for [
k_conv, n_conv1, n_conv2, n_lstm, n_layer,
test_frac, val_frac, batch_frac, n_sensors,
n_dipoles
] in params_list:
if cnn is 'fft' or subject_id is 'rat':
n_chan_in = 1
else:
n_chan_in = 2
if subject_id is 'aud':
meas_dims, m, p, n_steps, total_batch_size, Wt = nn_prepro.aud_dataset(
justdims=True,
cnn=cnn,
locate=locate,
treat=None)
meas_dims, m, p, n_steps, total_batch_size, Wt = nn_prepro.aud_dataset(
justdims=True,
cnn=cnn,
locate=locate,
treat=treat,
Wt=Wt)
elif subject_id is 'rat':
total_batch_size = 1000
delT = 1e-2
n_steps = 100
meas_dims_in = [n_sensors, 1]
dipole_dims = [1, 1, n_dipoles]
if cnn is True:
print num
return ax, fig
pca = True
subsample = 1
pca = True
cnn = True
locate = 95
subject_id = 7
if subject_id is 'aud':
treats = [
None
] #, 'left/auditory', 'right/auditory', 'left/visual', 'right/visual']
else:
treats = [None] #, 'face/famous','scrambled','face/unfamiliar']
nummax = len(treats)
ax = None
fig = None
num = 0
for treat in treats:
if subject_id is 'aud':
meas_dims, m, p, n_steps, total_batch_size, Wt = nn_prepro.aud_dataset(
justdims=True, cnn=cnn, locate=locate, treat=treat)
else:
meas_dims, m, p, n_steps, total_batch_size, Wt = nn_prepro.faces_dataset(
subject_id, cnn=cnn, justdims=True, locate=locate, treat=treat)