def initialize_folder(self, save_location, folder_name):
parent_folder = os.path.join(save_location, folder_name)
# make folder if it does not exist
if not gfile.IsDirectory(parent_folder):
gfile.MkDir(parent_folder)
self.parent_folder = parent_folder
save_location = os.path.join(parent_folder, self.short_filename)
if not gfile.IsDirectory(save_location):
gfile.MkDir(save_location)
self.save_location = save_location
self.save_filename = os.path.join(self.save_location, self.short_filename)
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 initialize_folder(self, save_location, folder_name):
"""Intialize saving location of the model."""
parent_folder = os.path.join(save_location, folder_name)
# Make folder if it does not exist.
if not gfile.IsDirectory(parent_folder):
gfile.MkDir(parent_folder)
self.parent_folder = parent_folder
save_location = os.path.join(parent_folder, self.short_filename)
if not gfile.IsDirectory(save_location):
gfile.MkDir(save_location)
self.save_location = save_location
self.save_filename = os.path.join(self.save_location, self.short_filename)
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 convert_to_TFRecords_chunks(self,
prefix,
save_location='~/tmp',
examples_per_file=1000):
# converts the stimulus-response data into a TFRecords file.
tf.logging.info('making TFRecords chunks')
stimulus = self.stimulus.astype(np.float32)
response = self.response.astype(np.float32)
num_examples = stimulus.shape[0]
num_files = np.ceil(num_examples / examples_per_file).astype(np.int)
tf.logging.info('Number of files: %d, examples: %d' %
(num_files, num_examples))
def _bytes_feature(value):
# value: bytes to convert to tf.train.Feature
return tf.train.Feature(bytes_list=tf.train.BytesList(
value=[value]))
# make folder for storing .tfrecords files
folder_tfrec = os.path.join(save_location, prefix)
# Make folder if it does not exist.
if not gfile.IsDirectory(folder_tfrec):
tf.logging.info('making folder to store tfrecords')
gfile.MkDir(folder_tfrec)
else:
tf.logging.info('folder exists, will overwrite results')
index = -1
for ifile in range(num_files):
filename = os.path.join(folder_tfrec,
'chunk_' + str(ifile) + '.tfrecords')
tf.logging.info('Writing %s , starting index %d' %
(filename, index + 1))
writer = tf.python_io.TFRecordWriter(filename)
for iexample in range(examples_per_file):
index += 1
stimulus_raw = stimulus[index, :].tostring()
response_raw = response[index, :].tostring()
#print(index, stimulus[index,:].shape, response[index,:].shape)
example = tf.train.Example(features=tf.train.Features(
feature={
'stimulus': _bytes_feature(stimulus_raw),
'response': _bytes_feature(response_raw)
}))
writer.write(example.SerializeToString())
writer.close()
def initialize_model(save_folder, file_name, sess):
"""Setup model variables and saving information.
Args:
save_folder (string) : Folder to store model.
Makes one if it does not exist.
filename (string) : Prefix of model/checkpoint files.
sess : Tensorflow session.
"""
# Make folder.
if not gfile.IsDirectory(save_folder):
gfile.MkDir(save_folder)
# Initialize variables.
saver_var, start_iter = initialize_variables(sess, save_folder, file_name)
return saver_var, start_iter
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 RunComputation():
# filename for saving file
if FLAGS.architecture == '2 layer_stimulus':
architecture_string = ('_architecture=' + str(FLAGS.architecture) +
'_stim_downsample_window=' +
str(FLAGS.stim_downsample_window) +
'_stim_downsample_stride=' +
str(FLAGS.stim_downsample_stride))
else:
architecture_string = ('_architecture=' + str(FLAGS.architecture))
short_filename = ('model=' + str(FLAGS.model_id) + '_loss=' +
str(FLAGS.loss) + '_batch_sz=' + str(FLAGS.batchsz) +
'_lam_w=' + str(FLAGS.lam_w) + '_step_sz' +
str(FLAGS.step_sz) + '_tlen=' + str(FLAGS.train_len) +
'_window=' + str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + str(architecture_string) + '_jitter')
# make a folder with name derived from parameters of the algorithm - it saves checkpoint files and summaries used in tensorboard
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
# make folder if it does not exist
if not gfile.IsDirectory(parent_folder):
gfile.MkDir(parent_folder)
FLAGS.save_location = parent_folder + short_filename + '/'
print('Does the file exist?', gfile.IsDirectory(FLAGS.save_location))
if not gfile.IsDirectory(FLAGS.save_location):
gfile.MkDir(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
"""Main function which runs all TensorFlow computations."""
with tf.Graph().as_default() as gra:
with tf.device(tf.ReplicaDeviceSetter(FLAGS.ps_tasks)):
print(FLAGS.config_params)
tf.logging.info(FLAGS.config_params)
# set up training dataset
tc_mean = get_data_mat.init_chunks(FLAGS.n_chunks)
'''
# plot histogram of a training dataset
stim_train, resp_train, train_len = get_data_mat.get_stim_resp('train',
num_chunks=FLAGS.num_chunks_to_load)
plt.hist(np.ndarray.flatten(stim_train[:,:,0:]))
plt.show()
plt.draw()
'''
# Create computation graph.
#
# Graph should be fully constructed before you create supervisor.
# Attempt to modify graph after supervisor is created will cause an error.
with tf.name_scope('model'):
if FLAGS.architecture == '1 layer':
# single GPU model
if False:
global_step = tf.contrib.framework.create_global_step()
model, stim, resp = jitter_model.approximate_conv_jitter(
FLAGS.n_cells, FLAGS.lam_w, FLAGS.window,
FLAGS.stride, FLAGS.step_sz, tc_mean,
FLAGS.su_channels)
# multiGPU model
if True:
model, stim, resp, global_step = jitter_model.approximate_conv_jitter_multigpu(
FLAGS.n_cells, FLAGS.lam_w, FLAGS.window,
FLAGS.stride, FLAGS.step_sz, tc_mean,
FLAGS.su_channels, FLAGS.config_params)
if FLAGS.architecture == '2 layer_stimulus':
# stimulus is first smoothened to lower dimensions, then same model is applied
print(' put stimulus to lower dimenstions!')
model, stim, resp, global_step, stim_tuple = jitter_model.approximate_conv_jitter_multigpu_stim_lr(
FLAGS.n_cells, FLAGS.lam_w, FLAGS.window, FLAGS.stride,
FLAGS.step_sz, tc_mean, FLAGS.su_channels,
FLAGS.config_params, FLAGS.stim_downsample_window,
FLAGS.stim_downsample_stride)
# Print the number of variables in graph
print('Calculating model size') # Hope we do not exceed memory
PrintModelAnalysis(gra, max_depth=10)
#import pdb; pdb.set_trace()
# Builds our summary op.
summary_op = model.merged_summary
# Create a Supervisor. It will take care of initialization, summaries,
# checkpoints, and recovery.
#
# When multiple replicas of this program are running, the first one,
# identified by --task=0 is the 'chief' supervisor. It is the only one
# that takes case of initialization, etc.
is_chief = (FLAGS.task == 0) # & (FLAGS.learn==1)
print(save_filename)
if FLAGS.learn == 1:
# use supervisor only for learning,
# otherwise it messes up data as it tries to store variables while you are doing analysis
sv = tf.train.Supervisor(logdir=save_filename,
is_chief=is_chief,
saver=tf.train.Saver(),
summary_op=None,
save_model_secs=100,
global_step=global_step,
recovery_wait_secs=5)
if (is_chief and FLAGS.learn == 1):
tf.train.write_graph(tf.get_default_graph().as_graph_def(),
save_filename, 'graph.pbtxt')
# Get an initialized, and possibly recovered session. Launch the
# services: Checkpointing, Summaries, step counting.
#
# When multiple replicas of this program are running the services are
# only launched by the 'chief' replica.
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
#import pdb; pdb.set_trace()
sess = sv.PrepareSession(FLAGS.master, config=session_config)
FitComputation(sv, sess, model, stim, resp, global_step,
summary_op, stim_tuple)
sv.Stop()
else:
# if not learn, then analyse
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
with tf.Session(config=session_config) as sess:
saver_var = tf.train.Saver(
tf.all_variables(),
keep_checkpoint_every_n_hours=float('inf'))
restore_file = tf.train.latest_checkpoint(save_filename)
print(restore_file)
start_iter = int(
restore_file.split('/')[-1].split('-')[-1])
saver_var.restore(sess, restore_file)
if FLAGS.architecture == '2 layer_stimulus':
AnalyseModel_lr(sess, model)
else:
AnalyseModel(sv, sess, model)
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):
# global variables will be used for getting training data
global cells_choose
global chosen_mask
global chunk_order
# set random seeds: when same algorithm run with different FLAGS,
# the sequence of random data is same.
np.random.seed(FLAGS.np_randseed)
random.seed(FLAGS.randseed)
# initial chunk order (will be re-shuffled everytime we go over a chunk)
chunk_order = np.random.permutation(np.arange(FLAGS.n_chunks - 1))
# Load data summary
data_filename = FLAGS.data_location + 'data_details.mat'
summary_file = gfile.Open(data_filename, 'r')
data_summary = sio.loadmat(summary_file)
cells = np.squeeze(data_summary['cells'])
# which cells to train subunits for
if FLAGS.all_cells == 'True':
cells_choose = np.array(np.ones(np.shape(cells)), dtype='bool')
else:
cells_choose = (cells == 3287) | (cells == 3318) | (cells == 3155) | (
cells == 3066)
n_cells = np.sum(cells_choose) # number of cells
# load spikes and relevant stimulus pixels for chosen cells
tot_spks = np.squeeze(data_summary['tot_spks'])
tot_spks_chosen_cells = np.array(tot_spks[cells_choose], dtype='float32')
total_mask = np.squeeze(data_summary['totalMaskAccept_log']).T
# chosen_mask = which pixels to learn subunits over
if FLAGS.masked_stimulus == 'True':
chosen_mask = np.array(np.sum(total_mask[cells_choose, :], 0) > 0,
dtype='bool')
else:
chosen_mask = np.array(np.ones(3200).astype('bool'))
stim_dim = np.sum(chosen_mask) # stimulus dimensions
print('\ndataset summary loaded')
# print parameters
print('Save folder name: ' + str(FLAGS.folder_name) + '\nmodel:' +
str(FLAGS.model_id) + '\nLoss:' + str(FLAGS.loss) +
'\nmasked stimulus:' + str(FLAGS.masked_stimulus) + '\nall_cells?' +
str(FLAGS.all_cells) + '\nbatch size' + str(FLAGS.batchsz) +
'\nstep size' + str(FLAGS.step_sz) + '\ntraining length: ' +
str(FLAGS.train_len) + '\nn_cells: ' + str(n_cells))
# decide the number of subunits to fit
n_su = FLAGS.ratio_SU * n_cells
# filename for saving file
short_filename = ('_masked_stim=' + str(FLAGS.masked_stimulus) +
'_all_cells=' + str(FLAGS.all_cells) + '_loss=' +
str(FLAGS.loss) + '_batch_sz=' + str(FLAGS.batchsz) +
'_step_sz' + str(FLAGS.step_sz) + '_tlen=' +
str(FLAGS.train_len) + '_bg')
with tf.Session() as sess:
# set up stimulus and response palceholders
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')
if FLAGS.loss == 'poisson':
b_init = np.array(
0.000001 * np.ones(n_cells)
) # a very small positive bias needed to avoid log(0) in poisson loss
else:
b_init = np.log(
(tot_spks_chosen_cells) / (216000. - tot_spks_chosen_cells)
) # log-odds, a good initialization for some losses (like logistic)
# different firing rate models
if FLAGS.model_id == 'exp_additive':
# This model was implemented for earlier work.
# firing rate for cell c: lam_c = sum_s exp(w_s.x + a_sc)
# filename
short_filename = ('model=' + str(FLAGS.model_id) + short_filename)
# variables
w = tf.Variable(np.array(0.01 * np.random.randn(stim_dim, n_su),
dtype='float32'),
name='w')
a = tf.Variable(np.array(0.01 * np.random.rand(n_cells, 1, n_su),
dtype='float32'),
name='a')
# firing rate model
lam = tf.transpose(tf.reduce_sum(tf.exp(tf.matmul(stim, w) + a),
2))
regularization = 0
vars_fit = [w, a]
def proj(
): # called after every training step - to project to parameter constraints
pass
if FLAGS.model_id == 'relu':
# firing rate for cell c: lam_c = a_c'.relu(w.x) + b
# we know a>0 and for poisson loss, b>0
# for poisson loss: small b added to prevent lam_c going to 0
# filename
short_filename = ('model=' + str(FLAGS.model_id) + '_lam_w=' +
str(FLAGS.lam_w) + '_lam_a=' + str(FLAGS.lam_a) +
'_nsu=' + str(n_su) + short_filename)
# variables
w = tf.Variable(np.array(0.01 * np.random.randn(stim_dim, n_su),
dtype='float32'),
name='w')
a = tf.Variable(np.array(0.01 * np.random.rand(n_su, n_cells),
dtype='float32'),
name='a')
b = tf.Variable(np.array(b_init, dtype='float32'), name='b')
# firing rate model
lam = tf.matmul(tf.nn.relu(tf.matmul(stim, w)), a) + b
vars_fit = [w, a] # which variables are learnt
if not FLAGS.loss == 'poisson': # don't learn b for poisson loss
vars_fit = vars_fit + [b]
# regularization of parameters
regularization = (FLAGS.lam_w * tf.reduce_sum(tf.abs(w)) +
FLAGS.lam_a * tf.reduce_sum(tf.abs(a)))
# projection to satisfy constraints
a_pos = tf.assign(a, (a + tf.abs(a)) / 2)
b_pos = tf.assign(b, (b + tf.abs(b)) / 2)
def proj():
sess.run(a_pos)
if FLAGS.loss == 'poisson':
sess.run(b_pos)
if FLAGS.model_id == 'relu_window':
# firing rate for cell c: lam_c = a_c'.relu(w.x) + b,
# where w_i are over a small window which are convolutionally related with each other.
# we know a>0 and for poisson loss, b>0
# for poisson loss: small b added to prevent lam_c going to 0
# filename
short_filename = ('model=' + str(FLAGS.model_id) + '_window=' +
str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + short_filename)
mask_tf, dimx, dimy, n_pix = get_windows(
) # get convolutional windows
# variables
w = tf.Variable(np.array(0.1 +
0.05 * np.random.rand(dimx, dimy, n_pix),
dtype='float32'),
name='w')
a = tf.Variable(np.array(np.random.rand(dimx * dimy, n_cells),
dtype='float32'),
name='a')
b = tf.Variable(np.array(b_init, dtype='float32'), name='b')
vars_fit = [w, a] # which variables are learnt
if not FLAGS.loss == 'poisson': # don't learn b for poisson loss
vars_fit = vars_fit + [b]
# stimulus filtered with convolutional windows
stim4D = tf.expand_dims(tf.reshape(stim, (-1, 40, 80)), 3)
stim_masked = tf.nn.conv2d(
stim4D,
mask_tf,
strides=[1, FLAGS.stride, FLAGS.stride, 1],
padding="VALID")
stim_wts = tf.nn.relu(tf.reduce_sum(tf.mul(stim_masked, w), 3))
# get firing rate
lam = tf.matmul(tf.reshape(stim_wts, [-1, dimx * dimy]), a) + b
# regularization
regularization = FLAGS.lam_w * tf.reduce_sum(tf.nn.l2_loss(w))
# projection to satisfy hard variable constraints
a_pos = tf.assign(a, (a + tf.abs(a)) / 2)
b_pos = tf.assign(b, (b + tf.abs(b)) / 2)
def proj():
sess.run(a_pos)
if FLAGS.loss == 'poisson':
sess.run(b_pos)
if FLAGS.model_id == 'relu_window_mother':
# firing rate for cell c: lam_c = a_c'.relu(w.x) + b,
# where w_i are over a small window which are convolutionally related with each other.
# w_i = w_mother + w_del_i,
# where w_mother is common accross all 'windows' and w_del is different for different windows.
# we know a>0 and for poisson loss, b>0
# for poisson loss: small b added to prevent lam_c going to 0
# filename
short_filename = ('model=' + str(FLAGS.model_id) + '_window=' +
str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + short_filename)
mask_tf, dimx, dimy, n_pix = get_windows()
# variables
w_del = tf.Variable(np.array(0.05 *
np.random.randn(dimx, dimy, n_pix),
dtype='float32'),
name='w_del')
w_mother = tf.Variable(np.array(np.ones(
(2 * FLAGS.window + 1, 2 * FLAGS.window + 1, 1, 1)),
dtype='float32'),
name='w_mother')
a = tf.Variable(np.array(np.random.rand(dimx * dimy, n_cells),
dtype='float32'),
name='a')
b = tf.Variable(np.array(b_init, dtype='float32'), name='b')
vars_fit = [w_mother, w_del, a] # which variables to learn
if not FLAGS.loss == 'poisson':
vars_fit = vars_fit + [b]
# stimulus filtered with convolutional windows
stim4D = tf.expand_dims(tf.reshape(stim, (-1, 40, 80)), 3)
stim_convolved = tf.reduce_sum(
tf.nn.conv2d(stim4D,
w_mother,
strides=[1, FLAGS.stride, FLAGS.stride, 1],
padding="VALID"), 3)
stim_masked = tf.nn.conv2d(
stim4D,
mask_tf,
strides=[1, FLAGS.stride, FLAGS.stride, 1],
padding="VALID")
stim_del = tf.reduce_sum(tf.mul(stim_masked, w_del), 3)
# activation of differnet subunits
su_act = tf.nn.relu(stim_del + stim_convolved)
# get firing rate
lam = tf.matmul(tf.reshape(su_act, [-1, dimx * dimy]), a) + b
# regularization
regularization = FLAGS.lam_w * tf.reduce_sum(tf.nn.l2_loss(w_del))
# projection to satisfy hard variable constraints
a_pos = tf.assign(a, (a + tf.abs(a)) / 2)
b_pos = tf.assign(b, (b + tf.abs(b)) / 2)
def proj():
sess.run(a_pos)
if FLAGS.loss == 'poisson':
sess.run(b_pos)
if FLAGS.model_id == 'relu_window_mother_sfm':
# firing rate for cell c: lam_c = a_sfm_c'.relu(w.x) + b,
# a_sfm_c = softmax(a) : so a cell cannot be connected to all subunits equally well.
# where w_i are over a small window which are convolutionally related with each other.
# w_i = w_mother + w_del_i,
# where w_mother is common accross all 'windows' and w_del is different for different windows.
# we know a>0 and for poisson loss, b>0
# for poisson loss: small b added to prevent lam_c going to 0
# filename
short_filename = ('model=' + str(FLAGS.model_id) + '_window=' +
str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + short_filename)
mask_tf, dimx, dimy, n_pix = get_windows()
# variables
w_del = tf.Variable(np.array(0.05 *
np.random.randn(dimx, dimy, n_pix),
dtype='float32'),
name='w_del')
w_mother = tf.Variable(np.array(np.ones(
(2 * FLAGS.window + 1, 2 * FLAGS.window + 1, 1, 1)),
dtype='float32'),
name='w_mother')
a = tf.Variable(np.array(np.random.randn(dimx * dimy, n_cells),
dtype='float32'),
name='a')
a_sfm = tf.transpose(tf.nn.softmax(tf.transpose(a)))
b = tf.Variable(np.array(b_init, dtype='float32'), name='b')
vars_fit = [w_mother, w_del, a] # which variables to fit
if not FLAGS.loss == 'poisson':
vars_fit = vars_fit + [b]
# stimulus filtered with convolutional windows
stim4D = tf.expand_dims(tf.reshape(stim, (-1, 40, 80)), 3)
stim_convolved = tf.reduce_sum(
tf.nn.conv2d(stim4D,
w_mother,
strides=[1, FLAGS.stride, FLAGS.stride, 1],
padding="VALID"), 3)
stim_masked = tf.nn.conv2d(
stim4D,
mask_tf,
strides=[1, FLAGS.stride, FLAGS.stride, 1],
padding="VALID")
stim_del = tf.reduce_sum(tf.mul(stim_masked, w_del), 3)
# activation of differnet subunits
su_act = tf.nn.relu(stim_del + stim_convolved)
# get firing rate
lam = tf.matmul(tf.reshape(su_act, [-1, dimx * dimy]), a_sfm) + b
# regularization
regularization = FLAGS.lam_w * tf.reduce_sum(tf.nn.l2_loss(w_del))
# projection to satisfy hard variable constraints
b_pos = tf.assign(b, (b + tf.abs(b)) / 2)
def proj():
if FLAGS.loss == 'poisson':
sess.run(b_pos)
if FLAGS.model_id == 'relu_window_mother_sfm_exp':
# firing rate for cell c: lam_c = exp(a_sfm_c'.relu(w.x)) + b,
# a_sfm_c = softmax(a) : so a cell cannot be connected to all subunits equally well.
# exponential output NL would cancel the log() in poisson and might get better estimation properties.
# where w_i are over a small window which are convolutionally related with each other.
# w_i = w_mother + w_del_i,
# where w_mother is common accross all 'windows' and w_del is different for different windows.
# we know a>0 and for poisson loss, b>0
# for poisson loss: small b added to prevent lam_c going to 0
# filename
short_filename = ('model=' + str(FLAGS.model_id) + '_window=' +
str(FLAGS.window) + '_stride=' +
str(FLAGS.stride) + '_lam_w=' +
str(FLAGS.lam_w) + short_filename)
# get windows
mask_tf, dimx, dimy, n_pix = get_windows()
# declare variables
w_del = tf.Variable(np.array(0.05 *
np.random.randn(dimx, dimy, n_pix),
dtype='float32'),
name='w_del')
w_mother = tf.Variable(np.array(np.ones(
(2 * FLAGS.window + 1, 2 * FLAGS.window + 1, 1, 1)),
dtype='float32'),
name='w_mother')
a = tf.Variable(np.array(np.random.randn(dimx * dimy, n_cells),
dtype='float32'),
name='a')
a_sfm = tf.transpose(tf.nn.softmax(tf.transpose(a)))
b = tf.Variable(np.array(b_init, dtype='float32'), name='b')
vars_fit = [w_mother, w_del, a]
if not FLAGS.loss == 'poisson':
vars_fit = vars_fit + [b]
# filter stimulus
stim4D = tf.expand_dims(tf.reshape(stim, (-1, 40, 80)), 3)
stim_convolved = tf.reduce_sum(
tf.nn.conv2d(stim4D,
w_mother,
strides=[1, FLAGS.stride, FLAGS.stride, 1],
padding="VALID"), 3)
stim_masked = tf.nn.conv2d(
stim4D,
mask_tf,
strides=[1, FLAGS.stride, FLAGS.stride, 1],
padding="VALID")
stim_del = tf.reduce_sum(tf.mul(stim_masked, w_del), 3)
# get subunit activation
su_act = tf.nn.relu(stim_del + stim_convolved)
# get cell firing rates
lam = tf.exp(
tf.matmul(tf.reshape(su_act, [-1, dimx * dimy]), a_sfm)) + b
# regularization
regularization = FLAGS.lam_w * tf.reduce_sum(tf.nn.l2_loss(w_del))
# projection to satisfy hard variable constraints
b_pos = tf.assign(b, (b + tf.abs(b)) / 2)
def proj():
if FLAGS.loss == 'poisson':
sess.run(b_pos)
# different loss functions
if FLAGS.loss == 'poisson':
loss_inter = (tf.reduce_sum(lam) / 120. -
tf.reduce_sum(resp * tf.log(lam))) / data_len
if FLAGS.loss == 'logistic':
loss_inter = tf.reduce_sum(tf.nn.softplus(
-2 * (resp - 0.5) * lam)) / data_len
if FLAGS.loss == 'hinge':
loss_inter = tf.reduce_sum(
tf.nn.relu(1 - 2 * (resp - 0.5) * lam)) / data_len
loss = loss_inter + regularization # add regularization to get final loss function
# training consists of calling training()
# which performs a train step and
# project parameters to model specific constraints using proj()
train_step = tf.train.AdagradOptimizer(FLAGS.step_sz).minimize(
loss, var_list=vars_fit)
def training(inp_dict):
sess.run(train_step,
feed_dict=inp_dict) # one step of gradient descent
proj() # model specific projection operations
# evaluate loss on given data.
def get_loss(inp_dict):
ls = sess.run(loss, feed_dict=inp_dict)
return ls
# saving details
# make a folder with name derived from parameters of the algorithm
# - it saves checkpoint files and summaries used in tensorboard
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
# make folder if it does not exist
if not gfile.IsDirectory(parent_folder):
gfile.MkDir(parent_folder)
FLAGS.save_location = parent_folder + short_filename + '/'
if not gfile.IsDirectory(FLAGS.save_location):
gfile.MkDir(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
# create summary writers
# create histogram summary for all parameters which are learnt
for ivar in vars_fit:
tf.histogram_summary(ivar.name, ivar)
# loss summary
l_summary = tf.scalar_summary('loss', loss)
# loss without regularization summary
l_inter_summary = tf.scalar_summary('loss_inter', loss_inter)
# Merge all the summary writer ops into one op (this way,
# calling one op stores all summaries)
merged = tf.merge_all_summaries()
# training and testing has separate summary writers
train_writer = tf.train.SummaryWriter(FLAGS.save_location + 'train',
sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.save_location + 'test')
## Fitting procedure
print('Start fitting')
sess.run(tf.initialize_all_variables())
saver_var = tf.train.Saver(tf.all_variables(),
keep_checkpoint_every_n_hours=0.05)
load_prev = False
start_iter = 0
try:
# restore previous fits if they are available
# - useful when programs are preempted frequently on .
latest_filename = short_filename + '_latest_fn'
restore_file = tf.train.latest_checkpoint(FLAGS.save_location,
latest_filename)
# restore previous iteration count and start from there.
start_iter = int(restore_file.split('/')[-1].split('-')[-1])
saver_var.restore(sess, restore_file) # restore variables
load_prev = True
except:
print('No previous dataset')
if load_prev:
print('Previous results loaded')
else:
print('Variables initialized')
# Finally, do fitting
icnt = 0
# get test data and make test dictionary
stim_test, resp_test, test_length = get_test_data()
fd_test = {stim: stim_test, resp: resp_test, data_len: test_length}
for istep in np.arange(start_iter, 400000):
print(istep)
# get training data and make test dictionary
stim_train, resp_train, train_len = get_next_training_batch(istep)
fd_train = {
stim: stim_train,
resp: resp_train,
data_len: train_len
}
# take training step
training(fd_train)
if istep % 10 == 0:
# compute training and testing losses
ls_train = get_loss(fd_train)
ls_test = get_loss(fd_test)
latest_filename = short_filename + '_latest_fn'
saver_var.save(sess,
save_filename,
global_step=istep,
latest_filename=latest_filename)
# add training summary
summary = sess.run(merged, feed_dict=fd_train)
train_writer.add_summary(summary, istep)
# add testing summary
summary = sess.run(merged, feed_dict=fd_test)
test_writer.add_summary(summary, istep)
print(istep, ls_train, ls_test)
icnt += FLAGS.batchsz
if icnt > 216000 - 1000:
icnt = 0
tms = np.random.permutation(np.arange(216000 - 1000))
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 RunComputation():
# filename for saving files, derived from FLAGS.
short_filename = get_filename()
# make a folder with name derived from parameters of the algorithm
# it saves checkpoint files and summaries used in tensorboard
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
# make folder if it does not exist
if not gfile.IsDirectory(parent_folder):
gfile.MkDir(parent_folder)
FLAGS.save_location = parent_folder + short_filename + '/'
print('Does the file exist?', gfile.IsDirectory(FLAGS.save_location))
if not gfile.IsDirectory(FLAGS.save_location):
gfile.MkDir(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
if FLAGS.learn == 0:
# for analysis, use smaller batch sizes, so that we can work with single GPU.
FLAGS.batchsz = 600
#Set up tensorflow
with tf.Graph().as_default() as gra:
with tf.device(tf.ReplicaDeviceSetter(FLAGS.ps_tasks)):
print(FLAGS.config_params)
tf.logging.info(FLAGS.config_params)
# set up training dataset
# tc_mean = get_data_mat.init_chunks(FLAGS.n_chunks) <- use this with old get_data_mat
tc_mean = get_data_mat.init_chunks(FLAGS.batchsz)
#plt.plot(tc_mean)
#plt.show()
#plt.draw()
# Create computation graph.
#
# Graph should be fully constructed before you create supervisor.
# Attempt to modify graph after supervisor is created will cause an error.
with tf.name_scope('model'):
if FLAGS.architecture == '1 layer':
# single GPU model
if False:
global_step = tf.contrib.framework.create_global_step()
model, stim, resp = jitter_model.approximate_conv_jitter(
FLAGS.n_cells, FLAGS.lam_w, FLAGS.window,
FLAGS.stride, FLAGS.step_sz, tc_mean,
FLAGS.su_channels)
# multiGPU model
if True:
model, stim, resp, global_step = jitter_model.approximate_conv_jitter_multigpu(
FLAGS.n_cells, FLAGS.lam_w, FLAGS.window,
FLAGS.stride, FLAGS.step_sz, tc_mean,
FLAGS.su_channels, FLAGS.config_params)
if FLAGS.architecture == '2 layer_stimulus':
# stimulus is first smoothened to lower dimensions, then same model is applied
print('First take a low resolution version of stimulus')
model, stim, resp, global_step, stim_tuple = (
jitter_model.approximate_conv_jitter_multigpu_stim_lr(
FLAGS.n_cells, FLAGS.lam_w, FLAGS.window,
FLAGS.stride, FLAGS.step_sz, tc_mean,
FLAGS.su_channels, FLAGS.config_params,
FLAGS.stim_downsample_window,
FLAGS.stim_downsample_stride))
if FLAGS.architecture == 'complex':
print(' Multiple modifications over 2 layered model above')
model, stim, resp, global_step = (
jitter_model_2.
approximate_conv_jitter_multigpu_complex(
FLAGS.n_cells, FLAGS.lam_w, FLAGS.window,
FLAGS.stride, FLAGS.step_sz, tc_mean,
FLAGS.su_channels, FLAGS.config_params,
FLAGS.stim_downsample_window,
FLAGS.stim_downsample_stride))
# Print the number of variables in graph
print('Calculating model size') # Hope we do not exceed memory
PrintModelAnalysis(gra, max_depth=10)
# Builds our summary op.
summary_op = model.merged_summary
# Create a Supervisor. It will take care of initialization, summaries,
# checkpoints, and recovery.
#
# When multiple replicas of this program are running, the first one,
# identified by --task=0 is the 'chief' supervisor. It is the only one
# that takes case of initialization, etc.
is_chief = (FLAGS.task == 0) # & (FLAGS.learn==1)
print(save_filename)
if FLAGS.learn == 1:
# use supervisor only for learning,
# otherwise it messes up data as it tries to store variables while you are doing analysis
sv = tf.train.Supervisor(logdir=save_filename,
is_chief=is_chief,
saver=tf.train.Saver(),
summary_op=None,
save_model_secs=100,
global_step=global_step,
recovery_wait_secs=5)
if (is_chief and FLAGS.learn == 1):
# save graph only if task id =0 (is_chief) and learning the model
tf.train.write_graph(tf.get_default_graph().as_graph_def(),
save_filename, 'graph.pbtxt')
# Get an initialized, and possibly recovered session. Launch the
# services: Checkpointing, Summaries, step counting.
#
# When multiple replicas of this program are running the services are
# only launched by the 'chief' replica.
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
sess = sv.PrepareSession(FLAGS.master, config=session_config)
# Finally, learn the parameters of the model
FitComputation(sv, sess, model, stim, resp, global_step,
summary_op)
sv.Stop()
else:
# Analyse the model
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
with tf.Session(config=session_config) as sess:
# First, recover the model
saver_var = tf.train.Saver(
tf.all_variables(),
keep_checkpoint_every_n_hours=float('inf'))
restore_file = tf.train.latest_checkpoint(save_filename)
print(restore_file)
start_iter = int(
restore_file.split('/')[-1].split('-')[-1])
saver_var.restore(sess, restore_file)
# model specific analysis
if FLAGS.architecture == '2 layer_stimulus':
AnalyseModel_lr(sess, model)
elif FLAGS.architecture == 'complex':
AnalyseModel_complex(sess, model, stim, resp,
save_filename)
else:
AnalyseModel(sv, sess, model)
def main(argv):
# initialize training and testing chunks
init_chunks()
# setup dataset
_,cids_select, n_cells, tc_select, tc_mean = setup_dataset()
# print parameters
print('Save folder name: ' + str(FLAGS.folder_name) +
'\nmodel:' + str(FLAGS.model_id) +
'\nLoss:' + str(FLAGS.loss) +
'\nbatch size' + str(FLAGS.batchsz) +
'\nstep size' + str(FLAGS.step_sz) +
'\ntraining length: ' + str(FLAGS.train_len) +
'\nn_cells: '+str(n_cells))
# filename for saving file
short_filename = ('_loss='+
str(FLAGS.loss) + '_batch_sz='+ str(FLAGS.batchsz) +
'_step_sz'+ str(FLAGS.step_sz) +
'_tlen=' + str(FLAGS.train_len) + '_jitter')
# setup model
with tf.Session() as sess:
# initialize stuff
if FLAGS.loss == 'poisson':
b_init = np.array(0.000001*np.ones(n_cells)) # a very small positive bias needed to avoid log(0) in poisson loss
else:
b_init = np.log((tot_spks_chosen_cells)/(216000. - tot_spks_chosen_cells)) # log-odds, a good initialization for some
# RGB time filter
tm4D = np.zeros((30,1,3,3))
for ichannel in range(3):
tm4D[:,0,ichannel,ichannel] = tc_mean[:,ichannel]
tc = tf.Variable((tm4D).astype('float32'),name = 'tc')
d1=640
d2=320
colors=3
# make data placeholders
stim = tf.placeholder(tf.float32,shape=[None,d1,d2,colors],name='stim')
resp = tf.placeholder(tf.float32,shape=[None,n_cells],name='resp')
data_len = tf.placeholder(tf.float32,name='data_len')
# time convolution
# time course should be time,d1,color,color
# original stimulus is (time, d1,d2,color). Permute it to (d2,time,d1,color) so that 1D convolution could be mimicked using conv_2d.
stim_time_filtered = tf.transpose(tf.nn.conv2d(tf.transpose(stim,(2,0,1,3)),tc, strides=[1,1,1,1], padding='VALID'), (1,2,0,3))
# learn almost convolutional model
short_filename = ('model=' + str(FLAGS.model_id) + short_filename)
mask_tf, dimx, dimy, n_pix = get_windows()
w_del = tf.Variable(np.array( 0.05*np.random.randn(dimx, dimy, n_pix),dtype='float32'), name='w_del')
w_mother = tf.Variable(np.array( np.ones((2 * FLAGS.window + 1, 2 * FLAGS.window + 1, FLAGS.su_channels, 1)),dtype='float32'), name='w_mother')
a = tf.Variable(np.array(np.random.randn(dimx*dimy, n_cells),dtype='float32'), name='a')
a_sfm = tf.transpose(tf.nn.softmax(tf.transpose(a)))
b = tf.Variable(np.array(b_init,dtype='float32'), name='b')
vars_fit = [w_mother, w_del, a] # which variables to fit
if not FLAGS.loss == 'poisson':
vars_fit = vars_fit + [b]
# stimulus filtered with convolutional windows
stim4D = stim_time_filtered#tf.expand_dims(tf.reshape(stim, (-1,40,80)), 3)
stim_convolved = tf.reduce_sum(tf.nn.conv2d(stim4D, w_mother, strides=[1, FLAGS.stride, FLAGS.stride, 1], padding="VALID"),3)
stim_masked = tf.nn.conv2d(stim4D, mask_tf, strides=[1, FLAGS.stride, FLAGS.stride, 1], padding="VALID" )
stim_del = tf.reduce_sum(tf.mul(stim_masked, w_del), 3)
# activation of different subunits
su_act = tf.nn.relu(stim_del + stim_convolved)
# get firing rate
lam = tf.matmul(tf.reshape(su_act, [-1, dimx*dimy]), a_sfm) + b
# regularization
regularization = FLAGS.lam_w * tf.reduce_sum(tf.nn.l2_loss(w_del))
# projection to satisfy hard variable constraints
b_pos = tf.assign(b, (b + tf.abs(b))/2)
def proj():
if FLAGS.loss == 'poisson':
sess.run(b_pos)
if FLAGS.loss == 'poisson':
loss_inter = (tf.reduce_sum(lam)/120. - tf.reduce_sum(resp*tf.log(lam))) / data_len
loss = loss_inter + regularization # add regularization to get final loss function
# training consists of calling training()
# which performs a train step and project parameters to model specific constraints using proj()
train_step = tf.train.AdagradOptimizer(FLAGS.step_sz).minimize(loss, var_list=vars_fit)
def training(inp_dict):
sess.run(train_step, feed_dict=inp_dict) # one step of gradient descent
proj() # model specific projection operations
# evaluate loss on given data.
def get_loss(inp_dict):
ls = sess.run(loss,feed_dict = inp_dict)
return ls
# saving details
# make a folder with name derived from parameters of the algorithm - it saves checkpoint files and summaries used in tensorboard
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
# make folder if it does not exist
if not gfile.IsDirectory(parent_folder):
gfile.MkDir(parent_folder)
FLAGS.save_location = parent_folder + short_filename + '/'
if not gfile.IsDirectory(FLAGS.save_location):
gfile.MkDir(FLAGS.save_location)
save_filename = FLAGS.save_location + short_filename
# create summary writers
# create histogram summary for all parameters which are learnt
for ivar in vars_fit:
tf.histogram_summary(ivar.name, ivar)
# loss summary
l_summary = tf.scalar_summary('loss',loss)
# loss without regularization summary
l_inter_summary = tf.scalar_summary('loss_inter',loss_inter)
# Merge all the summary writer ops into one op (this way, calling one op stores all summaries)
merged = tf.merge_all_summaries()
# training and testing has separate summary writers
train_writer = tf.train.SummaryWriter(FLAGS.save_location + 'train',
sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.save_location + 'test')
## load previous results
sess.run(tf.initialize_all_variables())
saver_var = tf.train.Saver(tf.all_variables(), keep_checkpoint_every_n_hours=0.05)
load_prev = False
start_iter=0
try:
# restore previous fits if they are available - useful when programs are preempted frequently on .
latest_filename = short_filename + '_latest_fn'
restore_file = tf.train.latest_checkpoint(FLAGS.save_location, latest_filename)
start_iter = int(restore_file.split('/')[-1].split('-')[-1]) # restore previous iteration count and start from there.
saver_var.restore(sess, restore_file) # restore variables
load_prev = True
except:
print('No previous dataset')
if load_prev:
print('\nPrevious results loaded')
else:
print('\nVariables initialized')
# Finally, do fitting
# get test data and make test dictionary
stim_test,resp_test,test_length = get_stim_resp('test')
fd_test = {stim: stim_test,
resp: resp_test,
data_len: test_length}
for istep in np.arange(start_iter,400000):
print(istep)
# get training data and make test dictionary
stim_train, resp_train, train_len = get_stim_resp('train')
fd_train = {stim: stim_train,
resp: resp_train,
data_len: train_len}
print('loaded a training set')
# take training step
training(fd_train)
print('performed a training step')
if istep%10 == 0:
# save variables
latest_filename = short_filename + '_latest_fn'
saver_var.save(sess, save_filename, global_step=istep, latest_filename = latest_filename)
# add training summary
summary = sess.run(merged, feed_dict=fd_train)
train_writer.add_summary(summary,istep)
print('training loss calculated')
# add testing summary
summary = sess.run(merged, feed_dict=fd_test)
test_writer.add_summary(summary,istep)
print('testing loss calculated')
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(argv):
print('\nCode started')
global cells_choose
global chosen_mask
np.random.seed(FLAGS.np_randseed)
random.seed(FLAGS.randseed)
global chunk_order
chunk_order = np.random.permutation(np.arange(FLAGS.n_chunks - 1))
## 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' or FLAGS.model_id == 'hinge' or FLAGS.model_id == 'poisson_relu':
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 = np.array(tot_spks[cells_choose], dtype='float32')
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(FLAGS.model_id)
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
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) + '_tlen=' +
str(FLAGS.train_len) + '_bg')
else:
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) + '_tlen=' +
str(FLAGS.train_len) + '_bg')
parent_folder = FLAGS.save_location + FLAGS.folder_name + '/'
if not gfile.IsDirectory(parent_folder):
gfile.MkDir(parent_folder)
FLAGS.save_location = parent_folder + short_filename + '/'
print(gfile.IsDirectory(FLAGS.save_location))
if not gfile.IsDirectory(FLAGS.save_location):
gfile.MkDir(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')
if FLAGS.model_id == 'poisson' or FLAGS.model_id == 'poisson_full':
# variables
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_cells, 1, n_su),
dtype='float32'))
lam = tf.transpose(tf.reduce_sum(tf.exp(tf.matmul(stim, w) + a),
2))
#loss_inter = (tf.reduce_sum(lam/tot_spks_chosen_cells)/120. - tf.reduce_sum(resp*tf.log(lam)/tot_spks_chosen_cells)) / data_len
loss_inter = (tf.reduce_sum(lam) / 120. -
tf.reduce_sum(resp * tf.log(lam))) / data_len
loss = loss_inter
train_step = tf.train.AdagradOptimizer(FLAGS.step_sz).minimize(
loss, var_list=[w, a])
def training(inp_dict):
sess.run(train_step, feed_dict=inp_dict)
def get_loss(inp_dict):
ls = sess.run(loss, feed_dict=inp_dict)
return ls
if FLAGS.model_id == 'poisson_relu':
# variables
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.log(
(tot_spks_chosen_cells) / (216000. - tot_spks_chosen_cells))
b = tf.Variable(b_init, dtype='float32')
f = tf.matmul(
tf.exp(tf.nn.relu(stim, w)), a
) + b #loss_inter = (tf.reduce_sum(lam/tot_spks_chosen_cells)/120. - tf.reduce_sum(resp*tf.log(lam)/tot_spks_chosen_cells)) / data_len
loss_inter = (tf.reduce_sum(f) / 120. -
tf.reduce_sum(resp * tf.log(f))) / data_len
loss = loss_inter
train_step = tf.train.AdagradOptimizer(FLAGS.step_sz).minimize(
loss, var_list=[w, a])
def training(inp_dict):
sess.run(train_step, feed_dict=inp_dict)
def get_loss(inp_dict):
ls = sess.run(loss, feed_dict=inp_dict)
return ls
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.log(
(tot_spks_chosen_cells) / (216000. - tot_spks_chosen_cells))
b = tf.Variable(b_init, dtype='float32')
f = tf.matmul(tf.nn.relu(tf.matmul(stim, w)), a) + b
loss_inter = tf.reduce_sum(tf.nn.softplus(
-2 * (resp - 0.5) * f)) / data_len
loss = loss_inter
train_step = tf.train.AdagradOptimizer(FLAGS.step_sz).minimize(
loss, var_list=[w, a, b])
a_pos = tf.assign(a, (a + tf.abs(a)) / 2)
def training(inp_dict):
sess.run(train_step, feed_dict=inp_dict)
sess.run(a_pos)
def get_loss(inp_dict):
ls = sess.run(loss, feed_dict=inp_dict)
return ls
if FLAGS.model_id == 'hinge':
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.log(
(tot_spks_chosen_cells) / (216000. - tot_spks_chosen_cells))
b = tf.Variable(b_init, dtype='float32')
f = tf.matmul(tf.nn.relu(tf.matmul(stim, w)), a) + b
loss_inter = tf.reduce_sum(tf.nn.relu(1 - 2 *
(resp - 0.5) * f)) / data_len
loss = loss_inter
train_step = tf.train.AdagradOptimizer(FLAGS.step_sz).minimize(
loss, var_list=[w, a, b])
a_pos = tf.assign(a, (a + tf.abs(a)) / 2)
def training(inp_dict):
sess.run(train_step, feed_dict=inp_dict)
sess.run(a_pos)
def get_loss(inp_dict):
ls = sess.run(loss, feed_dict=inp_dict)
return ls
# summaries
l_summary = tf.scalar_summary('loss', loss)
l_inter_summary = tf.scalar_summary('loss_inter', loss_inter)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter(FLAGS.save_location + 'train',
sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.save_location + 'test')
print('\nStarting new code')
sess.run(tf.initialize_all_variables())
saver_var = tf.train.Saver(tf.all_variables(),
keep_checkpoint_every_n_hours=0.05)
load_prev = False
start_iter = 0
try:
latest_filename = short_filename + '_latest_fn'
restore_file = tf.train.latest_checkpoint(FLAGS.save_location,
latest_filename)
start_iter = int(restore_file.split('/')[-1].split('-')[-1])
saver_var.restore(sess, restore_file)
load_prev = True
except:
print('No previous dataset')
if load_prev:
print('\nPrevious results loaded')
else:
print('\nVariables initialized')
# Do the fitting
icnt = 0
stim_test, resp_test, test_length = get_test_data()
fd_test = {stim: stim_test, resp: resp_test, data_len: test_length}
#logfile.close()
for istep in np.arange(start_iter, 400000):
print(istep)
# get training data
stim_train, resp_train, train_len = get_next_training_batch(istep)
fd_train = {
stim: stim_train,
resp: resp_train,
data_len: train_len
}
# take training step
training(fd_train)
if istep % 10 == 0:
# compute training and testing losses
ls_train = get_loss(fd_train)
ls_test = get_loss(fd_test)
latest_filename = short_filename + '_latest_fn'
saver_var.save(sess,
save_filename,
global_step=istep,
latest_filename=latest_filename)
# add training summary
summary = sess.run(merged, feed_dict=fd_train)
train_writer.add_summary(summary, istep)
# add testing summary
summary = sess.run(merged, feed_dict=fd_test)
test_writer.add_summary(summary, istep)
print(istep, ls_train, ls_test)
icnt += FLAGS.batchsz
if icnt > 216000 - 1000:
icnt = 0
tms = np.random.permutation(np.arange(216000 - 1000))
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(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'))