Python theano.tensor 模块，bmatrix() 实例源码
我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用theano.tensor.bmatrix()。
def make_node(self, state, time):
"""Creates an Apply node representing the application of the op on
the inputs provided.
Parameters
----------
state : array_like
The state to transform into feature space
time : int
The current time being processed
Returns
-------
theano.Apply
[description]
"""
state = T.as_tensor_variable(state)
time = T.as_tensor_variable(time)
return theano.Apply(self, [state, time], [T.bmatrix()])
def test_all_grad(self):
x = tensor.bmatrix('x')
x_all = x.all()
gx = theano.grad(x_all, x)
f = theano.function([x], gx)
x_random = self.rng.binomial(n=1, p=0.5, size=(5, 7)).astype('int8')
for x_val in (x_random,
numpy.zeros_like(x_random),
numpy.ones_like(x_random)):
gx_val = f(x_val)
assert gx_val.shape == x_val.shape
assert numpy.all(gx_val == 0)
def test_any_grad(self):
x = tensor.bmatrix('x')
x_all = x.any()
gx = theano.grad(x_all, x)
f = theano.function([x], gx)
x_random = self.rng.binomial(n=1, p=0.5, size=(5, 7)).astype('int8')
for x_val in (x_random,
numpy.zeros_like(x_random),
numpy.ones_like(x_random)):
gx_val = f(x_val)
assert gx_val.shape == x_val.shape
assert numpy.all(gx_val == 0)
def __init__(self, K, vocab_size, num_chars, W_init,
nhidden, embed_dim, dropout, train_emb, char_dim, use_feat, gating_fn,
save_attn=False):
self.nhidden = nhidden
self.embed_dim = embed_dim
self.dropout = dropout
self.train_emb = train_emb
self.char_dim = char_dim
self.learning_rate = LEARNING_RATE
self.num_chars = num_chars
self.use_feat = use_feat
self.save_attn = save_attn
self.gating_fn = gating_fn
self.use_chars = self.char_dim!=0
if W_init is None: W_init = lasagne.init.GlorotNormal().sample((vocab_size, self.embed_dim))
doc_var, query_var, cand_var = T.itensor3('doc'), T.itensor3('quer'), \
T.wtensor3('cand')
docmask_var, qmask_var, candmask_var = T.bmatrix('doc_mask'), T.bmatrix('q_mask'), \
T.bmatrix('c_mask')
target_var = T.ivector('ans')
feat_var = T.imatrix('feat')
doc_toks, qry_toks= T.imatrix('dchars'), T.imatrix('qchars')
tok_var, tok_mask = T.imatrix('tok'), T.bmatrix('tok_mask')
cloze_var = T.ivector('cloze')
self.inps = [doc_var, doc_toks, query_var, qry_toks, cand_var, target_var, docmask_var,
qmask_var, tok_var, tok_mask, candmask_var, feat_var, cloze_var]
self.predicted_probs, predicted_probs_val, self.network, W_emb, attentions = (
self.build_network(K, vocab_size, W_init))
self.loss_fn = T.nnet.categorical_crossentropy(self.predicted_probs, target_var).mean()
self.eval_fn = lasagne.objectives.categorical_accuracy(self.predicted_probs,
target_var).mean()
loss_fn_val = T.nnet.categorical_crossentropy(predicted_probs_val, target_var).mean()
eval_fn_val = lasagne.objectives.categorical_accuracy(predicted_probs_val,
target_var).mean()
self.params = L.get_all_params(self.network, trainable=True)
updates = lasagne.updates.adam(self.loss_fn, self.params, learning_rate=self.learning_rate)
self.train_fn = theano.function(self.inps,
[self.loss_fn, self.eval_fn, self.predicted_probs],
updates=updates,
on_unused_input='warn')
self.validate_fn = theano.function(self.inps,
[loss_fn_val, eval_fn_val, predicted_probs_val]+attentions,
on_unused_input='warn')
def __init__(self, K, vocab_size, num_chars, W_init, regularizer, rlambda,
nhidden, embed_dim, dropout, train_emb, subsample, char_dim, use_feat, feat_cnt,
save_attn=False):
self.nhidden = nhidden
self.embed_dim = embed_dim
self.dropout = dropout
self.train_emb = train_emb
self.subsample = subsample
self.char_dim = char_dim
self.learning_rate = LEARNING_RATE
self.num_chars = num_chars
self.use_feat = use_feat
self.feat_cnt = feat_cnt
self.save_attn = save_attn
norm = lasagne.regularization.l2 if regularizer=='l2' else lasagne.regularization.l1
self.use_chars = self.char_dim!=0
if W_init is None: W_init = lasagne.init.GlorotNormal().sample((vocab_size, self.embed_dim))
doc_var, query_var, cand_var = T.itensor3('doc'), T.itensor3('quer'), \
T.wtensor3('cand')
docmask_var, qmask_var, candmask_var = T.bmatrix('doc_mask'), T.bmatrix('q_mask'), \
T.bmatrix('c_mask')
target_var = T.ivector('ans')
feat_var = T.imatrix('feat')
doc_toks, qry_toks= T.imatrix('dchars'), T.imatrix('qchars')
tok_var, tok_mask = T.imatrix('tok'), T.bmatrix('tok_mask')
cloze_var = T.ivector('cloze')
match_feat_var = T.itensor3('match_feat')
use_char_var = T.tensor3('use_char')
use_char_q_var = T.tensor3('use_char_q')
self.inps = [doc_var, doc_toks, query_var, qry_toks, cand_var, target_var, docmask_var,
qmask_var, tok_var, tok_mask, candmask_var, feat_var, cloze_var, match_feat_var, use_char_var, use_char_q_var]
if rlambda> 0.: W_pert = W_init + lasagne.init.GlorotNormal().sample(W_init.shape)
else: W_pert = W_init
self.predicted_probs, predicted_probs_val, self.network, W_emb, attentions = (
self.build_network(K, vocab_size, W_pert))
self.loss_fn = T.nnet.categorical_crossentropy(self.predicted_probs, target_var).mean() + \
rlambda*norm(W_emb-W_init)
self.eval_fn = lasagne.objectives.categorical_accuracy(self.predicted_probs,
target_var).mean()
loss_fn_val = T.nnet.categorical_crossentropy(predicted_probs_val, target_var).mean() + \
rlambda*norm(W_emb-W_init)
eval_fn_val = lasagne.objectives.categorical_accuracy(predicted_probs_val,
target_var).mean()
self.params = L.get_all_params(self.network, trainable=True)
updates = lasagne.updates.adam(self.loss_fn, self.params, learning_rate=self.learning_rate)
self.train_fn = theano.function(self.inps,
[self.loss_fn, self.eval_fn, self.predicted_probs],
updates=updates,
on_unused_input='ignore')
self.validate_fn = theano.function(self.inps,
[loss_fn_val, eval_fn_val, predicted_probs_val]+attentions,
on_unused_input='ignore')