Python dynet.rectify方法代码示例

# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import rectify [as 别名]
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}) 

# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import rectify [as 别名]
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 

# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import rectify [as 别名]
def __init__(self, vocab, options):

        # import here so we don't load Dynet if just running parser.py --help for example
        from uuparser.multilayer_perceptron import MLP
        from uuparser.feature_extractor import FeatureExtractor
        import dynet as dy
        global dy

        global LEFT_ARC, RIGHT_ARC, SHIFT, SWAP
        LEFT_ARC, RIGHT_ARC, SHIFT, SWAP = 0,1,2,3

        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.oracle = options.oracle

        self.headFlag = options.headFlag
        self.rlMostFlag = options.rlMostFlag
        self.rlFlag = options.rlFlag
        self.k = options.k

        #dimensions depending on extended features
        self.nnvecs = (1 if self.headFlag else 0) + (2 if self.rlFlag or self.rlMostFlag else 0)
        self.feature_extractor = FeatureExtractor(self.model, options, vocab, self.nnvecs)
        self.irels = self.feature_extractor.irels

        if options.no_bilstms > 0:
            mlp_in_dims = options.lstm_output_size*2*self.nnvecs*(self.k+1)
        else:
            mlp_in_dims = self.feature_extractor.lstm_input_size*self.nnvecs*(self.k+1)

        self.unlabeled_MLP = MLP(self.model, 'unlabeled', mlp_in_dims, options.mlp_hidden_dims,
                                 options.mlp_hidden2_dims, 4, self.activation)
        self.labeled_MLP = MLP(self.model, 'labeled' ,mlp_in_dims, options.mlp_hidden_dims,
                               options.mlp_hidden2_dims,2*len(self.irels)+2,self.activation) 

# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import rectify [as 别名]
def __init__(self, n_in, n_hid, n_out, n_layers, model):
        self.mlp = MultiLayerPerceptron(
            [2 * n_in] + [n_hid] * n_layers + [n_out], activation=dy.rectify,
            model=model) 

# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import rectify [as 别名]
def __call__(self, src, trg):
        return self.bilinear(
            dy.rectify(self.src_mlp(src)),  # HOTFIX rectify here?
            dy.rectify(self.trg_mlp(trg))) 

# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import rectify [as 别名]
def predict_emb(self, chars):
        dy.renew_cg()

        conv_param = dy.parameter(self.conv)
        conv_param_bias = dy.parameter(self.conv_bias)

        H = dy.parameter(self.cnn_to_rep_params)
        Hb = dy.parameter(self.cnn_to_rep_bias)
        O = dy.parameter(self.mlp_out)
        Ob = dy.parameter(self.mlp_out_bias)

        # padding
        pad_char = self.c2i[PADDING_CHAR]
        padding_size = self.window_width // 2 # TODO also consider w_stride?
        char_ids = ([pad_char] * padding_size) + chars + ([pad_char] * padding_size)
        if len(chars) < self.pooling_maxk:
            # allow k-max pooling layer output to transform to affine
            char_ids.extend([pad_char] * (self.pooling_maxk - len(chars)))

        embeddings = dy.concatenate_cols([self.char_lookup[cid] for cid in char_ids])
        reshaped_embeddings = dy.reshape(dy.transpose(embeddings), (1, len(char_ids), self.char_dim))

        # not using is_valid=False due to maxk-pooling-induced extra padding
        conv_out = dy.conv2d_bias(reshaped_embeddings, conv_param, conv_param_bias, self.stride, is_valid=True)

        relu_out = dy.rectify(conv_out)

        ### pooling when max_k can only be 1, not sure what other differences may be
        #poolingk = [1, len(chars)]
        #pooling_out = dy.maxpooling2d(relu_out, poolingk, self.stride, is_valid=True)
        #pooling_out_flat = dy.reshape(pooling_out, (self.hidden_dim,))

        ### another possible way for pooling is just max_dim(relu_out, d=1)

        pooling_out = dy.kmax_pooling(relu_out, self.pooling_maxk, d=1) # d = what dimension to max over
        pooling_out_flat = dy.reshape(pooling_out, (self.hidden_dim * self.pooling_maxk,))

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

# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import rectify [as 别名]
def __call__(self, query, options, gold, lengths, query_no):
        if len(options) == 1:
            return None, 0

        final = []
        if args.word_vectors:
            qvecs = [dy.lookup(self.pEmbedding, w) for w in query]
            qvec_max = dy.emax(qvecs)
            qvec_mean = dy.average(qvecs)
        for otext, features in options:
            inputs = dy.inputTensor(features)
            if args.word_vectors:
                ovecs = [dy.lookup(self.pEmbedding, w) for w in otext]
                ovec_max = dy.emax(ovecs)
                ovec_mean = dy.average(ovecs)
                inputs = dy.concatenate([inputs, qvec_max, qvec_mean, ovec_max, ovec_mean])
            if args.drop > 0:
                inputs = dy.dropout(inputs, args.drop)
            h = inputs
            for pH, pB in zip(self.hidden, self.bias):
                h = dy.affine_transform([pB, pH, h])
                if args.nonlin == "linear":
                    pass
                elif args.nonlin == "tanh":
                    h = dy.tanh(h)
                elif args.nonlin == "cube":
                    h = dy.cube(h)
                elif args.nonlin == "logistic":
                    h = dy.logistic(h)
                elif args.nonlin == "relu":
                    h = dy.rectify(h)
                elif args.nonlin == "elu":
                    h = dy.elu(h)
                elif args.nonlin == "selu":
                    h = dy.selu(h)
                elif args.nonlin == "softsign":
                    h = dy.softsign(h)
                elif args.nonlin == "swish":
                    h = dy.cmult(h, dy.logistic(h))
            final.append(dy.sum_dim(h, [0]))

        final = dy.concatenate(final)
        nll = -dy.log_softmax(final)
        dense_gold = []
        for i in range(len(options)):
            dense_gold.append(1.0 / len(gold) if i in gold else 0.0)
        answer = dy.inputTensor(dense_gold)
        loss = dy.transpose(answer) * nll
        predicted_link = np.argmax(final.npvalue())

        return loss, predicted_link