Python dynet.tanh方法代碼示例

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [as 別名]
def nonlinearity(h_nonlin_name):

    def compile_fn(di, dh):

        def fn(di):
            nonlin_name = dh['nonlin_name']
            if nonlin_name == 'relu':
                Out = dy.rectify(di['in'])
            elif nonlin_name == 'elu':
                Out = dy.elu(di['in'])
            elif nonlin_name == 'tanh':
                Out = dy.tanh(di['in'])
            else:
                raise ValueError
            return {'out': Out}

        return fn

    return siso_dynet_module('Nonlinearity', compile_fn,
                             {'nonlin_name': h_nonlin_name}) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [as 別名]
def _upsample_old(self, mgc, start, stop):
        mgc_index = start / len(self.upsample_w_t)
        ups_index = start % len(self.upsample_w_t)
        upsampled = []
        mgc_vect = dy.inputVector(mgc[mgc_index])
        for x in range(stop - start):
            # sigm = dy.logistic(self.upsample_w_s[ups_index].expr(update=True) * mgc_vect + self.upsample_b_s[ups_index].expr(update=True))
            tnh = dy.tanh(self.upsample_w_t[ups_index].expr(update=True) * mgc_vect + self.upsample_b_t[ups_index].expr(
                update=True))
            # r = dy.cmult(sigm, tnh)
            upsampled.append(tnh)
            ups_index += 1
            if ups_index == len(self.upsample_w_t):
                ups_index = 0
                mgc_index += 1
                if mgc_index == len(
                        mgc):  # last frame is sometimes not processed, but it should have similar parameters
                    mgc_index -= 1
                else:
                    mgc_vect = dy.inputVector(mgc[mgc_index])
        return upsampled 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [as 別名]
def _upsample(self, mgc, start, stop):
        mgc_index = start / len(self.upsample_w_s)
        ups_index = start % len(self.upsample_w_s)
        upsampled = []
        mgc_vect = dy.inputVector(mgc[mgc_index])
        for x in range(stop - start):
            sigm = dy.logistic(
                self.upsample_w_s[ups_index].expr(update=True) * mgc_vect + self.upsample_b_s[ups_index].expr(
                    update=True))
            tnh = dy.tanh(self.upsample_w_t[ups_index].expr(update=True) * mgc_vect + self.upsample_b_t[ups_index].expr(
                update=True))
            r = dy.cmult(sigm, tnh)
            upsampled.append(r)
            ups_index += 1
            if ups_index == len(self.upsample_w_s):
                ups_index = 0
                mgc_index += 1
                if mgc_index == len(
                        mgc):  # last frame is sometimes not processed, but it should have similar parameters
                    mgc_index -= 1
                else:
                    mgc_vect = dy.inputVector(mgc[mgc_index])
        return upsampled 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [as 別名]
def __init__(self, vocab, options):
        import dynet as dy
        from uuparser.feature_extractor import FeatureExtractor
        global dy
        self.model = dy.ParameterCollection()
        self.trainer = dy.AdamTrainer(self.model, alpha=options.learning_rate)
        self.activations = {'tanh': dy.tanh, 'sigmoid': dy.logistic, 'relu':
                            dy.rectify, 'tanh3': (lambda x:
                                                  dy.tanh(dy.cwise_multiply(dy.cwise_multiply(x, x), x)))}
        self.activation = self.activations[options.activation]
        self.costaugFlag = options.costaugFlag
        self.feature_extractor = FeatureExtractor(self.model, options, vocab)
        self.labelsFlag=options.labelsFlag
        mlp_in_dims = options.lstm_output_size*2

        self.unlabeled_MLP = biMLP(self.model, mlp_in_dims, options.mlp_hidden_dims,
                                 options.mlp_hidden2_dims, 1, self.activation)
        if self.labelsFlag:
            self.labeled_MLP = biMLP(self.model, mlp_in_dims, options.mlp_hidden_dims,
                               options.mlp_hidden2_dims,len(self.feature_extractor.irels),self.activation)

        self.proj = options.proj 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [as 別名]
def Init(self,options):
        paddingWordVec = self.word_lookup[1] if options.word_emb_size > 0 else None
        paddingElmoVec = dy.zeros(self.elmo.emb_dim) if self.elmo else None
        paddingPosVec = self.pos_lookup[1] if options.pos_emb_size > 0 else None
        paddingCharVec = self.charPadding.expr() if options.char_emb_size > 0 else None
        paddingTbankVec = self.treebank_lookup[0] if options.tbank_emb_size > 0 else None

        self.paddingVec = dy.tanh(self.word2lstm.expr() *\
            dy.concatenate(list(filter(None,[paddingWordVec,
                                        paddingElmoVec,
                                        paddingPosVec,
                                        paddingCharVec,
                                        paddingTbankVec]))) + self.word2lstmbias.expr())

        self.empty = self.paddingVec if self.nnvecs == 1 else\
            dy.concatenate([self.paddingVec for _ in range(self.nnvecs)]) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [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 tanh [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 tanh [as 別名]
def add_input(self, input_vec):
            """
            Note that this function updates the existing State object!
            """
            x = dynet.concatenate([input_vec, self.h])

            i = dynet.logistic(self.W_i * x + self.b_i)
            f = dynet.logistic(self.W_f * x + self.b_f)
            g = dynet.tanh(self.W_c * x + self.b_c)
            o = dynet.logistic(self.W_o * x + self.b_o)

            c = dynet.cmult(f, self.c) + dynet.cmult(i, g)
            h = dynet.cmult(o, dynet.tanh(c))

            self.c = c
            self.h = h
            self.outputs.append(h)

            return self 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [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 tanh [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 tanh [as 別名]
def dnn_net_simple(num_classes):

    # declaring hyperparameter
    h_nonlin_name = D(['relu', 'tanh',
                       'elu'])  # nonlinearity function names to choose from
    h_opt_drop = D(
        [0, 1])  # dropout optional hyperparameter; 0 is exclude, 1 is include
    h_drop_keep_prob = D([0.25, 0.5,
                          0.75])  # dropout probability to choose from
    h_num_hidden = D([64, 128, 256, 512, 1024
                     ])  # number of hidden units for affine transform module
    h_num_repeats = D([1, 2])  # 1 is appearing once, 2 is appearing twice

    # defining search space topology
    model = mo.siso_sequential([
        flatten(),
        mo.siso_repeat(
            lambda: mo.siso_sequential([
                dense(h_num_hidden),
                nonlinearity(h_nonlin_name),
                mo.siso_optional(lambda: dropout(h_drop_keep_prob), h_opt_drop),
            ]), h_num_repeats),
        dense(D([num_classes]))
    ])

    return model 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [as 別名]
def dnn_net(num_classes):
    h_nonlin_name = D(['relu', 'tanh', 'elu'])
    h_opt_drop = D([0, 1])
    return mo.siso_sequential([
        flatten(),
        mo.siso_repeat(
            lambda: dnn_cell(D([64, 128, 256, 512, 1024]), h_nonlin_name,
                             h_opt_drop, D([0.25, 0.5, 0.75])), D([1, 2])),
        dense(D([num_classes]))
    ])


# Main/Searcher
# Getting and reading mnist data adapted from here:
# https://github.com/clab/dynet/blob/master/examples/mnist/mnist-autobatch.py 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [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 tanh [as 別名]
def _get_intermediate_state(self, state, dropout_amount=0.):
        intermediate_state = dy.tanh(
            du.linear_layer(
                state, self.state_transform_weights))
        return dy.dropout(intermediate_state, dropout_amount) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import tanh [as 別名]
def _predict_one(self, mgc, noise):
        mgc = dy.inputVector(mgc)
        outputs = []

        noise_vec = dy.inputVector(noise[0:self.UPSAMPLE_COUNT])
        [hidden_w, hidden_b] = self.mlp_excitation
        hidden_input = mgc  # dy.concatenate([mgc, noise_vec])
        for w, b in zip(hidden_w, hidden_b):
            hidden_input = dy.tanh(w.expr(update=True) * hidden_input + b.expr(update=True))
        excitation = dy.logistic(
            self.excitation_w.expr(update=True) * hidden_input + self.excitation_b.expr(update=True))

        [hidden_w, hidden_b] = self.mlp_filter
        hidden_input = mgc  # dy.concatenate([mgc, noise_vec])
        for w, b in zip(hidden_w, hidden_b):
            hidden_input = dy.tanh(w.expr(update=True) * hidden_input + b.expr(update=True))
        filter = dy.tanh(self.filter_w.expr(update=True) * hidden_input + self.filter_b.expr(update=True))

        [hidden_w, hidden_b] = self.mlp_vuv
        hidden_input = mgc  # dy.concatenate([mgc, noise_vec])
        for w, b in zip(hidden_w, hidden_b):
            hidden_input = dy.tanh(w.expr(update=True) * hidden_input + b.expr(update=True))
        vuv = dy.logistic(self.vuv_w.expr(update=True) * hidden_input + self.vuv_b.expr(update=True))

        # sample_vec = dy.inputVector(noise[self.UPSAMPLE_COUNT:self.UPSAMPLE_COUNT * 2])

        # noise_vec = dy.inputVector(noise[0:self.UPSAMPLE_COUNT + self.FILTER_SIZE - 1])
        mixed = excitation  # * vuv + noise_vec * (1.0 - vuv)
        for ii in range(self.UPSAMPLE_COUNT):
            tmp = dy.cmult(filter, dy.pickrange(mixed, ii, ii + self.FILTER_SIZE))
            outputs.append(dy.sum_elems(tmp))
        outputs = dy.concatenate(outputs)
        # from ipdb import set_trace
        # set_trace()
        # mixed = dy.reshape(mixed, (self.UPSAMPLE_COUNT + self.FILTER_SIZE - 1, 1, 1))
        # filter = dy.reshape(filter, (self.FILTER_SIZE, 1, 1, 1))
        # outputs = dy.conv2d(mixed, filter, stride=(1, 1), is_valid=True)
        # outputs = dy.reshape(outputs, (self.UPSAMPLE_COUNT,))
        # outputs = outputs + noise_vec * vuv

        return outputs, excitation, filter, vuv