本文整理汇总了Python中dynet.AdamTrainer方法的典型用法代码示例。如果您正苦于以下问题：Python dynet.AdamTrainer方法的具体用法？Python dynet.AdamTrainer怎么用？Python dynet.AdamTrainer使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类dynet的用法示例。
在下文中一共展示了dynet.AdamTrainer方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import AdamTrainer [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 示例2: init_params
# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import AdamTrainer [as 别名]
def init_params(self):
self.trainer = dy.AdamTrainer(self.model.pc) 示例3: __init__
# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import AdamTrainer [as 别名]
def __init__(self, params, model=None):
self.UPSAMPLE_PROJ = 200
self.RNN_SIZE = 100
self.RNN_LAYERS = 1
self.OUTPUT_EMB_SIZE = 200
self.params = params
if model is None:
self.model = dy.Model()
else:
self.model = model
# self.trainer = dy.AdamTrainer(self.model, alpha=2e-3, beta_1=0.9, beta_2=0.9)
self.trainer = dy.AdamTrainer(self.model)
# MGCs are extracted at 12.5 ms
upsample_count = int(12.5 * self.params.target_sample_rate / 1000)
self.upsample_w_s = []
self.upsample_w_t = []
self.upsample_b_s = []
self.upsample_b_t = []
for _ in range(upsample_count):
self.upsample_w_s.append(self.model.add_parameters((self.UPSAMPLE_PROJ, self.params.mgc_order)))
self.upsample_w_t.append(self.model.add_parameters((self.UPSAMPLE_PROJ, self.params.mgc_order)))
self.upsample_b_s.append(self.model.add_parameters((self.UPSAMPLE_PROJ)))
self.upsample_b_t.append(self.model.add_parameters((self.UPSAMPLE_PROJ)))
self.output_lookup = self.model.add_lookup_parameters((256, self.OUTPUT_EMB_SIZE))
from models.utils import orthonormal_VanillaLSTMBuilder
# self.rnn = orthonormal_VanillaLSTMBuilder(self.RNN_LAYERS, self.OUTPUT_EMB_SIZE + self.UPSAMPLE_PROJ, self.RNN_SIZE, self.model)
self.rnn = dy.VanillaLSTMBuilder(self.RNN_LAYERS, self.OUTPUT_EMB_SIZE + self.UPSAMPLE_PROJ,
self.RNN_SIZE, self.model)
self.mlp_w = []
self.mlp_b = []
self.mlp_w.append(self.model.add_parameters((1024, self.RNN_SIZE)))
self.mlp_b.append(self.model.add_parameters((1024)))
self.softmax_w = self.model.add_parameters((256, 1024))
self.softmax_b = self.model.add_parameters((256)) 示例4: __init__
# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import AdamTrainer [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) 示例5: __init__
# 需要导入模块: import dynet [as 别名]
# 或者: from dynet import AdamTrainer [as 别名]
def __init__(self, encodings):
self.losses = []
self.model = dy.Model()
self.trainer = dy.AdamTrainer(self.model, alpha=2e-3, beta_1=0.9, beta_2=0.9)
self.encodings = encodings
self.DECODER_SIZE = 100
self.ENCODER_SIZE = 100
self.CHAR_EMB_SIZE = 100
self.HIDDEN_SIZE = 100
self.lexicon = {}
self.char_lookup = self.model.add_lookup_parameters((len(self.encodings.char2int), self.CHAR_EMB_SIZE))
self.phoneme_lookup = self.model.add_lookup_parameters(
(len(self.encodings.phoneme2int) + 1, self.CHAR_EMB_SIZE)) # +1 is for special START
self.start_lookup = self.model.add_lookup_parameters(
(1, self.CHAR_EMB_SIZE + self.ENCODER_SIZE * 2)) # START SYMBOL
self.encoder_fw = []
self.encoder_bw = []
input_layer_size = self.CHAR_EMB_SIZE
for ii in range(2):
self.encoder_fw.append(dy.VanillaLSTMBuilder(1, input_layer_size, self.ENCODER_SIZE, self.model))
self.encoder_bw.append(dy.VanillaLSTMBuilder(1, input_layer_size, self.ENCODER_SIZE, self.model))
input_layer_size = self.ENCODER_SIZE * 2
self.decoder = dy.VanillaLSTMBuilder(2, self.ENCODER_SIZE * 2 + self.CHAR_EMB_SIZE, self.DECODER_SIZE,
self.model)
self.att_w1 = self.model.add_parameters((100, self.ENCODER_SIZE * 2))
self.att_w2 = self.model.add_parameters((100, self.DECODER_SIZE))
self.att_v = self.model.add_parameters((1, 100))
self.hidden_w = self.model.add_parameters((self.HIDDEN_SIZE, self.DECODER_SIZE))
self.hidden_b = self.model.add_parameters((self.HIDDEN_SIZE))
self.softmax_w = self.model.add_parameters(
(len(self.encodings.phoneme2int) + 1, self.HIDDEN_SIZE)) # +1 is for EOS
self.softmax_b = self.model.add_parameters((len(self.encodings.phoneme2int) + 1))