else:
print 'unknown optimization method'
angle=T.constant(0)
# angle=theano.typed_list.TypedListType(T.dscalar)('angle')
for param in all_params:
if param in param2orthogonlize:
delta=updates[param]-param
tan_grad=tangent_grad(param,delta)
angle=angle+T.sqrt((tan_grad**2).sum() / (delta**2).sum())/len(param2orthogonlize)
if PROJ_GRAD==True:
updates[param] = param + tan_grad
retract_updates=[]
for p in param2orthogonlize:
retract_updates.append((p,GAIN*retraction(p)))
J=theano.gradient.jacobian(loss,param2orthogonlize)
hidden=lasagne.layers.get_output(layers_to_concat)
# Compile functions for training and computing output
train = theano.function([l_in.input_var, target], [loss,angle,grad_norm], updates=updates,allow_input_downcast=True)
probe_loss = theano.function([l_in.input_var, target], loss,allow_input_downcast=True)
probe_J = theano.function([l_in.input_var, target], J,allow_input_downcast=True)
retract_w = theano.function([], [], updates=retract_updates,allow_input_downcast=True)
get_output = theano.function([l_in.input_var], network_output,allow_input_downcast=True)
probe_hidden = theano.function([l_in.input_var], hidden,allow_input_downcast=True)
track_train_error=[]
track_valid_error=[]
track_test_error=[]
updates[l_rec.W_hid_to_hid] - l_rec.W_hid_to_hid)
updates[l_rec.W_hid_to_hid] = l_rec.W_hid_to_hid + new_update
# Theano functions for training and computing cost
train = theano.function(
[l_in.input_var, target_values, l_mask.input_var],
loss, updates=updates)
# Accuracy is defined as the proportion of examples whose absolute
# error is less than .04
accuracy = T.mean(abs(predicted_values - target_values) < .04)
# Theano function for computing accuracy
compute_accuracy = theano.function(
[l_in.input_var, target_values, l_mask.input_var], accuracy)
# Function for orthogonalizing weight matrix
retract_w = theano.function(
[], [],
updates={l_rec.W_hid_to_hid: util.retraction(l_rec.W_hid_to_hid)})
# Keep track of the number of samples used to train
samples_trained = 0
# Did we converge?
success = True
# Store cost over minibatches
cost = 0
while samples_trained < N_SAMPLES:
# Generate a batch of data
X, y, mask = task_options[task](sequence_length, BATCH_SIZE)
cost += train(X.astype(theano.config.floatX),
y.astype(theano.config.floatX),
mask.astype(theano.config.floatX))
# Quit when a non-finite value is found
if any([not np.isfinite(cost),
any([not np.all(np.isfinite(p.get_value()))
