def fit_mlp_models(n_hidden=25,
learning_rate=0.15,
n_epochs=100,
batch_size=600,
L1_reg=0.0e-9,
L2_reg=0.0e-9):
exp_type = 'natural'
movie_type = 'movie'
spatial_downsample_factor = 4
n_lag = 13
saved_models_dir = './temp/mlp-models-%d' % n_hidden
predicted_responses_dir = './temp/mlp-predicted-responses-%s' % n_hidden
if not os.path.isdir(saved_models_dir):
os.makedirs(saved_models_dir)
else:
shutil.rmtree(saved_models_dir)
os.makedirs(saved_models_dir)
if not os.path.isdir(predicted_responses_dir):
os.makedirs(predicted_responses_dir)
else:
shutil.rmtree(predicted_responses_dir)
os.makedirs(predicted_responses_dir)
print '%d hidden layer neurons, %d epochs to train for' % (n_hidden,
n_epochs)
responses = load_responses(exp_type)
movies = load_movies(exp_type,
movie_type,
downsample_factor=spatial_downsample_factor)
mlp_training_errors = []
for i, response in enumerate(responses):
name = response.name
print 'Mouse %s' % name
print 'Splitting out training and test data...'
tr_rsp, tst_rsp, tr_mov, tst_mov = train_test_split(response,
movies,
'even',
train_frac=0.8)
print 'Splitting out training and validation data...'
tr_rsp, val_rsp, tr_mov, val_mov = train_test_split(tr_rsp,
tr_mov,
'even',
train_frac=0.9)
tr_rsp = smooth_responses(tr_rsp)
val_rsp = smooth_responses(val_rsp)
tst_rsp = smooth_responses(tst_rsp)
train_set_x, train_set_y = window_matrices(tr_rsp, tr_mov, n_lag)
valid_set_x, valid_set_y = window_matrices(val_rsp, val_mov, n_lag)
test_set_x, test_set_y = window_matrices(tst_rsp, tst_mov, n_lag)
model = RegressionModel(model_name=name,
n_hidden=n_hidden,
learning_rate=learning_rate,
n_epochs=n_epochs,
batch_size=batch_size,
L1_reg=L1_reg,
L2_reg=L2_reg)
model.setup_with_data([(train_set_x, train_set_y),
(valid_set_x, valid_set_y),
(test_set_x, test_set_y)])
test_error = model.train()
mlp_training_errors.append(test_error)
predicted = model.y_pred()
np.save(os.path.join(predicted_responses_dir, 'pred_%s' % name),
predicted)
with open(os.path.join(saved_models_dir, 'mlp_%s' % name), 'wb') as f:
pickle.dump(model.regression.params, f)
with open(os.path.join(saved_models_dir, 'train_errors'), 'wb') as f:
pickle.dump(mlp_training_errors, f)
pred[i,:] = net.run(X_val[i])
print mean_absolute_error(np.array(Y_val), pred)
return net
################################################################################
################################################################################
exp_type = 'natural'
movie_type = 'movie'
spatial_downsample_factor = 4
# time_downsample_factor = 5
n_lag = 6
split_type = 'even'
saved_nns_dir = './temp/nets'
responses = load_responses(exp_type)
movies = load_movies(exp_type, movie_type,
downsample_factor=spatial_downsample_factor)
train_test_splits = map(lambda r: train_test_split(r, movies, split_type,
train_frac=0.7,
to_leave_out=0),
responses)
nns = []
for i, response in enumerate(responses):
name = response.name
print 'Mouse %s' % name
print 'Splitting out training and test data...'
tr_rsp, val_rsp, tr_mov, val_mov = train_test_splits[i]
from src.io import load_responses, load_movies
from src.data_manip_utils import train_test_split, train_test_split_grating
from src.data_manip_utils import confusion_matrix, confusion_matrix_grating
from src.data_manip_utils import cm_goodness
from src.data_manip_utils import smooth_responses
################################################################################
################################################################################
exp_type = 'natural'
movie_type = 'movie'
downsample_factor = 4
split_type = 'movie_index_list'
n_clusters = 5
responses = map(smooth_responses, load_responses(exp_type))
n_movies = [m.data.shape[0]/5 for m in responses]
train_idxs = [range(n) for n in n_movies]
k = 1
test_idxs = [range(k * n, (k+1) * n) for n in n_movies]
"""
for r in responses:
r.clustering = NeuronClustering(n_clusters, signal_correlation)
r.clustering.fit(r.data)
r.data = r.clustering.cluster_response(r.data)
"""
# movies = load_movies(exp_type, movie_type, downsample_factor=downsample_factor)
movies = [None] * 25
from src.params.naturalmovies.stimulus_params import CA_SAMPLING_RATE
elif exp_type == 'grating':
from src.params.grating.datafile_params import PLOTS_DIR
from src.params.grating.stimulus_params import CA_SAMPLING_RATE
movie_type = 'movie'
downsample_factor = 8
n_lag = 13
n_clusters = 4
n_components = 16
split_type = 'loo'
model_name = 'linear-regression'
model_type = 'reverse'
regularisation = None
responses = map(smooth_responses, load_responses(exp_type))
movies = load_movies(exp_type, movie_type, downsample_factor=downsample_factor)
for i, response in enumerate(responses):
if i > 0:
break
name = response.name
print 'Mouse %s' % name
print 'Splitting out training and test data...'
tr_rsp, te_rsp, tr_mov, te_mov = train_test_split(response, movies,
split_type,
train_frac=0.7,
to_leave_out=0)
def fit_mlp_models(n_hidden=25, learning_rate=0.15, n_epochs=100,
batch_size=600, L1_reg=0.0e-9, L2_reg = 0.0e-9):
exp_type = 'natural'
movie_type = 'movie'
spatial_downsample_factor = 4
n_lag = 13
saved_models_dir = './temp/mlp-models-%d' % n_hidden
predicted_responses_dir = './temp/mlp-predicted-responses-%s' % n_hidden
if not os.path.isdir(saved_models_dir):
os.makedirs(saved_models_dir)
else:
shutil.rmtree(saved_models_dir)
os.makedirs(saved_models_dir)
if not os.path.isdir(predicted_responses_dir):
os.makedirs(predicted_responses_dir)
else:
shutil.rmtree(predicted_responses_dir)
os.makedirs(predicted_responses_dir)
print '%d hidden layer neurons, %d epochs to train for'%(n_hidden,n_epochs)
responses = load_responses(exp_type)
movies = load_movies(exp_type, movie_type,
downsample_factor=spatial_downsample_factor)
mlp_training_errors = []
for i, response in enumerate(responses):
name = response.name
print 'Mouse %s' % name
print 'Splitting out training and test data...'
tr_rsp, tst_rsp, tr_mov, tst_mov = train_test_split(response, movies,
'even', train_frac=0.8)
print 'Splitting out training and validation data...'
tr_rsp, val_rsp, tr_mov, val_mov = train_test_split(tr_rsp, tr_mov,
'even', train_frac=0.9)
tr_rsp = smooth_responses(tr_rsp)
val_rsp = smooth_responses(val_rsp)
tst_rsp = smooth_responses(tst_rsp)
train_set_x, train_set_y = window_matrices(tr_rsp, tr_mov, n_lag)
valid_set_x, valid_set_y = window_matrices(val_rsp, val_mov, n_lag)
test_set_x, test_set_y = window_matrices(tst_rsp, tst_mov, n_lag)
model = RegressionModel(model_name=name, n_hidden=n_hidden,
learning_rate=learning_rate,
n_epochs=n_epochs, batch_size=batch_size,
L1_reg=L1_reg, L2_reg=L2_reg)
model.setup_with_data([(train_set_x, train_set_y),
(valid_set_x, valid_set_y),
(test_set_x, test_set_y)])
test_error = model.train()
mlp_training_errors.append(test_error)
predicted = model.y_pred()
np.save(os.path.join(predicted_responses_dir, 'pred_%s' % name),
predicted)
with open(os.path.join(saved_models_dir,'mlp_%s' % name), 'wb') as f:
pickle.dump(model.regression.params, f)
with open(os.path.join(saved_models_dir, 'train_errors'), 'wb') as f:
pickle.dump(mlp_training_errors, f)
print mean_absolute_error(np.array(Y_val), pred)
return net
################################################################################
################################################################################
exp_type = 'natural'
movie_type = 'movie'
spatial_downsample_factor = 4
# time_downsample_factor = 5
n_lag = 6
split_type = 'even'
saved_nns_dir = './temp/nets'
responses = load_responses(exp_type)
movies = load_movies(exp_type,
movie_type,
downsample_factor=spatial_downsample_factor)
train_test_splits = map(
lambda r: train_test_split(
r, movies, split_type, train_frac=0.7, to_leave_out=0), responses)
nns = []
for i, response in enumerate(responses):
name = response.name
print 'Mouse %s' % name
print 'Splitting out training and test data...'
tr_rsp, val_rsp, tr_mov, val_mov = train_test_splits[i]
from src.io import load_responses
from src.response import Response
from src.reliability import reliability
from src.correlation import signal_correlation, noise_correlation
from src.clustering import NeuronClustering
from src.data_manip_utils import smooth_responses
n_clusters = 3
exp_type = 'natural'
if exp_type == 'natural':
from src.params.naturalmovies.datafile_params import PLOTS_DIR
elif exp_type == 'grating':
from src.params.grating.datafile_params import PLOTS_DIR
data = load_responses(exp_type)
for index, m in enumerate(data):
print 'Mouse %s' % m.name
if exp_type == 'natural':
m.data = m.data[:5,:,:,:]
data[index] = smooth_responses(m)
m = data[index]
m.clustering = NeuronClustering(n_clusters, signal_correlation)
m.clustering.fit(m.data)
m.data = m.clustering.divnorm(m.data)
S, N, L, R = m.data.shape
# Computing signal correlation.