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')
