feed_dict = {}
for t in xrange(ntimesteps):
feed_dict[inputs_ph_t[t]] = np.reshape(u_1xt[:,t], (batch_size,-1))
states_t_bxn, outputs_t_bxn = sess.run([states_t, outputs_t],
feed_dict=feed_dict)
states_nxt = np.transpose(np.squeeze(np.asarray(states_t_bxn)))
outputs_t_bxn = np.squeeze(np.asarray(outputs_t_bxn))
r_sxt = np.dot(P_nxn, states_nxt)
for s in xrange(nspikifications):
data_e.append(r_sxt)
u_e.append(u_1xt)
outs_e.append(outputs_t_bxn)
truth_data_e = normalize_rates(data_e, E, N)
spiking_data_e = spikify_data(truth_data_e, rng, dt=FLAGS.dt,
max_firing_rate=FLAGS.max_firing_rate)
train_inds, valid_inds = get_train_n_valid_inds(E, train_percentage,
nspikifications)
data_train_truth, data_valid_truth = split_list_by_inds(truth_data_e,
train_inds,
valid_inds)
data_train_spiking, data_valid_spiking = split_list_by_inds(spiking_data_e,
train_inds,
valid_inds)
data_train_truth = nparray_and_transpose(data_train_truth)
data_valid_truth = nparray_and_transpose(data_valid_truth)
feed_dict = {}
for t in xrange(ntimesteps):
feed_dict[inputs_ph_t[t]] = np.reshape(u_1xt[:,t], (batch_size,-1))
states_t_bxn, outputs_t_bxn = sess.run([states_t, outputs_t],
feed_dict=feed_dict)
states_nxt = np.transpose(np.squeeze(np.asarray(states_t_bxn)))
outputs_t_bxn = np.squeeze(np.asarray(outputs_t_bxn))
r_sxt = np.dot(P_nxn, states_nxt)
for s in xrange(nreplications):
data_e.append(r_sxt)
u_e.append(u_1xt)
outs_e.append(outputs_t_bxn)
truth_data_e = normalize_rates(data_e, E, N)
spiking_data_e = spikify_data(truth_data_e, rng, dt=FLAGS.dt,
max_firing_rate=FLAGS.max_firing_rate)
train_inds, valid_inds = get_train_n_valid_inds(E, train_percentage,
nreplications)
data_train_truth, data_valid_truth = split_list_by_inds(truth_data_e,
train_inds,
valid_inds)
data_train_spiking, data_valid_spiking = split_list_by_inds(spiking_data_e,
train_inds,
valid_inds)
data_train_truth = nparray_and_transpose(data_train_truth)
data_valid_truth = nparray_and_transpose(data_valid_truth)