing_datasets,
responses, stimuli, embedding, sess,
file_name, sample_fcn)
elif FLAGS.mode == 2:
prosthesis.stimulate(embedding, sess, file_name, dimx, dimy)
elif FLAGS.mode == 1:
testing.test_metric(training_datasets, testing_datasets, responses,
stimuli, embedding, sess, file_name)
else:
training.training(start_iter,
sess,
embedding,
summary_writers,
summary_ops,
saver_var,
training_datasets,
testing_datasets,
responses,
stimuli,
file_name,
sample_fcn,
summary_freq=500,
save_freq=500)
def main(argv):
#plt.ion() # interactive plotting
# load model
# load filename
print(FLAGS.model_id)
print(FLAGS.folder_name)
if FLAGS.model_id == 'relu':
# lam_c(X) = sum_s(a_cs relu(k_s.x)) , a_cs>0
short_filename = ('data_model=' + str(FLAGS.model_id) + '_lam_w=' +
str(FLAGS.lam_w) + '_lam_a=' + str(FLAGS.lam_a) +
'_ratioSU=' + str(FLAGS.ratio_SU) +
'_grid_spacing=' + str(FLAGS.su_grid_spacing) +
'_normalized_bg')
if FLAGS.model_id == 'exp':
short_filename = ('data_model3=' + str(FLAGS.model_id) +
'_bias_init=' + str(FLAGS.bias_init_scale) +
'_ratioSU=' + str(FLAGS.ratio_SU) +
'_grid_spacing=' + str(FLAGS.su_grid_spacing) +
'_normalized_bg')
if FLAGS.model_id == 'mel_re_pow2':
short_filename = ('data_model3=' + str(FLAGS.model_id) + '_lam_w=' +
str(FLAGS.lam_w) + '_lam_a=' + str(FLAGS.lam_a) +
'_ratioSU=' + str(FLAGS.ratio_SU) +
'_grid_spacing=' + str(FLAGS.su_grid_spacing) +
'_normalized_bg')
if FLAGS.model_id == 'relu_logistic':
short_filename = ('data_model3=' + str(FLAGS.model_id) + '_lam_w=' +
str(FLAGS.lam_w) + '_lam_a=' + str(FLAGS.lam_a) +
'_ratioSU=' + str(FLAGS.ratio_SU) +
'_grid_spacing=' + str(FLAGS.su_grid_spacing) +
'_normalized_bg')
if FLAGS.model_id == 'relu_proximal':
short_filename = ('data_model3=' + str(FLAGS.model_id) + '_lam_w=' +
str(FLAGS.lam_w) + '_lam_a=' + str(FLAGS.lam_a) +
'_eta_w=' + str(FLAGS.eta_w) + '_eta_a=' +
str(FLAGS.eta_a) + '_ratioSU=' +
str(FLAGS.ratio_SU) + '_grid_spacing=' +
str(FLAGS.su_grid_spacing) + '_proximal_bg')
if FLAGS.model_id == 'relu_eg':
short_filename = ('data_model3=' + str(FLAGS.model_id) + '_lam_w=' +
str(FLAGS.lam_w) + '_eta_w=' + str(FLAGS.eta_w) +
'_eta_a=' + str(FLAGS.eta_a) + '_ratioSU=' +
str(FLAGS.ratio_SU) + '_grid_spacing=' +
str(FLAGS.su_grid_spacing) + '_eg_bg')
# get relevant files
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
FLAGS.save_location = parent_folder + short_filename + '/'
file_list = gfile.ListDirectory(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
bin_files = []
meta_files = []
for file_n in file_list:
if re.search(short_filename + '.', file_n):
if re.search('.meta', file_n):
meta_files += [file_n]
else:
bin_files += [file_n]
#print(bin_files)
print(len(meta_files), len(bin_files), len(file_list))
# get iteration numbers
iterations = np.array([])
for file_name in bin_files:
try:
iterations = np.append(
iterations, int(file_name.split('/')[-1].split('-')[-1]))
except:
print('Bad filename' + file_name)
iterations.sort()
print(iterations)
iter_plot = iterations[-1]
print(int(iter_plot))
with tf.Session() as sess:
# load tensorflow variables
w = tf.Variable(np.array(np.random.randn(3200, 749), dtype='float32'))
a = tf.Variable(np.array(np.random.randn(749, 107), dtype='float32'))
saver_var = tf.train.Saver(tf.all_variables())
restore_file = save_filename + '-' + str(int(iter_plot))
saver_var.restore(sess, restore_file)
# plot subunit - cell connections
plt.figure()
plt.cla()
plt.imshow(a.eval(), cmap='gray', interpolation='nearest')
plt.title('Iteration: ' + str(int(iter_plot)))
plt.show()
plt.draw()
# plot a few subunits
wts = w.eval()
for isu in range(100):
fig = plt.subplot(10, 10, isu + 1)
plt.imshow(np.reshape(wts[:, isu], [40, 80]),
interpolation='nearest',
cmap='gray')
plt.title('Iteration: ' + str(int(iter_plot)))
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.show()
plt.draw()
def main(argv):
np.random.seed(23)
# Figure out dictionary path.
dict_list = gfile.ListDirectory(FLAGS.src_dict)
dict_path = os.path.join(FLAGS.src_dict, dict_list[FLAGS.taskid])
# Load the dictionary
if dict_path[-3:] == 'pkl':
data = pickle.load(gfile.Open(dict_path, 'r'))
if dict_path[-3:] == 'mat':
data = sio.loadmat(gfile.Open(dict_path, 'r'))
#FLAGS.save_dir = '/home/bhaishahster/stimulation_algos/dictionaries/' + dict_list[FLAGS.taskid][:-4]
FLAGS.save_dir = FLAGS.save_dir + dict_list[FLAGS.taskid][:-4]
if not gfile.Exists(FLAGS.save_dir):
gfile.MkDir(FLAGS.save_dir)
# S_collection = data['S'] # Target
A = data['A'] # Decoder
D = data['D'].T # Dictionary
# clean dictionary
thr_list = np.arange(0, 1, 0.01)
dict_val = []
for thr in thr_list:
dict_val += [np.sum(np.sum(D.T > thr, 1) != 0)]
plt.ion()
plt.figure()
plt.plot(thr_list, dict_val)
plt.xlabel('Threshold')
plt.ylabel(
'Number of dictionary elements with \n atleast one element above threshold'
)
plt.title('Please choose threshold')
thr_use = float(input('What threshold to use?'))
plt.axvline(thr_use)
plt.title('Using threshold: %.5f' % thr_use)
dict_valid = np.sum(D.T > thr_use, 1) > 0
D = D[:, dict_valid]
D = np.append(D, np.zeros((D.shape[0], 1)), 1)
print(
'Appending a "dummy" dictionary element that does not activate any cell'
)
# Vary stimulus resolution
for itarget in range(20):
n_targets = 1
for stix_resolution in [32, 64, 16, 8]:
# Get the target
x_dim = int(640 / stix_resolution)
y_dim = int(320 / stix_resolution)
targets = (np.random.rand(y_dim, x_dim, n_targets) < 0.5) - 0.5
upscale = stix_resolution / 8
targets = np.repeat(np.repeat(targets, upscale, axis=0),
upscale,
axis=1)
targets = np.reshape(targets, [-1, targets.shape[-1]])
S_actual = targets[:, 0]
# Remove null component of A from S
S = A.dot(np.linalg.pinv(A).dot(S_actual))
# Run Greedy first to initialize
x_greedy = greedy_stimulation(S,
A,
D,
max_stims=FLAGS.t_max * FLAGS.delta,
file_suffix='%d_%d' %
(stix_resolution, itarget),
save=True,
save_dir=FLAGS.save_dir)
# Load greedy output from previous run
#data_greedy = pickle.load(gfile.Open('/home/bhaishahster/greedy_2000_32_0.pkl', 'r'))
#x_greedy = data_greedy['x_chosen']
# Plan for multiple time points
x_init = np.zeros((x_greedy.shape[0], FLAGS.t_max))
for it in range(FLAGS.t_max):
print((it + 1) * FLAGS.delta - 1)
x_init[:, it] = x_greedy[:, (it + 1) * FLAGS.delta - 1]
#simultaneous_planning(S, A, D, t_max=FLAGS.t_max, lr=FLAGS.learning_rate,
# delta=FLAGS.delta, normalization=FLAGS.normalization,
# file_suffix='%d_%d_normal' % (stix_resolution, itarget), x_init=x_init, save_dir=FLAGS.save_dir)
from IPython import embed
embed()
simultaneous_planning_interleaved_discretization(
S,
A,
D,
t_max=FLAGS.t_max,
lr=FLAGS.learning_rate,
delta=FLAGS.delta,
normalization=FLAGS.normalization,
file_suffix='%d_%d_pgd' % (stix_resolution, itarget),
x_init=x_init,
save_dir=FLAGS.save_dir,
freeze_freq=np.inf,
steps_max=500 * 20 - 1)
# Interleaved discretization.
simultaneous_planning_interleaved_discretization(
S,
A,
D,
t_max=FLAGS.t_max,
lr=FLAGS.learning_rate,
delta=FLAGS.delta,
normalization=FLAGS.normalization,
file_suffix='%d_%d_pgd_od' % (stix_resolution, itarget),
x_init=x_init,
save_dir=FLAGS.save_dir,
freeze_freq=500,
steps_max=500 * 20 - 1)
# Exponential weighing.
simultaneous_planning_interleaved_discretization_exp_gradient(
S,
A,
D,
t_max=FLAGS.t_max,
lr=FLAGS.learning_rate,
delta=FLAGS.delta,
normalization=FLAGS.normalization,
file_suffix='%d_%d_ew' % (stix_resolution, itarget),
x_init=x_init,
save_dir=FLAGS.save_dir,
freeze_freq=np.inf,
steps_max=500 * 20 - 1)
# Exponential weighing with interleaved discretization.
simultaneous_planning_interleaved_discretization_exp_gradient(
S,
A,
D,
t_max=FLAGS.t_max,
lr=FLAGS.learning_rate,
delta=FLAGS.delta,
normalization=FLAGS.normalization,
file_suffix='%d_%d_ew_od' % (stix_resolution, itarget),
x_init=x_init,
save_dir=FLAGS.save_dir,
freeze_freq=500,
steps_max=500 * 20 - 1)
'''
# Plot results
data_fractional = pickle.load(gfile.Open('/home/bhaishahster/2012-09-24-0_SAD_fr1/pgd_20_5.000000_C_100_32_0_normal.pkl', 'r'))
plt.ion()
start = 0
end = 20
error_target_frac = np.linalg.norm(S - data_fractional['S'])
#error_target_greedy = np.linalg.norm(S - data_greedy['S'])
print('Did target change? \n Err from fractional: %.3f ' % error_target_frac)
#'\n Err from greedy %.3f' % (error_target_frac,
# error_target_greedy))
# from IPython import embed; embed()
normalize = np.sum(data_fractional['S'] ** 2)
plt.ion()
plt.plot(np.arange(120, len(data_fractional['f_log'])), data_fractional['f_log'][120:] / normalize, 'b')
plt.axhline(data_fractional['errors'][5:].mean() / normalize, color='m')
plt.axhline(data_fractional['errors_ht_discrete'][5:].mean() / normalize, color='y')
plt.axhline(data_fractional['errors_rr_discrete'][5:].mean() / normalize, color='r')
plt.axhline(data_greedy['error_curve'][120:].mean() / normalize, color='k')
plt.legend(['fraction_curve', 'fractional error', 'HT', 'RR', 'Greedy'])
plt.pause(1.0)
'''
from IPython import embed
embed()
def subunit_discriminability(dataset_dict,
stimuli,
responses,
sr_graph,
num_examples=1000):
## compute distances between s-r pairs - pos and neg.
## negative_stim - if the negative is a stimulus or a response
if num_examples % 100 != 0:
raise ValueError('Only supports examples which are multiples of 100.')
subunit_fit_loc = '/home/bhaishahster/stim-resp_collection_big_wn_retina_subunit_properties_train'
subunits_datasets = gfile.ListDirectory(subunit_fit_loc)
save_dict = {}
datasets_log = {}
for dat_key, datasets in dataset_dict.items():
distances_log = {}
distances_retina_sr_log = []
distances_retina_rr_log = []
for iretina in range(len(datasets)):
# Find the relevant subunit fit
piece = responses[iretina]['piece']
matched_dataset = [
ifit for ifit in subunits_datasets if piece[:12] == ifit[:12]
]
if matched_dataset == []:
raise ValueError('Could not find subunit fit')
subunit_fit_path = os.path.join(subunit_fit_loc,
matched_dataset[0])
# Get predicted spikes.
dat_resp_su = pickle.load(
gfile.Open(
os.path.join(subunit_fit_path, 'response_prediction.pkl'),
'r'))
resp_su = dat_resp_su[
'resp_su'] # it has non-rejected cells as well.
# Remove some cells.
select_cells = [
icell for icell in range(resp_su.shape[2])
if dat_resp_su['cell_ids'][icell] in responses[iretina]
['cellID_list'].squeeze()
]
select_cells = np.array(select_cells)
resp_su = resp_su[:, :, select_cells].astype(np.float32)
# Get stimulus
stimulus = stimuli[responses[iretina]['stimulus_key']]
stimulus_test = stimulus[FLAGS.test_min:FLAGS.test_max, :, :]
responses_recorded_test = responses[iretina]['responses'][
FLAGS.test_min:FLAGS.test_max, :]
# Sample stimuli and responses.
random_times = np.random.randint(40, stimulus_test.shape[0],
num_examples)
batch_size = 100
# Recorded response - predicted response distances.
distances_retina = np.zeros((num_examples, 10)) + np.nan
for Nsub in range(1, 11):
for ibatch in range(
np.floor(num_examples / batch_size).astype(np.int)):
# construct stimulus tensor.
stim_history = 30
resp_pred_batch = np.zeros((batch_size, resp_su.shape[2]))
resp_rec_batch = np.zeros((batch_size, resp_su.shape[2]))
for isample in range(batch_size):
itime = random_times[batch_size * ibatch + isample]
resp_pred_batch[isample, :] = resp_su[Nsub - 1,
itime, :]
resp_rec_batch[isample, :] = responses_recorded_test[
itime, :]
# Embed predicted responses
feed_dict = make_feed_dict(sr_graph,
responses[iretina],
responses=resp_pred_batch)
embed_predicted = sr_graph.sess.run(
sr_graph.anchor_model.responses_embed,
feed_dict=feed_dict)
# Embed recorded responses
feed_dict = make_feed_dict(sr_graph,
responses[iretina],
responses=resp_rec_batch)
embed_recorded = sr_graph.sess.run(
sr_graph.anchor_model.responses_embed,
feed_dict=feed_dict)
dd = sr_graph.sess.run(sr_graph.distances_arbitrary,
feed_dict={
sr_graph.arbitrary_embedding_1:
embed_predicted,
sr_graph.arbitrary_embedding_2:
embed_recorded
})
distances_retina[batch_size * ibatch:batch_size *
(ibatch + 1), Nsub - 1] = dd
print(iretina, Nsub, ibatch)
distances_retina_rr_log += [distances_retina]
# Stimulus - predicted response distances.
distances_retina = np.zeros((num_examples, 10)) + np.nan
for Nsub in range(1, 11):
for ibatch in range(
np.floor(num_examples / batch_size).astype(np.int)):
# construct stimulus tensor.
stim_history = 30
stim_batch = np.zeros(
(batch_size, stimulus_test.shape[1],
stimulus_test.shape[2], stim_history))
resp_batch = np.zeros((batch_size, resp_su.shape[2]))
for isample in range(batch_size):
itime = random_times[batch_size * ibatch + isample]
stim_batch[isample, :, :, :] = np.transpose(
stimulus_test[itime:itime - stim_history:-1, :, :],
[1, 2, 0])
resp_batch[isample, :] = resp_su[Nsub - 1, itime, :]
feed_dict = make_feed_dict(sr_graph, responses[iretina],
resp_batch, stim_batch)
# Get distances
d_pos = sr_graph.sess.run(sr_graph.d_s_r_pos,
feed_dict=feed_dict)
distances_retina[batch_size * ibatch:batch_size *
(ibatch + 1), Nsub - 1] = d_pos
print(iretina, Nsub, ibatch)
distances_retina_sr_log += [distances_retina]
distances_log.update({'rr': distances_retina_rr_log})
distances_log.update({'sr': distances_retina_sr_log})
datasets_log.update({dat_key: distances_log})
save_dict.update({
'datasets_log': datasets_log,
'dataset_dict': dataset_dict
})
return save_dict
def response_transformation_increase_nl(stimuli,
responses,
sr_graph,
time_start_list,
time_len=100,
alpha_list=[1.5, 1.25, 0.8, 0.6]):
# 1. Take an LN model and increase non-linearity.
# How do the points move in response space?
# Load LN models
ln_save_folder = '/home/bhaishahster/stim-resp_collection_ln_model_exp'
files = gfile.ListDirectory(ln_save_folder)
ln_models = []
for ifile in files:
print(ifile)
ln_models += [
pickle.load(gfile.Open(os.path.join(ln_save_folder, ifile), 'r'))
]
t_start_dict = {}
t_min = FLAGS.test_min
t_max = FLAGS.test_max
for time_start in time_start_list:
print('Start time %d' % time_start)
retina_log = []
for iretina_test in range(3, len(responses)):
print('Retina: %d' % iretina_test)
piece_id = responses[iretina_test]['piece']
# find piece in ln_models
matched_ln_model = [
ifile for ifile in range(len(files))
if files[ifile][:12] == piece_id[:12]
]
if len(matched_ln_model) == 0:
print('LN model not found')
continue
if len(matched_ln_model) > 1:
print('More than 1 LN model found')
# Sample a sequence of stimuli and predict spikes
iresp = responses[iretina_test]
iln_model = ln_models[matched_ln_model[0]]
stimulus_test = stimuli[iresp['stimulus_key']]
stim_sample = stimulus_test[time_start:time_start + time_len, :, :]
spikes, lam_np = analysis_utils.predict_responses_ln(
stim_sample,
iln_model['k'],
iln_model['b'],
iln_model['ttf'],
n_trials=1)
spikes_log = np.copy(spikes[0, :, :])
alpha_log = np.ones(time_len)
# Increase nonlinearity, normalize firing rate and embed.
for alpha in alpha_list:
_, lam_np_alpha = analysis_utils.predict_responses_ln(
stim_sample,
alpha * iln_model['k'],
alpha * iln_model['b'],
iln_model['ttf'],
n_trials=1)
correction_firing_rate = np.mean(lam_np) / np.mean(
lam_np_alpha)
correction_b = np.log(correction_firing_rate)
spikes_corrected, lam_np_corrected = analysis_utils.predict_responses_ln(
stim_sample,
alpha * iln_model['k'],
alpha * iln_model['b'] + correction_b,
iln_model['ttf'],
n_trials=1)
print(alpha, np.mean(lam_np), np.mean(lam_np_alpha),
np.mean(lam_np_corrected))
spikes_log = np.append(spikes_log,
spikes_corrected[0, :, :],
axis=0)
alpha_log = np.append(alpha_log,
alpha * np.ones(time_len),
axis=0)
# plt.figure()
# analysis_utils.plot_raster(spikes_corrected[:, :, 23])
# plt.title(alpha)
# Embed responses
try:
resp_trans = np.expand_dims(
spikes_log[:, iresp['valid_cells']], 2)
feed_dict = {
sr_graph.anchor_model.map_cell_grid_tf:
iresp['map_cell_grid'],
sr_graph.anchor_model.cell_types_tf: iresp['ctype_1hot'],
sr_graph.anchor_model.mean_fr_tf:
iresp['mean_firing_rate'],
sr_graph.anchor_model.responses_tf: resp_trans
}
if hasattr(sr_graph.anchor_model, 'dist_nn'):
dist_nn = np.array([
iresp['dist_nn_cell_type'][1],
iresp['dist_nn_cell_type'][2]
]).astype(np.float32)
feed_dict.update({
sr_graph.anchor_model.dist_nn: dist_nn,
sr_graph.neg_model.dist_nn: dist_nn
})
rr = sr_graph.sess.run(sr_graph.anchor_model.responses_embed,
feed_dict=feed_dict)
retina_log += [{
'spikes_log': spikes_log,
'alpha_log': alpha_log,
'resp_embed': rr,
'piece': piece_id
}]
except:
print('Error! ')
retina_log += [np.nan]
pass
t_start_dict.update({time_start: retina_log})
return t_start_dict
def main(unused_argv=()):
## copy data locally
dst = FLAGS.tmp_dir
print('Starting Copy')
if not gfile.IsDirectory(dst):
gfile.MkDir(dst)
files = gfile.ListDirectory(FLAGS.src_dir)
for ifile in files:
ffile = os.path.join(dst, ifile)
if not gfile.Exists(ffile):
gfile.Copy(os.path.join(FLAGS.src_dir, ifile), ffile)
print('Copied %s' % os.path.join(FLAGS.src_dir, ifile))
else:
print('File exists %s' % ffile)
print('File copied to destination')
## load data
# load stimulus
data = h5py.File(os.path.join(dst, 'stimulus.mat'))
stimulus = np.array(data.get('stimulus')) - 0.5
# load responses from multiple retina
datasets_list = os.path.join(dst, 'datasets.txt')
datasets = open(datasets_list, "r").read()
training_datasets = [line for line in datasets.splitlines()]
responses = []
for idata in training_datasets:
print(idata)
data_file = os.path.join(dst, idata)
data = sio.loadmat(data_file)
responses += [data]
print(np.max(data['centers'], 0))
# generate additional features for responses
num_cell_types = 2
dimx = 80
dimy = 40
for iresp in responses:
# remove cells which are outside 80x40 window.
process_dataset(iresp, dimx, dimy, num_cell_types)
## generate graph -
if FLAGS.is_test == 0:
is_training = True
if FLAGS.is_test == 1:
is_training = True # False
with tf.Session() as sess:
## Make graph
# embed stimulus.
time_window = 30
stimx = stimulus.shape[1]
stimy = stimulus.shape[2]
stim_tf = tf.placeholder(tf.float32,
shape=[None, stimx,
stimy, time_window]) # batch x X x Y x time_window
batch_norm = FLAGS.batch_norm
stim_embed = embed_stimulus(FLAGS.stim_layers.split(','),
batch_norm, stim_tf, is_training,
reuse_variables=False)
'''
ttf_tf = tf.Variable(np.ones(time_window).astype(np.float32)/10, name='stim_ttf')
filt = tf.expand_dims(tf.expand_dims(tf.expand_dims(ttf_tf, 0), 0), 3)
stim_time_filt = tf.nn.conv2d(stim_tf, filt,
strides=[1, 1, 1, 1], padding='SAME') # batch x X x Y x 1
ilayer = 0
stim_time_filt = slim.conv2d(stim_time_filt, 1, [3, 3],
stride=1,
scope='stim_layer_wt_%d' % ilayer,
reuse=False,
normalizer_fn=slim.batch_norm,
activation_fn=tf.nn.softplus,
normalizer_params={'is_training': is_training},
padding='SAME')
'''
# embed responses.
num_cell_types = 2
layers = FLAGS.resp_layers # format: window x filters x stride .. NOTE: final filters=1, stride =1 throughout
batch_norm = FLAGS.batch_norm
time_window = 1
anchor_model = conv.ConvolutionalProsthesisScore(sess, time_window=1,
layers=layers,
batch_norm=batch_norm,
is_training=is_training,
reuse_variables=False,
num_cell_types=2,
dimx=dimx, dimy=dimy)
neg_model = conv.ConvolutionalProsthesisScore(sess, time_window=1,
layers=layers,
batch_norm=batch_norm,
is_training=is_training,
reuse_variables=True,
num_cell_types=2,
dimx=dimx, dimy=dimy)
d_s_r_pos = tf.reduce_sum((stim_embed - anchor_model.responses_embed)**2, [1, 2, 3]) # batch
d_pairwise_s_rneg = tf.reduce_sum((tf.expand_dims(stim_embed, 1) -
tf.expand_dims(neg_model.responses_embed, 0))**2, [2, 3, 4]) # batch x batch_neg
beta = 10
# if FLAGS.save_suffix == 'lr=0.001':
loss = tf.reduce_sum(beta * tf.reduce_logsumexp(tf.expand_dims(d_s_r_pos / beta, 1) -
d_pairwise_s_rneg / beta, 1), 0)
# else :
# loss = tf.reduce_sum(tf.nn.softplus(1 + tf.expand_dims(d_s_r_pos, 1) - d_pairwise_s_rneg))
accuracy_tf = tf.reduce_mean(tf.sign(-tf.expand_dims(d_s_r_pos, 1) + d_pairwise_s_rneg))
lr = 0.001
train_op = tf.train.AdagradOptimizer(lr).minimize(loss)
# set up training and testing data
training_datasets_all = [1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15]
testing_datasets = [0, 4, 8, 12]
print('Testing datasets', testing_datasets)
n_training_datasets_log = [1, 3, 5, 7, 9, 11, 12]
if (np.floor(FLAGS.taskid / 4)).astype(np.int) < len(n_training_datasets_log):
# do randomly sampled training data for 0<= FLAGS.taskid < 28
prng = RandomState(23)
n_training_datasets = n_training_datasets_log[(np.floor(FLAGS.taskid / 4)).astype(np.int)]
for _ in range(10*FLAGS.taskid):
print(prng.choice(training_datasets_all,
n_training_datasets, replace=False))
training_datasets = prng.choice(training_datasets_all,
n_training_datasets, replace=False)
# training_datasets = [i for i in range(7) if i< 7-FLAGS.taskid] #[0, 1, 2, 3, 4, 5]
else:
# do 1 training data, chosen in order for FLAGS.taskid >= 28
datasets_all = np.arange(16)
training_datasets = [datasets_all[FLAGS.taskid % (4 * len(n_training_datasets_log))]]
print('Task ID %d' % FLAGS.taskid)
print('Training datasets', training_datasets)
# Initialize stuff.
file_name = ('end_to_end_stim_%s_resp_%s_beta_%d_taskid_%d'
'_training_%s_testing_%s_%s' % (FLAGS.stim_layers,
FLAGS.resp_layers, beta, FLAGS.taskid,
str(training_datasets)[1: -1],
str(testing_datasets)[1: -1],
FLAGS.save_suffix))
saver_var, start_iter = initialize_model(FLAGS.save_folder, file_name, sess)
# print model graph
PrintModelAnalysis(tf.get_default_graph())
# Add summary ops
retina_number = tf.placeholder(tf.int16, name='input_retina');
summary_ops = []
for iret in np.arange(len(responses)):
print(iret)
r1 = tf.summary.scalar('loss_%d' % iret , loss)
r2 = tf.summary.scalar('accuracy_%d' % iret , accuracy_tf)
summary_ops += [tf.summary.merge([r1, r2])]
# tf.summary.scalar('loss', loss)
# tf.summary.scalar('accuracy', accuracy_tf)
# summary_op = tf.summary.merge_all()
# Setup summary writers
summary_writers = []
for loc in ['train', 'test']:
summary_location = os.path.join(FLAGS.save_folder, file_name,
'summary_' + loc )
summary_writer = tf.summary.FileWriter(summary_location, sess.graph)
summary_writers += [summary_writer]
# start training
batch_neg_train_sz = 100
batch_train_sz = 100
def batch(dataset_id):
batch_train = get_batch(stimulus, responses[dataset_id]['responses'],
batch_size=batch_train_sz,
batch_neg_resp=batch_neg_train_sz,
stim_history=30, min_window=10)
stim_batch, resp_batch, resp_batch_neg = batch_train
feed_dict = {stim_tf: stim_batch,
anchor_model.responses_tf: np.expand_dims(resp_batch, 2),
neg_model.responses_tf: np.expand_dims(resp_batch_neg, 2),
anchor_model.map_cell_grid_tf: responses[dataset_id]['map_cell_grid'],
anchor_model.cell_types_tf: responses[dataset_id]['ctype_1hot'],
anchor_model.mean_fr_tf: responses[dataset_id]['mean_firing_rate'],
neg_model.map_cell_grid_tf: responses[dataset_id]['map_cell_grid'],
neg_model.cell_types_tf: responses[dataset_id]['ctype_1hot'],
neg_model.mean_fr_tf: responses[dataset_id]['mean_firing_rate'],
retina_number : dataset_id}
return feed_dict
def batch_few_cells(responses):
batch_train = get_batch(stimulus, responses['responses'],
batch_size=batch_train_sz,
batch_neg_resp=batch_neg_train_sz,
stim_history=30, min_window=10)
stim_batch, resp_batch, resp_batch_neg = batch_train
feed_dict = {stim_tf: stim_batch,
anchor_model.responses_tf: np.expand_dims(resp_batch, 2),
neg_model.responses_tf: np.expand_dims(resp_batch_neg, 2),
anchor_model.map_cell_grid_tf: responses['map_cell_grid'],
anchor_model.cell_types_tf: responses['ctype_1hot'],
anchor_model.mean_fr_tf: responses['mean_firing_rate'],
neg_model.map_cell_grid_tf: responses['map_cell_grid'],
neg_model.cell_types_tf: responses['ctype_1hot'],
neg_model.mean_fr_tf: responses['mean_firing_rate'],
}
return feed_dict
if FLAGS.is_test == 1:
print('Testing')
save_dict = {}
from IPython import embed; embed()
## Estimate one, fix others
'''
grad_resp = tf.gradients(d_s_r_pos, anchor_model.responses_tf)
t_start = 1000
t_len = 100
stim_history = 30
stim_batch = np.zeros((t_len, stimulus.shape[1],
stimulus.shape[2], stim_history))
for isample, itime in enumerate(np.arange(t_start, t_start + t_len)):
stim_batch[isample, :, :, :] = np.transpose(stimulus[itime: itime-stim_history:-1, :, :], [1, 2, 0])
iretina = testing_datasets[0]
resp_batch = np.expand_dims(np.random.rand(t_len, responses[iretina]['responses'].shape[1]), 2)
step_sz = 0.01
eps = 1e-2
dist_prev = np.inf
for iiter in range(10000):
feed_dict = {stim_tf: stim_batch,
anchor_model.map_cell_grid_tf: responses[iretina]['map_cell_grid'],
anchor_model.cell_types_tf: responses[iretina]['ctype_1hot'],
anchor_model.mean_fr_tf: responses[iretina]['mean_firing_rate'],
anchor_model.responses_tf: resp_batch}
dist_np, resp_grad_np = sess.run([d_s_r_pos, grad_resp], feed_dict=feed_dict)
if np.sum(np.abs(dist_prev - dist_np)) < eps:
break
print(np.sum(dist_np), np.sum(np.abs(dist_prev - dist_np)))
dist_prev = dist_np
resp_batch = resp_batch - step_sz * resp_grad_np[0]
resp_batch = resp_batch.squeeze()
'''
# from IPython import embed; embed()
## compute distances between s-r pairs for small number of cells
test_retina = []
for iretina in range(len(testing_datasets)):
dataset_id = testing_datasets[iretina]
num_cells_total = responses[dataset_id]['responses'].shape[1]
dataset_center = responses[dataset_id]['centers'].mean(0)
dataset_cell_distances = np.sqrt(np.sum((responses[dataset_id]['centers'] -
dataset_center), 1))
order_cells = np.argsort(dataset_cell_distances)
test_sr_few_cells = {}
for num_cells_prc in [5, 10, 20, 30, 50, 100]:
num_cells = np.percentile(np.arange(num_cells_total),
num_cells_prc).astype(np.int)
choose_cells = order_cells[:num_cells]
resposnes_few_cells = {'responses': responses[dataset_id]['responses'][:, choose_cells],
'map_cell_grid': responses[dataset_id]['map_cell_grid'][:, :, choose_cells],
'ctype_1hot': responses[dataset_id]['ctype_1hot'][choose_cells, :],
'mean_firing_rate': responses[dataset_id]['mean_firing_rate'][choose_cells]}
# get a batch
d_pos_log = np.array([])
d_neg_log = np.array([])
for test_iter in range(1000):
print(iretina, num_cells_prc, test_iter)
feed_dict = batch_few_cells(resposnes_few_cells)
d_pos, d_neg = sess.run([d_s_r_pos, d_pairwise_s_rneg], feed_dict=feed_dict)
d_neg = np.diag(d_neg) # np.mean(d_neg, 1) #
d_pos_log = np.append(d_pos_log, d_pos)
d_neg_log = np.append(d_neg_log, d_neg)
precision_log, recall_log, F1_log, FPR_log, TPR_log = ROC(d_pos_log, d_neg_log)
print(np.sum(d_pos_log > d_neg_log))
print(np.sum(d_pos_log < d_neg_log))
test_sr= {'precision': precision_log, 'recall': recall_log,
'F1': F1_log, 'FPR': FPR_log, 'TPR': TPR_log,
'd_pos_log': d_pos_log, 'd_neg_log': d_neg_log,
'num_cells': num_cells}
test_sr_few_cells.update({'num_cells_prc_%d' % num_cells_prc : test_sr})
test_retina += [test_sr_few_cells]
save_dict.update({'few_cell_analysis': test_retina})
## compute distances between s-r pairs - pos and neg.
test_retina = []
for iretina in range(len(testing_datasets)):
# stim-resp log
d_pos_log = np.array([])
d_neg_log = np.array([])
for test_iter in range(1000):
print(test_iter)
feed_dict = batch(testing_datasets[iretina])
d_pos, d_neg = sess.run([d_s_r_pos, d_pairwise_s_rneg], feed_dict=feed_dict)
d_neg = np.diag(d_neg) # np.mean(d_neg, 1) #
d_pos_log = np.append(d_pos_log, d_pos)
d_neg_log = np.append(d_neg_log, d_neg)
precision_log, recall_log, F1_log, FPR_log, TPR_log = ROC(d_pos_log, d_neg_log)
print(np.sum(d_pos_log > d_neg_log))
print(np.sum(d_pos_log < d_neg_log))
test_sr = {'precision': precision_log, 'recall': recall_log,
'F1': F1_log, 'FPR': FPR_log, 'TPR': TPR_log,
'd_pos_log': d_pos_log, 'd_neg_log': d_neg_log}
test_retina += [test_sr]
save_dict.update({'test_sr': test_retina})
## ROC curves of responses from repeats - dataset 1
repeats_datafile = '/home/bhaishahster/metric_learning/datasets/2015-09-23-7.mat'
repeats_data = sio.loadmat(gfile.Open(repeats_datafile, 'r'));
repeats_data['cell_type'] = repeats_data['cell_type'].T
# process repeats data
process_dataset(repeats_data, dimx, dimy, num_cell_types)
# analyse and store the result
test_reps = analyse_response_repeats(repeats_data, anchor_model, neg_model, sess)
save_dict.update({'test_reps_2015-09-23-7': test_reps})
## ROC curves of responses from repeats - dataset 2
repeats_datafile = '/home/bhaishahster/metric_learning/examples_pc2005_08_03_0/data005_test.mat'
repeats_data = sio.loadmat(gfile.Open(repeats_datafile, 'r'));
process_dataset(repeats_data, dimx, dimy, num_cell_types)
# analyse and store the result
'''
test_clustering = analyse_response_repeats_all_trials(repeats_data, anchor_model, neg_model, sess)
save_dict.update({'test_reps_2005_08_03_0': test_clustering})
'''
#
# get model params
save_dict.update({'model_pars': sess.run(tf.trainable_variables())})
save_analysis_filename = os.path.join(FLAGS.save_folder, file_name + '_analysis.pkl')
pickle.dump(save_dict, gfile.Open(save_analysis_filename, 'w'))
print(save_analysis_filename)
return
test_iiter = 0
for iiter in range(start_iter, FLAGS.max_iter): # TODO(bhaishahster) :add FLAGS.max_iter
# get a new batch
# stim_tf, anchor_model.responses_tf, neg_model.responses_tf
# training step
train_dataset = training_datasets[iiter % len(training_datasets)]
feed_dict_train = batch(train_dataset)
_, loss_np_train = sess.run([train_op, loss], feed_dict=feed_dict_train)
print(train_dataset, loss_np_train)
# write summary
if iiter % 10 == 0:
# write train summary
test_iiter = test_iiter + 1
train_dataset = training_datasets[test_iiter % len(training_datasets)]
feed_dict_train = batch(train_dataset)
summary_train = sess.run(summary_ops[train_dataset], feed_dict=feed_dict_train)
summary_writers[0].add_summary(summary_train, iiter)
# write test summary
test_dataset = testing_datasets[test_iiter % len(testing_datasets)]
feed_dict_test = batch(test_dataset)
l_test, summary_test = sess.run([loss, summary_ops[test_dataset]], feed_dict=feed_dict_test)
summary_writers[1].add_summary(summary_test, iiter)
print('Test retina: %d, loss: %.3f' % (test_dataset, l_test))
# save model
if iiter % 10 == 0:
save_model(saver_var, FLAGS.save_folder, file_name, sess, iiter)
def main(unused_argv=()):
#np.random.seed(23)
#tf.set_random_seed(1234)
#random.seed(50)
# 1. Load stimulus-response data.
# Collect population response across retinas in the list 'responses'.
# Stimulus for each retina is indicated by 'stim_id',
# which is found in 'stimuli' dictionary.
datasets = gfile.ListDirectory(FLAGS.src_dir)
stimuli = {}
responses = []
for icnt, idataset in enumerate(datasets):
fullpath = os.path.join(FLAGS.src_dir, idataset)
if gfile.IsDirectory(fullpath):
key = 'stim_%d' % icnt
op = data_util.get_stimulus_response(
FLAGS.src_dir,
idataset,
key,
boundary=FLAGS.valid_cells_boundary)
stimulus, resp, dimx, dimy, _ = op
stimuli.update({key: stimulus})
responses += resp
# 2. Do response prediction for a retina
iretina = FLAGS.taskid
subunit_fit_loc = FLAGS.save_folder
subunits_datasets = gfile.ListDirectory(subunit_fit_loc)
piece = responses[iretina]['piece']
matched_dataset = [
ifit for ifit in subunits_datasets if piece[:12] == ifit[:12]
]
if matched_dataset == []:
raise ValueError('Could not find subunit fit')
subunit_fit_path = os.path.join(subunit_fit_loc, matched_dataset[0])
stimulus = stimuli[responses[iretina]['stimulus_key']]
# sample test data
stimulus_test = stimulus[FLAGS.test_min:FLAGS.test_max, :, :]
# Optionally, create a null stimulus for all the cells.
resp_ret = responses[iretina]
if FLAGS.is_null:
# Make null stimulus
stimulus_test = get_null_stimulus(resp_ret, subunit_fit_path,
stimulus_test)
resp_su = get_su_spks(subunit_fit_path, stimulus_test, responses[iretina])
save_dict = {
'resp_su': resp_su.astype(np.int8),
'cell_ids': responses[iretina]['cellID_list'].squeeze()
}
if FLAGS.is_null:
save_dict.update({'stimulus_null': stimulus_test})
save_suff = '_null'
else:
save_suff = ''
pickle.dump(
save_dict,
gfile.Open(
os.path.join(subunit_fit_path,
'response_prediction%s.pkl' % save_suff), 'w'))