def plot_representations(ladder, params_dicts):
train_data_stream, _, _ = get_streams(60000, 60000)
data = train_data_stream.get_epoch_iterator().next()
cg = ComputationGraph([ladder.costs.total])
f = theano.function(
[cg.inputs[0]],
[ladder.wz[-1], ladder.wu[-1], ladder.wzu[-1], ladder.ests[-1]])
wz, wu, wzu, est = f(data[0])
plt.imshow(np.vstack([
np.swapaxes(data[0][8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(est[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(wz[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(wu[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(wzu[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(
np.vstack([params_dicts['g_0_a1'].get_value()
for i in range(10)]).reshape(10, 28, 28), 0,
1).reshape(28, 280)
]),
cmap=plt.gray(),
interpolation='nearest',
vmin=0,
vmax=1)
plt.savefig('est.png')
def plot_representations(ladder, params_dicts):
train_data_stream, _, _ = get_streams(60000, 60000)
data = train_data_stream.get_epoch_iterator().next()
cg = ComputationGraph([ladder.costs.total])
f = theano.function(
[cg.inputs[0]],
[ladder.wz[-1], ladder.wu[-1], ladder.wzu[-1], ladder.ests[-1]])
wz, wu, wzu, est = f(data[0])
plt.imshow(
np.vstack(
[np.swapaxes(
data[0][8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(
est[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(
wz[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(
wu[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(
wzu[8:18].reshape(10, 28, 28), 0, 1).reshape(28, 280),
np.swapaxes(
np.vstack([params_dicts['g_0_a1'].get_value() for i in
range(10)]).reshape(10, 28, 28), 0, 1).reshape(
28, 280)]),
cmap=plt.gray(), interpolation='nearest', vmin=0, vmax=1)
plt.savefig('est.png')
def compute_noises(ladder):
cg = ComputationGraph([ladder.costs.total])
f_clean = theano.function([cg.inputs[0]], ladder.clean_zs)
f_corr = theano.function([cg.inputs[0]], ladder.corr_zs)
train_data_stream, _, _ = get_streams(60000, 60000)
data = train_data_stream.get_epoch_iterator().next()
rs_clean = f_clean(data[0])
rs_corr = f_corr(data[0])
import ipdb; ipdb.set_trace()
stds = [np.std(rs_clean[i] - rs_corr[i])
for i in range(len(ladder.corr_zs))]
print stds
means = [np.mean(rs_clean[i] - rs_corr[i])
for i in range(len(ladder.corr_zs))]
print means
def compute_noises(ladder):
cg = ComputationGraph([ladder.costs.total])
f_clean = theano.function([cg.inputs[0]], ladder.clean_zs)
f_corr = theano.function([cg.inputs[0]], ladder.corr_zs)
train_data_stream, _, _ = get_streams(60000, 60000)
data = train_data_stream.get_epoch_iterator().next()
rs_clean = f_clean(data[0])
rs_corr = f_corr(data[0])
import ipdb
ipdb.set_trace()
stds = [
np.std(rs_clean[i] - rs_corr[i]) for i in range(len(ladder.corr_zs))
]
print stds
means = [
np.mean(rs_clean[i] - rs_corr[i]) for i in range(len(ladder.corr_zs))
]
print means
