def __init__(self, config, variables, train=False):
self.config = config
self.v = variables
self.g = nn.ComputationalGraph(nodes=self.v.vs)
self.lm = config.lm
self.attention_has_been_set_up = False
self.dropout = self.config.p.dropout if train else None
self.train = train
# add in regularization if the regularization
# is not zero.
if self.config.p.regularization is not None:
reg_losses = [
nn.L2Node(self.config.p.regularization, var, self.g)
for var in self.v.rvs
]
self.loss = nn.AddNode(reg_losses, self.g)
else:
self.loss = nn.ConstantNode(0.0, graph=self.g)
# self.attention_memory should be a list of the
# intermediate states for the GRU block:
# self.attention_memory[i][j] is the ith input symbol
# at the jth layer
if self.config.p.attention:
self.attention_memory = []
def __init__(self, variables, config, proof_step, train=False):
''' this is the model. As a single pass, it processes the
inputs, and computes the losses, and runs a training step if
train.
'''
# we just defined the proof step as a triple
(tree, hyps, correct_output) = proof_step
DefaultModel.__init__(self, config, variables, train=train)
# fix the random seed
if not self.train:
np.random.seed(tree.size() + 100 * len(hyps) + 10000 * correct_output)
correct_score = self.get_score(
tree, hyps, None
)
wrong_score = nn.ConstantNode(np.array([0.0]), self.g)
correct_output = 1*correct_output
logits = nn.ConcatNode([wrong_score, correct_score], self.g)
cross_entropy = nn.SoftmaxCrossEntropyLoss(correct_output, logits, self.g)
self.loss = nn.AddNode([self.loss, cross_entropy], self.g)
accuracy = 1 * (np.argmax(logits.value) == correct_output)
self.outputs = [cross_entropy.value, accuracy, 1-correct_output]
self.output_counts = [1, 1, 1]
# perform the backpropagation if we are training
if train:
self.g.backprop(self.loss)
def get_score(self, statement, hyps, f):
in_string, in_parents, in_left, in_right, in_params, depths, \
parent_arity, leaf_position, arity = self.parse_statement_and_hyps(
statement, hyps, f)
#print in_string
to_middle = self.gru_block(self.v.forward_start,
in_string,
in_params,
hs_backward=self.v.backward_start,
parents=in_parents,
left_siblings=in_left,
right_siblings=in_right,
bidirectional=self.config.p.bidirectional,
structure_data=list(
zip(depths, parent_arity, leaf_position,
arity)),
feed_to_attention=False)
h = nn.ConcatNode(to_middle, self.g)
h = nn.DropoutNode(h, self.dropout, self.g)
h = nn.RELUDotAdd(h, self.v.main_first_W, self.v.main_first_b, self.g)
h = nn.DropoutNode(h, self.dropout, self.g)
for i in range(self.config.p.out_layers):
h = nn.RELUDotAdd(h, self.v.main_Ws[i], self.v.main_bs[i], self.g)
h = nn.DropoutNode(h, self.dropout, self.g)
h = nn.DotNode(h, self.v.last_W, self.g)
h = nn.AddNode([h, self.v.last_b], self.g)
return h
def __init__(self, variables, config, proof_step, train=False):
''' this is the model. As a single pass, it processes the
inputs, and computes the losses, and runs a training step if
train.
'''
DefaultModel.__init__(self, config, variables, train=train)
# fix the random seed
if not self.train:
np.random.seed(proof_step.context.number +
+proof_step.prop.number + proof_step.tree.size())
main = self.main_get_vector(proof_step.tree, proof_step.context.hyps,
proof_step.context.f)
main = nn.DotNode(main, self.v.W, self.g)
# get a list [right prop, wrong prop 0, ..., wrong_prop n]
props = self.get_props(proof_step)
###DEBUG
#if not self.train: print [p.label for p in props]
###DEBUG
out_vectors = [
self.prop_get_vector(prop.tree, prop.hyps, prop.f)
for prop in props
]
stacked = nn.StackNode(out_vectors, self.g)
stacked = nn.TransposeInPlaceNode(stacked, self.g)
logits = nn.DotNode(main, stacked, self.g)
cross_entropy = nn.SoftmaxCrossEntropyLoss(0, logits, self.g)
self.loss = nn.AddNode([self.loss, cross_entropy], self.g)
accuracy = 1 * (np.argmax(logits.value) == 0)
self.outputs = [cross_entropy.value, accuracy, 1.0 / len(props)]
self.output_counts = [1, 1, 1]
# perform the backpropagation if we are training
if train:
self.g.backprop(self.loss)
def __init__(self,
variables,
config,
proof_step,
train=False,
target_index=None):
''' this is the model. As a single pass, it processes the
inputs, and computes the losses, and runs a training step if
train.
'''
DefaultModel.__init__(self, config, variables, train=train)
if not self.train:
np.random.seed(proof_step.context.number +
+proof_step.prop.number + proof_step.tree.size())
self.parse_and_augment_proof_step(proof_step,
target_index=target_index)
# merge the inputs together so that we can bidirection it
in_string, in_parents, in_left, in_right, in_params, depths, parent_arity, leaf_position, arity = merge_graph_structures(
[self.known_graph_structure, self.to_prove_graph_structure],
[self.v.known_gru_block, self.v.to_prove_gru_block])
# print
# print in_string
# print in_parents
# print in_left
# print in_right
# print depths
# print parent_arity
# print leaf_position
# print arity
# do the left side gru blocks
to_middle = self.gru_block(self.v.forward_start,
in_string,
in_params,
hs_backward=self.v.backward_start,
parents=in_parents,
left_siblings=in_left,
right_siblings=in_right,
bidirectional=self.config.p.bidirectional,
structure_data=list(
zip(depths, parent_arity, leaf_position,
arity)),
feed_to_attention=self.config.p.attention)
# set up the attentional model
if self.config.p.attention:
self.set_up_attention()
# process the middle
from_middle = [
nn.RELUDotAdd(x, W, b, self.g)
for x, W, b in zip(to_middle, self.v.middle_W, self.v.middle_b)
]
# process the right side
out_string = self.out_graph_structure.string
out_parents = self.out_graph_structure.parents
out_left = self.out_graph_structure.left_sibling
arity = self.out_graph_structure.arity
leaf_position = self.out_graph_structure.leaf_position
parent_arity = self.out_graph_structure.parent_arity
depths = self.out_graph_structure.depth
structure_data = list(zip(depths, parent_arity, leaf_position, arity))
out_length = len(out_string)
out_xs = []
all_hs = []
hs = from_middle
for i in range(out_length):
# figure out the augmentation stuff.
if self.config.p.augmentation:
parent = out_parents[i]
parent_hs = [x.no_parent for x in self.v.out_gru_block.aug
] if parent == -1 else all_hs[parent]
left = out_left[i]
left_hs = [
x.no_left_sibling for x in self.v.out_gru_block.aug
] if left == -1 else all_hs[left]
else:
parent_hs = None
left_hs = None
hs, x = self.forward_vertical_slice(
hs,
parent_hs,
left_hs,
out_string[i],
self.v.out_gru_block.forward,
structure_data[i],
takes_attention=self.config.p.attention)
all_hs.append(hs)
out_xs.append(x)
# test
# calculate logits and score
self.correct_string = out_string[1:] + ['END_OF_SECTION']
#out_xs = [nn.ZerosNode([64], self.g) for token in correct_string]
self.all_correct = True
self.num_correct = 0
self.all_logits = [self.x_to_predictions(x) for x in out_xs]
self.prediction = []
all_costs = [
self.score(logits, c_token)
for logits, c_token in zip(self.all_logits, self.correct_string)
]
perplexity = nn.AddNode(all_costs, self.g)
#self.logit_matrix = np.concat([l.value for l in all_logits])
self.loss = nn.AddNode([perplexity, self.loss], self.g)
# and train
if train:
# print 'training2'
# print len(self.v.vs), len(self.g.nodes)
self.g.backprop(self.loss)
# for v in self.v.vs:
# print v.name, np.mean(v.value ** 2)
#self.v.optimizer.minimize()
# put the outputs in the standard training format
self.outputs = [
perplexity.value, self.num_correct, 1 * self.all_correct
]
self.output_counts = [out_length, out_length, 1]
