def objective_and_grad(par_data):
params, d, len_voc, rel_list = par_data[0]
data = par_data[1]
params = unroll_params(params, d, len_voc, rel_list)
grads = init_dtrnn_grads(rel_list, d, len_voc)
(rel_dict, Wv, b, L) = params
error_sum = 0.0
num_nodes = 0
tree_size = 0
# compute error and gradient for each tree in minibatch
# also keep track of total number of nodes in minibatch
for index, tree in enumerate(data):
nodes = tree.get_nodes()
for node in nodes:
node.vec = L[:, node.ind].reshape( (d, 1))
tree.ans_vec = L[:, tree.ans_ind].reshape( (d, 1))
prop.forward_prop(params, tree, d)
error_sum += tree.error()
tree_size += len(nodes)
prop.backprop(params[:-1], tree, d, len_voc, grads)
grad = roll_params(grads, rel_list)
return (error_sum, grad, tree_size)
def objective_and_grad(par_data):
params, d, len_voc, rel_list = par_data[0]
data = par_data[1]
params = unroll_params(params, d, len_voc, rel_list)
grads = init_dtrnn_grads(rel_list, d, len_voc)
(rel_dict, Wv, b, L) = params
error_sum = 0.0
num_nodes = 0
tree_size = 0
# compute error and gradient for each tree in minibatch
# also keep track of total number of nodes in minibatch
for index, tree in enumerate(data):
nodes = tree.get_nodes()
for node in nodes:
node.vec = L[:, node.ind].reshape((d, 1))
tree.ans_vec = L[:, tree.ans_ind].reshape((d, 1))
prop.forward_prop(params, tree, d)
error_sum += tree.error()
tree_size += len(nodes)
prop.backprop(params[:-1], tree, d, len_voc, grads)
grad = roll_params(grads, rel_list)
return (error_sum, grad, tree_size)
def objective_and_grad(all_params, trees_batch):
params_train, d, n_classes, len_voc, rel_list = all_params
# returns list of initialized zero gradients which backprop modifies
# rel_Wr, Wv, Wc, b, b_c, We
grads = init_dtrnn_grads(rel_list, d, n_classes, len_voc)
rel_Wr_dict, Wv, Wc, b, b_c, We = params_train
error_sum = 0.0
tree_size = 0
# compute error and gradient for each tree in minibatch
# also keep track of total number of nodes in minibatch
for idx, tree in enumerate(trees_batch):
nodes = tree.get_word_nodes()
for node in nodes:
node.vec = We[:, node.ind].reshape((d, 1))
prop.forward_prop(params_train, tree, d, n_classes)
error_sum += tree.error()
tree_size += len(nodes)
prop.backprop(params_train[:-1], tree, d, n_classes, len_voc, grads)
return error_sum, grads, tree_size
def objective_and_grad(par_data):
params, d, c, len_voc, rel_list = par_data[0]
data = par_data[1]
# returns list of initialized zero gradients which backprop modifies
grads = init_dtrnn_grads(rel_list, d, c, len_voc)
(rel_dict, Wv, Wc, b, b_c, L) = params
error_sum = 0.0
num_nodes = 0
tree_size = 0
# compute error and gradient for each tree in minibatch
# also keep track of total number of nodes in minibatch
for index, tree in enumerate(data):
nodes = tree.get_nodes()
for node in nodes:
node.vec = L[:, node.ind].reshape((d, 1))
prop.forward_prop(params, tree, d, c)
error_sum += tree.error()
tree_size += len(nodes)
prop.backprop(params[:-1], tree, d, c, len_voc, grads)
return (error_sum, grads, tree_size)
def objective_and_grad(par_data):
params, d, c, len_voc, rel_list = par_data[0]
data = par_data[1]
# returns list of initialized zero gradients which backprop modifies
grads = init_dtrnn_grads(rel_list, d, c, len_voc)
(rel_dict, Wv, Wc, b, b_c, L) = params
error_sum = 0.0
num_nodes = 0
tree_size = 0
# compute error and gradient for each tree in minibatch
# also keep track of total number of nodes in minibatch
for index, tree in enumerate(data):
nodes = tree.get_nodes()
for node in nodes:
node.vec = L[:, node.ind].reshape( (d, 1) )
prop.forward_prop(params, tree, d, c)
error_sum += tree.error()
tree_size += len(nodes)
prop.backprop(params[:-1], tree, d, c, len_voc, grads)
return (error_sum, grads, tree_size)
def objective_and_grad(par_data):
#params, d, len_voc, rel_list = par_data[0]
params, d, c, len_voc, rel_list1, rel_list2 = par_data[0]
data = par_data[1]
#params = unroll_params(params, d, len_voc, rel_list)
#return [rel_dict, Wv, Wc, b, b_c, We]
params = unroll_params(params, d, c, len_voc, rel_list1, rel_list2)
# returns list of zero gradients which backprop modifies
#grads = init_dtrnn_grads(rel_list, d, len_voc)
grads = init_dtrnn_grads(rel_list1, rel_list2, d, c, len_voc)
#(rel_dict, Wv, b, L) = params
(rel_dict1, rel_dict2, Wv_1, Wv_2, Wc, b_1, b_2, b_c, L) = params
error_sum = 0.0
num_nodes = 0
tree_size = 0
# compute error and gradient for each tree in minibatch
# also keep track of total number of nodes in minibatch
for index, tree in enumerate(data):
nodes = tree.get_nodes()
for node in nodes:
node.vec = L[:, node.ind].reshape((d, 1))
#tree.ans_vec = L[:, tree.ans_ind].reshape( (d, 1))
#prop.forward_prop(params, tree, d)
prop.forward_prop(params, tree, d, c)
error_sum += tree.error()
tree_size += len(nodes)
#prop.backprop(params[:-1], tree, d, len_voc, grads)
prop.backprop(params[:-1], tree, d, c, len_voc, grads)
grad = roll_params(grads, rel_list1, rel_list2)
return (error_sum, grad, tree_size)
def objective_and_grad(par_data):
#params, d, len_voc, rel_list = par_data[0]
params, d, c, len_voc, rel_list1, rel_list2 = par_data[0]
data = par_data[1]
#params = unroll_params(params, d, len_voc, rel_list)
#return [rel_dict, Wv, Wc, b, b_c, We]
params = unroll_params(params, d, c, len_voc, rel_list1, rel_list2)
# returns list of zero gradients which backprop modifies
#grads = init_dtrnn_grads(rel_list, d, len_voc)
grads = init_dtrnn_grads(rel_list1, rel_list2, d, c, len_voc)
#(rel_dict, Wv, b, L) = params
(rel_dict1, rel_dict2, Wv_1, Wv_2, Wc, b_1, b_2, b_c, L) = params
error_sum = 0.0
num_nodes = 0
tree_size = 0
# compute error and gradient for each tree in minibatch
# also keep track of total number of nodes in minibatch
for index, tree in enumerate(data):
nodes = tree.get_nodes()
for node in nodes:
node.vec = L[:, node.ind].reshape( (d, 1) )
#tree.ans_vec = L[:, tree.ans_ind].reshape( (d, 1))
#prop.forward_prop(params, tree, d)
prop.forward_prop(params, tree, d, c)
error_sum += tree.error()
tree_size += len(nodes)
#prop.backprop(params[:-1], tree, d, len_voc, grads)
prop.backprop(params[:-1], tree, d, c, len_voc, grads)
grad = roll_params(grads, rel_list1, rel_list2)
return (error_sum, grad, tree_size)
