def hyperloss(transform_vect, i_hyper):
RS = RandomState((seed, i_hyper, "hyperloss"))
cur_train_data, cur_valid_data = random_partition(
train_data, RS, [10, 2])
z_vect_final = train_z(cur_train_data, transform_vect, RS)
w_vect_final = transform_weights(z_vect_final, transform_vect)
return likelihood_loss(w_vect_final, cur_valid_data) / N_scripts
def hyperloss(transform_vect, i_hyper):
RS = RandomState((seed, i_hyper, "hyperloss"))
cur_train_data, cur_valid_data = random_partition(train_data, RS, [10, 2])
z_vect_final = train_z(cur_train_data, transform_vect, RS)
w_vect_final = transform_weights(z_vect_final, transform_vect)
return likelihood_loss(w_vect_final, cur_valid_data) / N_scripts
def run():
RS = RandomState((seed, "top_rs"))
all_data = omniglot.load_flipped_alphabets()
train_data, tests_data = random_partition(all_data, RS, [12, 3])
w_parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weights = w_parser.vect.size
script_parser = VectorParser()
for i_script in range(N_scripts):
script_parser[i_script] = np.zeros(N_weights)
transform_parser = make_transform([0] * N_layers)
def get_layers(vect):
layers = []
for i_layer in range(N_layers):
weights_by_scripts = vect.reshape((N_scripts, N_weights))
weights_idxs, _ = w_parser.idxs_and_shapes[('weights', i_layer)]
biases_idxs, _ = w_parser.idxs_and_shapes[('biases', i_layer)]
assert weights_idxs.stop == biases_idxs.start
layer_idxs = slice(weights_idxs.start, biases_idxs.stop)
layers.append(weights_by_scripts[:, layer_idxs])
return layers
def transform_weights(z_vect, transform_vect):
z_layers = get_layers(z_vect)
transform = transform_parser.new_vect(transform_vect)
w_layers = [np.dot(transform[i], z) for i, z in enumerate(z_layers)]
return np.concatenate(w_layers, axis=1).ravel()
def likelihood_loss(w_vect, data):
w = script_parser.new_vect(w_vect)
return sum([
loss_fun(w[i], **script_data) for i, script_data in enumerate(data)
])
def regularization(z_vect):
return np.dot(z_vect, z_vect) * np.exp(log_L2)
def train_z(data, transform_vect, RS):
def primal_loss(z_vect,
transform_vect,
i_primal,
record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = likelihood_loss(w_vect, data)
reg = regularization(z_vect)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal,
getval(loss) / N_scripts)
return loss + reg
z_vect_0 = RS.randn(script_parser.vect.size) * np.exp(log_init_scale)
return sgd(grad(primal_loss), transform_vect, z_vect_0, alpha, beta,
N_iters)
def train_sharing():
def hyperloss(transform_vect, i_hyper):
RS = RandomState((seed, i_hyper, "hyperloss"))
cur_train_data, cur_valid_data = random_partition(
train_data, RS, [10, 2])
z_vect_final = train_z(cur_train_data, transform_vect, RS)
w_vect_final = transform_weights(z_vect_final, transform_vect)
return likelihood_loss(w_vect_final, cur_valid_data) / N_scripts
hypergrad = grad(hyperloss)
cur_transform_vect = make_transform([init_script_corr] * N_layers).vect
for i_hyper in range(N_meta_iter):
print "Hyper iter {0}".format(i_hyper)
grad_transform = hypergrad(cur_transform_vect, i_hyper)
cur_transform_vect = cur_transform_vect - grad_transform * meta_alpha
return cur_transform_vect
transform_vects, train_losses, tests_losses = {}, {}, {}
transform_vects['no_sharing'] = make_transform([0, 0, 0]).vect
transform_vects['full_sharing'] = make_transform([1, 0, 0]).vect
transform_vects['learned_sharing'] = train_sharing()
for name in transform_vects.keys():
RS = RandomState("final_training")
tv = transform_vects[name]
trained_z = train_z(train_data, tv, RS)
trained_w = transform_weights(trained_z, tv)
train_losses[name] = likelihood_loss(trained_w, train_data) / N_scripts
tests_losses[name] = likelihood_loss(trained_w, tests_data) / N_scripts
print "{0} : train: {1}, test: {2}".format(name, train_losses[name],
tests_losses[name])
return transform_parser, transform_vects, train_losses, tests_losses
def run():
RS = RandomState((seed, "top_rs"))
all_data = omniglot.load_flipped_alphabets()
train_data, tests_data = random_partition(all_data, RS, [12, 3])
w_parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weights = w_parser.vect.size
script_parser = VectorParser()
for i_script in range(N_scripts):
script_parser[i_script] = np.zeros(N_weights)
transform_parser = make_transform([0] * N_layers)
def get_layers(vect):
layers = []
for i_layer in range(N_layers):
weights_by_scripts = vect.reshape((N_scripts, N_weights))
weights_idxs, _ = w_parser.idxs_and_shapes[('weights', i_layer)]
biases_idxs, _ = w_parser.idxs_and_shapes[('biases', i_layer)]
assert weights_idxs.stop == biases_idxs.start
layer_idxs = slice(weights_idxs.start, biases_idxs.stop)
layers.append(weights_by_scripts[:, layer_idxs])
return layers
def transform_weights(z_vect, transform_vect):
z_layers = get_layers(z_vect)
transform = transform_parser.new_vect(transform_vect)
w_layers = [np.dot(transform[i], z) for i, z in enumerate(z_layers)]
return np.concatenate(w_layers, axis=1).ravel()
def likelihood_loss(w_vect, data):
w = script_parser.new_vect(w_vect)
return sum([loss_fun(w[i], **script_data) for i, script_data in enumerate(data)])
def regularization(z_vect):
return np.dot(z_vect, z_vect) * np.exp(log_L2)
def train_z(data, transform_vect, RS):
def primal_loss(z_vect, transform_vect, i_primal, record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = likelihood_loss(w_vect, data)
reg = regularization(z_vect)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss) / N_scripts)
return loss + reg
z_vect_0 = RS.randn(script_parser.vect.size) * np.exp(log_init_scale)
return sgd(grad(primal_loss), transform_vect, z_vect_0, alpha, beta, N_iters)
def train_sharing():
def hyperloss(transform_vect, i_hyper):
RS = RandomState((seed, i_hyper, "hyperloss"))
cur_train_data, cur_valid_data = random_partition(train_data, RS, [10, 2])
z_vect_final = train_z(cur_train_data, transform_vect, RS)
w_vect_final = transform_weights(z_vect_final, transform_vect)
return likelihood_loss(w_vect_final, cur_valid_data) / N_scripts
hypergrad = grad(hyperloss)
cur_transform_vect = make_transform([init_script_corr] * N_layers).vect
for i_hyper in range(N_meta_iter):
print "Hyper iter {0}".format(i_hyper)
grad_transform = hypergrad(cur_transform_vect, i_hyper)
cur_transform_vect = cur_transform_vect - grad_transform * meta_alpha
return cur_transform_vect
transform_vects, train_losses, tests_losses = {}, {}, {}
transform_vects['no_sharing'] = make_transform([0, 0, 0]).vect
transform_vects['full_sharing'] = make_transform([1, 0, 0]).vect
transform_vects['learned_sharing'] = train_sharing()
for name in transform_vects.keys():
RS = RandomState("final_training")
tv = transform_vects[name]
trained_z = train_z(train_data, tv, RS)
trained_w = transform_weights(trained_z, tv)
train_losses[name] = likelihood_loss(trained_w, train_data) / N_scripts
tests_losses[name] = likelihood_loss(trained_w, tests_data) / N_scripts
print "{0} : train: {1}, test: {2}".format(name, train_losses[name], tests_losses[name])
return transform_parser, transform_vects, train_losses, tests_losses
