Python dynet.dropout方法代碼示例

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def source_ranker_cache(self, rel):
        """
        test mode only (no updates, no dropout)
        :param rel: relation to create cache for quick score calculation once source is given
        :return: mode-appropriate pre-computation for association scores
        """
        T = self.embeddings.as_array()
        A = self.word_assoc_weights[rel].as_array()
        if self.mode == BILINEAR_MODE:
            return A.dot(T.transpose())
        elif self.mode == DIAG_RANK1_MODE:
            diag_A = np.diag(A[0])
            rank1_BC = np.outer(A[1],A[2])
            ABC = diag_A + rank1_BC
            return ABC.dot(T.transpose())
        elif self.mode == TRANSLATIONAL_EMBED_MODE:
            return A - T
        elif self.mode == DISTMULT:
            return A * T # elementwise, broadcast 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def target_ranker_cache(self, rel):
        """
        test mode only (no updates, no dropout)
        :param rel: relation to create cache for quick score calculation once target is given
        :returns: mode-appropriate pre-computation for association scores
        """
        S = self.embeddings.as_array()
        A = self.word_assoc_weights[rel].as_array()
        if self.mode == BILINEAR_MODE:
            return S.dot(A)
        elif self.mode == DIAG_RANK1_MODE:
            diag_A = np.diag(A[0])
            rank1_BC = np.outer(A[1],A[2])
            ABC = diag_A + rank1_BC
            return S.dot(ABC)
        elif self.mode == TRANSLATIONAL_EMBED_MODE:
            return S + A
        elif self.mode == DISTMULT:
            return S * A # elementwise, broadcast 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def score_from_source_cache(self, cache, src):
        """
        test mode only (no updates, no dropout)
        :param cache: cache computed earlier using source_ranker_cache
        :param src: index of source node to create ranking of all targets for
        :return: array of scores for all possible targets
        """
        s = self.embeddings[src].npvalue()
        if self.mode == BILINEAR_MODE:
            return (s.dot(cache)).transpose()
        elif self.mode == DIAG_RANK1_MODE:
            return (s.dot(cache)).transpose()
        elif self.mode == TRANSLATIONAL_EMBED_MODE:
            diff_vecs = s + cache
            return -np.sqrt((diff_vecs * diff_vecs).sum(1))
        elif self.mode == DISTMULT:
            return cache.dot(s) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def evaluate_struct(self, fwd_out, back_out, lefts, rights, test=False):

        fwd_span_out = []
        for left_index, right_index in zip(lefts, rights):
            fwd_span_out.append(fwd_out[right_index] - fwd_out[left_index - 1])
        fwd_span_vec = dynet.concatenate(fwd_span_out)

        back_span_out = []
        for left_index, right_index in zip(lefts, rights):
            back_span_out.append(back_out[left_index] - back_out[right_index + 1])
        back_span_vec = dynet.concatenate(back_span_out)

        hidden_input = dynet.concatenate([fwd_span_vec, back_span_vec])

        if self.droprate > 0 and not test:
            hidden_input = dynet.dropout(hidden_input, self.droprate)

        hidden_output = self.activation(self.W1_struct * hidden_input + self.b1_struct)

        scores = (self.W2_struct * hidden_output + self.b2_struct)

        return scores 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def evaluate_label(self, fwd_out, back_out, lefts, rights, test=False):

        fwd_span_out = []
        for left_index, right_index in zip(lefts, rights):
            fwd_span_out.append(fwd_out[right_index] - fwd_out[left_index - 1])
        fwd_span_vec = dynet.concatenate(fwd_span_out)

        back_span_out = []
        for left_index, right_index in zip(lefts, rights):
            back_span_out.append(back_out[left_index] - back_out[right_index + 1])
        back_span_vec = dynet.concatenate(back_span_out)

        hidden_input = dynet.concatenate([fwd_span_vec, back_span_vec])

        if self.droprate > 0 and not test:
            hidden_input = dynet.dropout(hidden_input, self.droprate)

        hidden_output = self.activation(self.W1_label * hidden_input + self.b1_label)

        scores = (self.W2_label * hidden_output + self.b2_label)

        return scores 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def dropout(h_keep_prob):

    def compile_fn(di, dh):
        p = dh['keep_prop']
        Dropout = dy.dropout

        def fn(di):
            return {'out': Dropout(di['in'], p)}

        return fn

    return siso_dynet_module('Dropout', compile_fn, {'keep_prop': h_keep_prob}) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [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 dropout [as 別名]
def dnn_cell(h_num_hidden, h_nonlin_name, h_opt_drop, h_drop_keep_prob):
    return mo.siso_sequential([
        dense(h_num_hidden),
        nonlinearity(h_nonlin_name),
        mo.siso_optional(lambda: dropout(h_drop_keep_prob), h_opt_drop)
    ]) 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def set_dropout(self, p):
        self.bi_lstm.set_dropout(p)
        self.dropout = p 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def disable_dropout(self):
        self.bi_lstm.disable_dropout()
        self.dropout = None 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [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 dropout [as 別名]
def forward_one_multilayer(lstm_input, layer_states, dropout_amount=0.):
    """ Goes forward for one multilayer RNN cell step.

    Inputs:
        lstm_input (dy.Expression): Some input to the step.
        layer_states (list of dy.RNNState): The states of each layer in the cell.
        dropout_amount (float, optional): The amount of dropout to apply, in
            between the layers.

    Returns:
        (list of dy.Expression, list of dy.Expression), dy.Expression, (list of dy.RNNSTate),
        representing (each layer's cell memory, each layer's cell hidden state),
        the final hidden state, and (each layer's updated RNNState).
    """
    num_layers = len(layer_states)
    new_states = []
    cell_states = []
    hidden_states = []
    state = lstm_input
    for i in range(num_layers):
        new_states.append(layer_states[i].add_input(state))

        layer_c, layer_h = new_states[i].s()

        state = layer_h

        if i < num_layers - 1:
            state = dy.dropout(state, dropout_amount)

        cell_states.append(layer_c)
        hidden_states.append(layer_h)

    return (cell_states, hidden_states), state, new_states 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [as 別名]
def encode_sequence(sequence, rnns, embedder, dropout_amount=0.):
    """ Encodes a sequence given RNN cells and an embedding function.

    Inputs:
        seq (list of str): The sequence to encode.
        rnns (list of dy._RNNBuilder): The RNNs to use.
        emb_fn (dict str->dy.Expression): Function that embeds strings to
            word vectors.
        size (int): The size of the RNN.
        dropout_amount (float, optional): The amount of dropout to apply.

    Returns:
        (list of dy.Expression, list of dy.Expression), list of dy.Expression,
        where the first pair is the (final cell memories, final cell states) of
        all layers, and the second list is a list of the final layer's cell
        state for all tokens in the sequence.
    """
    layer_states = []
    for rnn in rnns:
        hidden_size = rnn.spec[2]
        layer_states.append(rnn.initial_state([dy.zeroes((hidden_size, 1)),
                                               dy.zeroes((hidden_size, 1))]))

    outputs = []

    for token in sequence:
        rnn_input = embedder(token)

        (cell_states, hidden_states), output, layer_states = \
            forward_one_multilayer(rnn_input,
                                   layer_states,
                                   dropout_amount)

        outputs.append(output)

    return (cell_states, hidden_states), outputs 

# 需要導入模塊: import dynet [as 別名]
# 或者: from dynet import dropout [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 dropout [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