Python dynet.parameter方法代碼示例

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def test_multilinear_forward():
    model = dy.Model()

    a, b, c = np.random.RandomState(0).randn(3, 100)
    ml = MultilinearFactored(n_features=100, n_inputs=3, n_components=5,
                             model=model)
    dy_fwd = ml(dy.inputVector(a),
                dy.inputVector(b),
                dy.inputVector(c)).value()

    U = [dy.parameter(u).value() for u in ml.get_components()]
    expected = np.dot(U[0], a)
    expected *= np.dot(U[1], b)
    expected *= np.dot(U[2], c)
    expected = np.sum(expected)

    assert (expected - dy_fwd) ** 2 < 1e-4 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def word_repr(self, char_seq, cembs):
        # obtain the word representation when given its character sequence

        wlen = len(char_seq)
        if 'rgW%d'%wlen not in self.param_exprs:
            self.param_exprs['rgW%d'%wlen] = dy.parameter(self.params['reset_gate_W'][wlen-1])
            self.param_exprs['rgb%d'%wlen] = dy.parameter(self.params['reset_gate_b'][wlen-1])
            self.param_exprs['cW%d'%wlen] = dy.parameter(self.params['com_W'][wlen-1])
            self.param_exprs['cb%d'%wlen] = dy.parameter(self.params['com_b'][wlen-1])

        chars = dy.concatenate(cembs)
        reset_gate = dy.logistic(self.param_exprs['rgW%d'%wlen] * chars + self.param_exprs['rgb%d'%wlen])
        word = dy.tanh(self.param_exprs['cW%d'%wlen] * dy.cmult(reset_gate,chars) + self.param_exprs['cb%d'%wlen])
        if self.known_words is not None and tuple(char_seq) in self.known_words:
            return (word + dy.lookup(self.params['word_embed'],self.known_words[tuple(char_seq)]))/2.
        return word 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def __init__(self, lstm):
            self.lstm = lstm

            self.outputs = []

            self.c = dynet.parameter(self.lstm.c0)
            self.h = dynet.tanh(self.c)

            self.W_i = dynet.parameter(self.lstm.W_i)
            self.b_i = dynet.parameter(self.lstm.b_i)

            self.W_f = dynet.parameter(self.lstm.W_f)
            self.b_f = dynet.parameter(self.lstm.b_f)

            self.W_c = dynet.parameter(self.lstm.W_c)
            self.b_c = dynet.parameter(self.lstm.b_c)

            self.W_o = dynet.parameter(self.lstm.W_o)
            self.b_o = dynet.parameter(self.lstm.b_o) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def predict_emb(self, chars):
        dy.renew_cg()

        finit = self.char_fwd_lstm.initial_state()
        binit = self.char_bwd_lstm.initial_state()

        H = dy.parameter(self.lstm_to_rep_params)
        Hb = dy.parameter(self.lstm_to_rep_bias)
        O = dy.parameter(self.mlp_out)
        Ob = dy.parameter(self.mlp_out_bias)

        pad_char = self.c2i[PADDING_CHAR]
        char_ids = [pad_char] + chars + [pad_char]
        embeddings = [self.char_lookup[cid] for cid in char_ids]

        bi_fwd_out = finit.transduce(embeddings)
        bi_bwd_out = binit.transduce(reversed(embeddings))

        rep = dy.concatenate([bi_fwd_out[-1], bi_bwd_out[-1]])

        return O * dy.tanh(H * rep + Hb) + Ob 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def build_tagging_graph(self, sentence):
        dy.renew_cg()

        embeddings = [self.word_rep(w) for w in sentence]

        lstm_out = self.word_bi_lstm.transduce(embeddings)

        H = {}
        Hb = {}
        O = {}
        Ob = {}
        scores = {}
        for att in self.attributes:
            H[att] = dy.parameter(self.lstm_to_tags_params[att])
            Hb[att] = dy.parameter(self.lstm_to_tags_bias[att])
            O[att] = dy.parameter(self.mlp_out[att])
            Ob[att] = dy.parameter(self.mlp_out_bias[att])
            scores[att] = []
            for rep in lstm_out:
                score_t = O[att] * dy.tanh(H[att] * rep + Hb[att]) + Ob[att]
                scores[att].append(score_t)

        return scores 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def build_tagging_graph(self, sentence):
        dy.renew_cg()

        embeddings = [self.word_rep(w) for w in sentence]

        lstm_out = self.bi_lstm.transduce(embeddings)

        H = dy.parameter(self.lstm_to_tags_params)
        Hb = dy.parameter(self.lstm_to_tags_bias)
        O = dy.parameter(self.mlp_out)
        Ob = dy.parameter(self.mlp_out_bias)
        scores = []
        if options.bigram:
            for rep, word in zip(lstm_out, sentence):
                bi1 = dy.lookup(self.bigram_lookup, word[0], update=self.we_update)
                bi2 = dy.lookup(self.bigram_lookup, word[1], update=self.we_update)
                if self.dropout is not None:
                    bi1 = dy.dropout(bi1, self.dropout)
                    bi2 = dy.dropout(bi2, self.dropout)
                score_t = O * dy.tanh(H * dy.concatenate(
                    [bi1,
                     rep,
                     bi2]) + Hb) + Ob
                scores.append(score_t)
        else:
            for rep in lstm_out:
                score_t = O * dy.tanh(H * rep + Hb) + Ob
                scores.append(score_t)

        return scores 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def encode_ws(self, X, train=False):
        dy.renew_cg()

        # Remove dy.parameters(...) for DyNet v.2.1
        #w_ws = dy.parameter(self.w_ws)
        #b_ws = dy.parameter(self.b_ws)
        w_ws = self.w_ws
        b_ws = self.b_ws

        ipts = []
        length = len(X[0])
        for i in range(length):
            uni   = X[0][i]
            bi    = X[1][i]
            ctype = X[2][i]
            start = X[3][i]
            end   = X[4][i]

            vec_uni   = dy.concatenate([self.UNI[uid] for uid in uni])
            vec_bi    = dy.concatenate([self.BI[bid] for bid in bi])
            vec_start = dy.esum([self.WORD[sid] for sid in start])
            vec_end   = dy.esum([self.WORD[eid] for eid in end])
            vec_ctype = dy.concatenate([self.CTYPE[cid] for cid in ctype])
            vec_at_i  = dy.concatenate([vec_uni, vec_bi, vec_ctype, vec_start, vec_end])

            if train is True:
                vec_at_i = dy.dropout(vec_at_i, self.dropout_rate)
            ipts.append(vec_at_i)

        bilstm_outputs = self.ws_model.transduce(ipts)
        observations   = [w_ws*h+b_ws for h in bilstm_outputs]
        return observations 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def encode_pt(self, X, train=False):
        dy.renew_cg()

        # Remove dy.parameters(...) for DyNet v.2.1
        #w_pos = dy.parameter(self.w_pos)
        #b_pos = dy.parameter(self.b_pos)
        w_pos = self.w_pos
        b_pos = self.b_pos

        ipts  = []
        length = len(X[0])
        for i in range(length):
            cids = X[0][i]
            wid  = X[1][i]
            tids = X[2][i]
            vec_char = self.char_seq_model.transduce([self.UNI[cid] for cid in cids])[-1]

            vec_tags = []
            for tid in tids:
                if tid == 0:
                    zero = dy.inputVector(np.zeros(self.dim_tag_emb))
                    vec_tags.append(zero)
                else:
                    vec_tags.append(self.POS[tid])
            vec_tag = dy.esum(vec_tags)

            if wid == 0:
                vec_word = dy.inputVector(np.zeros(self.dim_word))
            else:
                vec_word = self.WORD[wid]

            vec_at_i = dy.concatenate([vec_word, vec_char, vec_tag])
            if train is True:
                vec_at_i = dy.dropout(vec_at_i, self.dropout_rate)
            ipts.append(vec_at_i)
        hiddens = self.pos_model.transduce(ipts)
        probs = [dy.softmax(w_pos*h+b_pos) for h in hiddens]
        return probs 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def ergm_score(self):
        """
        :return: ERGM score (dynet Expression) computed based on ERGM weights and features only
        Does not populate any field
        """
        W = dy.parameter(self.ergm_weights)
        f = dy.transpose(dy.inputVector([self.feature_vals[k] for k in self.feature_set]))
        return f * W 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def rescore(self):
        """
        Computes score based on current parameter and feature values, populates field
        """
        self.dy_score = self.ergm_score()
        self.score = self.dy_score.scalar_value()
        self.score_is_stale = False

    ### EDGE ABLATION MODE ### 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def word_assoc_score(self, source_idx, target_idx, relation):
        """
        NOTE THAT DROPOUT IS BEING APPLIED HERE
        :param source_idx: embedding index of source atom
        :param target_idx: embedding index of target atom
        :param relation: relation type
        :return: score
        """
        # prepare
        s = self.embeddings[source_idx]
        if self.no_assoc:
            A = dy.const_parameter(self.word_assoc_weights[relation])
        else:
            A = dy.parameter(self.word_assoc_weights[relation])
        dy.dropout(A, self.dropout)
        t = self.embeddings[target_idx]
        
        # compute
        if self.mode == BILINEAR_MODE:
            return dy.transpose(s) * A * t
        elif self.mode == DIAG_RANK1_MODE:
            diag_A = dyagonalize(A[0])
            rank1_BC = A[1] * dy.transpose(A[2])
            ABC = diag_A + rank1_BC
            return dy.transpose(s) * ABC * t
        elif self.mode == TRANSLATIONAL_EMBED_MODE:
            return -dy.l2_norm(s - t + A)
        elif self.mode == DISTMULT:
            return dy.sum_elems(dy.cmult(dy.cmult(s, A), t)) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def hid_layer(self,x,dropout):
        if dropout:
            W = dy.dropout(dy.parameter(self._W1),0.3)
            b = dy.dropout(dy.parameter(self._b1),0.3)
        else:
            W = dy.parameter(self._W1)
            b = dy.parameter(self._b1)
        return self.activation(W*x+b) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def hid_2_layer(self,x,dropout):
        if dropout:
            W = dy.dropout(dy.parameter(self._W12),0.3)
            b = dy.dropout(dy.parameter(self._b12),0.3)
        else:
            W = dy.parameter(self._W12)
            b = dy.parameter(self._b12)
        return self.activation(W*x+b) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def out_layer(self,x,dropout):
        if dropout:
            W = dy.dropout(dy.parameter(self._W2),0.3)
            b = dy.dropout(dy.parameter(self._b2),0.3)
        else:
            W = dy.parameter(self._W2)
            b = dy.parameter(self._b2)
        return (W*x+b) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import parameter [as 別名]
def __call__(self, x):
        b = dy.parameter(self.b)
        W = dy.parameter(self.W)
        out = b + W * x
        if self.activation:
            out = self.activation(out)
        return out