def __init__(self,
data_config,
model_config="emb:32|conv:48|cell:16|bi:1|l1:16|do:0.1"):
super(Model, self).__init__()
no_chars = data_config['num_tokens']
config = utils.parse_model_params(model_config)
emb_dim = config['emb']
conv_filters = config['conv']
dropout_rate = config.get("do", 0)
self.embeddings = nn.Embedding(no_chars, emb_dim, padding_idx=0)
self.dropout = torch.nn.Dropout(p=dropout_rate)
bidirection = bool(config.get("bi", 0))
self.lstm = nn.LSTM(emb_dim,
config['cell'],
batch_first=True,
bidirectional=bidirection)
out_dim = config['cell'] * 2 if bidirection else config['cell']
self.conv1 = ConvolutionLayer(out_dim, conv_filters, 5)
self.linear1 = nn.Linear(conv_filters, config['l1'])
self.linear2 = nn.Linear(config['l1'], 1)
self.model_params = model_config
def __init__(self,
data_config,
model_config="emb:32|conv:48|l1:16|do:0.1"):
super(Model, self).__init__()
no_chars = data_config['num_tokens']
config = utils.parse_model_params(model_config)
emb_dim = config['emb']
conv_filters = config['conv']
dropout_rate = config.get("do", 0)
self.embeddings = nn.Embedding(no_chars, emb_dim, padding_idx=0)
self.dropout = torch.nn.Dropout(p=dropout_rate)
self.conv1 = ConvolutionLayer(emb_dim, conv_filters, 1)
self.conv2 = ConvolutionLayer(emb_dim, conv_filters, 3)
self.conv3 = ConvolutionLayer(emb_dim, conv_filters, 5, dilation=3)
self.conv4 = ConvolutionLayer(emb_dim, conv_filters, 9, dilation=2)
self.linear1 = nn.Linear(conv_filters, config['l1'])
self.linear2 = nn.Linear(config['l1'], 1)
self.model_params = model_config
def __init__(self,
data_config,
model_config="emb_c:8|emb_t:8|conv:8|l1:6|do:0.1"):
super(Model, self).__init__()
no_chars = data_config['num_tokens']
config = utils.parse_model_params(model_config)
emb_c_dim = config['emb_c']
emb_t_dim = config['emb_t']
conv_filters = config['conv']
dropout_rate = config.get("do", 0)
self.ch_embeddings = nn.Embedding(no_chars, emb_c_dim, padding_idx=0)
self.ch_type_embeddings = nn.Embedding(
character_type.TOTAL_CHARACTER_TYPES, emb_t_dim, padding_idx=0)
self.dropout = torch.nn.Dropout(p=dropout_rate)
emb_dim = emb_c_dim + emb_t_dim
self.conv1 = ConvolutionLayer(emb_dim, conv_filters, 3)
self.conv2 = ConvolutionLayer(emb_dim, conv_filters, 5, dilation=3)
self.conv3 = ConvolutionLayer(emb_dim, conv_filters, 9, dilation=2)
self.linear1 = nn.Linear(conv_filters, config['l1'])
self.linear2 = nn.Linear(config['l1'], 1)
self.model_params = model_config
def __init__(self,
data_config,
model_config="emb:32|conv:48|l1:16|do:0.1"):
super(Model, self).__init__()
no_chars = data_config['num_tokens']
config = utils.parse_model_params(model_config)
emb_dim = config['emb']
conv_filters = config['conv']
dropout_rate = config.get("do", 0)
self.embeddings = nn.Embedding(no_chars, emb_dim, padding_idx=0)
self.dropout = torch.nn.Dropout(p=dropout_rate)
kernel_size = 1
self.conv0 = nn.Conv1d(emb_dim,
conv_filters,
kernel_size,
stride=1,
dilation=1,
padding=kernel_size // 2)
kernel_size = 3
self.conv1 = nn.Conv1d(emb_dim,
conv_filters,
kernel_size,
stride=1,
dilation=1,
padding=kernel_size // 2)
kernel_size = 5
dilation = 3
padding = kernel_size // 2
padding += padding * (dilation - 1)
self.conv2 = nn.Conv1d(emb_dim,
conv_filters,
kernel_size,
stride=1,
dilation=dilation,
padding=6)
kernel_size = 9
dilation = 2
padding = kernel_size // 2
padding += padding * (dilation - 1)
self.conv3 = nn.Conv1d(emb_dim,
conv_filters,
kernel_size,
stride=1,
dilation=dilation,
padding=padding)
self.linear1 = nn.Linear(conv_filters, config['l1'])
self.linear2 = nn.Linear(config['l1'], 1)
self.model_params = model_config
def __init__(self, data_config, model_config="emb:32|l1:64"):
super(Model, self).__init__()
window_size = data_config['window_size']
no_vocabs = data_config['num_tokens']
config = utils.parse_model_params(model_config)
emb_dim = config['emb']
l1 = config['l1']
self.embeddings = nn.Embedding(no_vocabs, emb_dim, padding_idx=0)
self.linear1 = nn.Linear((2 * window_size + 1) * emb_dim, l1)
self.linear2 = nn.Linear(l1, 1)
self.model_params = model_config
def __init__(self, data_config, model_config="emb:16|l1:64"):
super(Model, self).__init__()
window_size = data_config['window_size']
no_chars = data_config['num_tokens']
max_length = data_config['max_seq_length']
config = utils.parse_model_params(model_config)
emb_dim = config['emb']
self.embeddings = nn.Embedding(no_chars, emb_dim, padding_idx=0)
self.num_embs = 2 * window_size + 1
self.pooling = nn.MaxPool1d(max_length)
self.linear1 = nn.Linear(self.num_embs * emb_dim, config['l1'])
self.linear2 = nn.Linear(config['l1'], 1)
def __init__(self, data_config, model_config="embc:16|embs:8|embct:8|conv:16|l1:16|do:0.0"):
super(Model, self).__init__()
no_chars = data_config['num_char_tokens']
print("We have %d characters" % no_chars)
no_syllables = data_config['num_tokens']
print("We have %d syllables" % no_syllables)
config = utils.parse_model_params(model_config)
conv_filters = config['conv']
dropout_rate = config.get("do", 0)
self.ch_embeddings = nn.Embedding(
no_chars,
config['embc'],
padding_idx=0
)
self.ch_type_embeddings = nn.Embedding(
character_type.TOTAL_CHARACTER_TYPES,
config['embct'],
padding_idx=0
)
self.sy_embeddings = nn.Embedding(
no_syllables,
config['embs'],
padding_idx=0
)
emb_dim = config['embc'] + config['embs'] + config['embct']
self.dropout= torch.nn.Dropout(p=dropout_rate)
self.conv1 = ConvolutionBatchNorm(emb_dim, conv_filters, 3)
self.conv2 = ConvolutionBatchNorm(emb_dim, conv_filters, 5, dilation=3)
self.conv3 = ConvolutionBatchNorm(emb_dim, conv_filters, 9, dilation=2)
self.linear1 = nn.Linear(conv_filters, config['l1'])
self.linear2 = nn.Linear(config['l1'], 1)
self.model_params = model_config
def __init__(self, data_config, model_config="emb:32|cell:64|l1:64"):
super(Model, self).__init__()
window_size = data_config['window_size']
no_chars = data_config['num_tokens']
self.num_embs = 2 * window_size + 1
config = utils.parse_model_params(model_config)
emb_dim = config['emb']
cell = config['cell']
self.embeddings = nn.Embedding(no_chars, emb_dim, padding_idx=0)
self.lstm = nn.LSTM(emb_dim, cell, batch_first=True)
self.linear1 = nn.Linear(self.num_embs * cell, config['l1'])
self.linear2 = nn.Linear(config['l1'], 1)
self.model_params = model_config
def __init__(self, data_config, model_config="emb:32|c1:16|l1:8|do:0.1"):
super(Model, self).__init__()
no_chars = data_config['num_tokens']
config = utils.parse_model_params(model_config)
emb_dim = config['emb']
dropout_rate = config.get("do", 0)
self.embeddings = nn.Embedding(no_chars, emb_dim, padding_idx=0)
self.dropout = torch.nn.Dropout(p=dropout_rate)
kernel_size = 2
self.conv1 = ConvolutionLayer(emb_dim, config['c1'], kernel_size)
self.conv2 = ConvolutionLayer(config['c1'], config['c1'], kernel_size)
self.conv3 = ConvolutionLayer(config['c1'], config['c1'], kernel_size)
self.linear1 = nn.Linear(config['c1'], config['lu1'])
self.linear2 = nn.Linear(config['l1'], 1)
self.model_params = model_config
