def __init__(self,
bert_model_path,
hidden_dim=768,
dropout=0.1,
mlp_dim=512,
mlp_layers_count=1,
labels_count=1,
bert_cls=None,
prob='auto'):
super().__init__()
self.labels_count = labels_count
self.embedding_dim = hidden_dim # size of bert representations
self.bert_cls = bert_cls if bert_cls else BertModel
self.bert = self.bert_cls.from_pretrained(bert_model_path,
output_hidden_states=False,
output_attentions=False)
# distilbert
self.distil_pooler = nn.Linear(self.embedding_dim, self.embedding_dim)
self.dropout = nn.Dropout(dropout)
self.mlp = get_mlp(input_dim=self.embedding_dim,
output_dim=self.labels_count,
hidden_dim=mlp_dim,
hidden_layers_count=mlp_layers_count,
activation_cls=nn.ReLU)
self.prob = self.get_classification_probability_layer(prob)
def get_policy(args, env):
N = env.observation_space.shape[0]
M = env.action_space.shape[0]
if args.init_policy == 'optimal':
K = env.optimal_controller()
mean_network = nn.Linear(*K.shape[::-1], bias=False)
mean_network.weight.data = tensor(K)
elif args.init_policy == 'linear':
K = np.random.randn(M, N)
mean_network = nn.Linear(*K.shape[::-1], bias=False)
mean_network.weight.data = tensor(K)
elif args.init_policy == 'linear_bias':
K = np.random.randn(M, N)
mean_network = nn.Linear(*K.shape[::-1], bias=True)
mean_network.weight.data = tensor(K)
elif args.init_policy == 'mlp':
mean_network = get_mlp((N, ) + tuple(args.hidden_sizes) + (M, ),
gate=nn.Tanh)
else:
raise Exception('unsupported policy type')
return GaussianPolicy(N,
M,
mean_network,
learn_std=not args.fix_std,
gate_output=args.gate_output)
def __init__(self, bert_model_path, hidden_dim=768, dropout=0.1, mlp_dim=512, mlp_layers_count=1, labels_count=1, bert_cls=None, concat='simple', prob='auto'):
super().__init__()
self.labels_count = labels_count
self.embedding_dim = hidden_dim # size of bert representations
self.bert_cls = bert_cls if bert_cls else BertModel
self.bert = self.bert_cls.from_pretrained(bert_model_path, output_hidden_states=False, output_attentions=False)
self.dropout = nn.Dropout(dropout)
# distilbert
self.distil_pooler = nn.Linear(self.embedding_dim, self.embedding_dim)
# Concat mode
self.concat = concat
self.concat_func, self.concat_dim = get_concat(concat, self.embedding_dim)
self.mlp = get_mlp(input_dim=self.concat_dim,
output_dim=self.labels_count,
hidden_dim=mlp_dim,
hidden_layers_count=mlp_layers_count,
activation_cls=nn.ReLU)
# TODO fill linear layers
# nn.init.xavier_normal_(self.classifier.weight)
# Fills the input Tensor with values according to the method described in “Understanding the difficulty of training deep feedforward neural networks” - Glorot, X. & Bengio, Y. (2010), using a normal distribution.
# kaiming_normal_
# Fills the input Tensor with values according to the method described in “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification” - He, K. et al. (2015), using a normal distribution.
self.prob = self.get_classification_probability_layer(prob)
def __init__(self, embeddings=None, embeddings_path=None, labels_count=1, mlp_dim=512, mlp_layers_count=1, concat='simple', prob='auto'):
super().__init__()
self.labels_count = labels_count
if embeddings:
# id to vector mapping is provided as matrix
self.embed = nn.Embedding.from_pretrained(embeddings)
elif embeddings_path:
# load id to vector mapping from file
# Load from txt file (in word2vec format)
w2v_model = gensim.models.KeyedVectors.load_word2vec_format(embeddings_path)
# Convert to PyTorch tensor
weights = torch.FloatTensor(w2v_model.vectors)
self.embed = nn.Embedding.from_pretrained(weights)
else:
raise ValueError('Either `embeddings` or `embeddings_path` must be set!')
self.embedding_dim = self.embed.embedding_dim
# Concat mode
self.concat = concat
self.concat_func, self.concat_dim = get_concat(concat, self.embedding_dim)
self.mlp = get_mlp(input_dim=self.concat_dim,
output_dim=self.labels_count,
hidden_dim=mlp_dim,
hidden_layers_count=mlp_layers_count,
activation_cls=nn.ReLU)
self.prob = self.get_classification_probability_layer(prob)
def mlp_time_compare(data, label):
from models import get_mlp, get_mymlp
import time
start = time.clock()
mymlp = get_mymlp(data.shape[1])
mymlp.fit(data, label)
elapsed = (time.clock() - start)
print("MyMLP Time used:", elapsed)
start = time.clock()
mlp = get_mlp()
mlp.fit(data, label)
elapsed = (time.clock() - start)
print("MLP Time used:", elapsed)
def __init__(self, bert_model_path, hidden_dim=768, dropout=0.1, mlp_dim=512, mlp_layers_count=1, labels_count=1, bert_cls=None, concat='simple', prob='auto'):
super().__init__()
self.labels_count = labels_count
self.embedding_dim = hidden_dim # size of bert representations
self.bert_cls = bert_cls if bert_cls else BertModel
self.bert = self.bert_cls.from_pretrained(bert_model_path, output_hidden_states=False)
self.dropout = nn.Dropout(dropout)
self.lstm = LSTM(input_size=hidden_dim, hidden_size=hidden_dim, batch_first=True)
# Concat mode
self.concat = concat
self.concat_func, self.concat_dim = get_concat(concat, self.embedding_dim)
self.mlp = get_mlp(input_dim=self.concat_dim,
output_dim=self.labels_count,
hidden_dim=mlp_dim,
hidden_layers_count=mlp_layers_count,
activation_cls=nn.ReLU)
self.prob = self.get_classification_probability_layer(prob)
