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Char-aware_NLM/LICENSE

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+MIT License
+
+Copyright (c) 2017 
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Char-aware_NLM/README.md

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+
+# PyTorch-Character-Aware-Neural-Language-Model
+
+This is the PyTorch implementation of character-aware neural language model proposed in this [paper](https://arxiv.org/abs/1508.06615) by Yoon Kim. 
+
+## Requiredments
+The code is run and tested with **Python 3.5.2** and **PyTorch 0.3.1**.
+
+## HyperParameters
+| HyperParam | value |
+| ------ | :-------|
+| LSTM batch size | 20 |
+| LSTM sequence length | 35 |
+| LSTM hidden units | 300 |
+| epochs | 35 |
+| initial learning rate | 1.0 |
+| character embedding dimension | 15 |
+
+## Demo
+Train the model with split train/valid/test data.
+
+`python train.py`
+
+The trained model will saved in `cache/net.pkl`.
+Test the model.
+
+`python test.py`
+
+Best result on test set: 
+PPl=127.2163
+cross entropy loss=4.8459
+
+## Acknowledgement 
+This implementation borrowed ideas from
+
+https://github.com/jarfo/kchar
+
+https://github.com/cronos123/Character-Aware-Neural-Language-Models
+
+

Char-aware_NLM/model.py

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+
+import torch
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Highway(nn.Module):
+    """Highway network"""
+    def __init__(self, input_size):
+        super(Highway, self).__init__()
+        self.fc1 = nn.Linear(input_size, input_size, bias=True)
+        self.fc2 = nn.Linear(input_size, input_size, bias=True)
+
+    def forward(self, x):
+        t = F.sigmoid(self.fc1(x))
+        return torch.mul(t, F.relu(self.fc2(x))) + torch.mul(1-t, x)
+
+
+class charLM(nn.Module):
+    """CNN + highway network + LSTM
+    # Input: 
+        4D tensor with shape [batch_size, in_channel, height, width]
+    # Output:
+        2D Tensor with shape [batch_size, vocab_size]
+    # Arguments:
+        char_emb_dim: the size of each character's embedding
+        word_emb_dim: the size of each word's embedding
+        vocab_size: num of unique words
+        num_char: num of characters
+        use_gpu: True or False
+    """
+    def __init__(self, char_emb_dim, word_emb_dim,  
+                vocab_size, num_char, use_gpu):
+        super(charLM, self).__init__()
+        self.char_emb_dim = char_emb_dim
+        self.word_emb_dim = word_emb_dim
+        self.vocab_size = vocab_size
+
+        # char embedding layer
+        self.char_embed = nn.Embedding(num_char, char_emb_dim)
+
+        # convolutions of filters with different sizes
+        self.convolutions = []
+
+        # list of tuples: (the number of filter, width)
+        self.filter_num_width = [(25, 1), (50, 2), (75, 3), (100, 4), (125, 5), (150, 6)]
+        
+        for out_channel, filter_width in self.filter_num_width:
+            self.convolutions.append(
+                nn.Conv2d(
+                    1,           # in_channel
+                    out_channel, # out_channel
+                    kernel_size=(char_emb_dim, filter_width), # (height, width)
+                    bias=True
+                    )
+            )
+
+        self.highway_input_dim = sum([x for x, y in self.filter_num_width])
+
+        self.batch_norm = nn.BatchNorm1d(self.highway_input_dim, affine=False)
+
+        # highway net
+        self.highway1 = Highway(self.highway_input_dim)
+        self.highway2 = Highway(self.highway_input_dim)
+
+        # LSTM
+        self.lstm_num_layers = 2
+
+        self.lstm = nn.LSTM(input_size=self.highway_input_dim, 
+                            hidden_size=self.word_emb_dim, 
+                            num_layers=self.lstm_num_layers,
+                            bias=True,
+                            dropout=0.5,
+                            batch_first=True)
+
+        # output layer
+        self.dropout = nn.Dropout(p=0.5)
+        self.linear = nn.Linear(self.word_emb_dim, self.vocab_size)
+
+        
+        if use_gpu is True:
+            for x in range(len(self.convolutions)):
+                self.convolutions[x] = self.convolutions[x].cuda()
+            self.highway1 = self.highway1.cuda()
+            self.highway2 = self.highway2.cuda()
+            self.lstm = self.lstm.cuda()
+            self.dropout = self.dropout.cuda()
+            self.char_embed = self.char_embed.cuda()
+            self.linear = self.linear.cuda()
+            self.batch_norm = self.batch_norm.cuda()
+
+
+    def forward(self, x, hidden):
+        # Input: Variable of Tensor with shape [num_seq, seq_len, max_word_len+2]
+        # Return: Variable of Tensor with shape [num_words, len(word_dict)]
+        lstm_batch_size = x.size()[0]
+        lstm_seq_len = x.size()[1]
+
+        x = x.contiguous().view(-1, x.size()[2])
+        # [num_seq*seq_len, max_word_len+2]
+        
+        x = self.char_embed(x)
+        # [num_seq*seq_len, max_word_len+2, char_emb_dim]
+        
+        x = torch.transpose(x.view(x.size()[0], 1, x.size()[1], -1), 2, 3)
+        # [num_seq*seq_len, 1, max_word_len+2, char_emb_dim]
+        
+        x = self.conv_layers(x)
+        # [num_seq*seq_len, total_num_filters]
+
+        x = self.batch_norm(x)
+        # [num_seq*seq_len, total_num_filters]
+
+        x = self.highway1(x)
+        x = self.highway2(x)
+        # [num_seq*seq_len, total_num_filters]
+
+        x = x.contiguous().view(lstm_batch_size,lstm_seq_len, -1)
+        # [num_seq, seq_len, total_num_filters]
+        
+        x, hidden = self.lstm(x, hidden)
+        # [seq_len, num_seq, hidden_size]
+        
+        x = self.dropout(x)
+        # [seq_len, num_seq, hidden_size]
+        
+        x = x.contiguous().view(lstm_batch_size*lstm_seq_len, -1)
+        # [num_seq*seq_len, hidden_size]
+
+        x = self.linear(x)
+        # [num_seq*seq_len, vocab_size]
+        return x, hidden
+
+
+    def conv_layers(self, x):
+        chosen_list = list()
+        for conv in self.convolutions:
+            feature_map = F.tanh(conv(x))
+            # (batch_size, out_channel, 1, max_word_len-width+1)
+            chosen = torch.max(feature_map, 3)[0]
+            # (batch_size, out_channel, 1)            
+            chosen = chosen.squeeze()
+            # (batch_size, out_channel)
+            chosen_list.append(chosen)
+        
+        # (batch_size, total_num_filers)
+        return torch.cat(chosen_list, 1)

Char-aware_NLM/test.py

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+import os
+import torch
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from model import charLM
+from utilities import *
+from collections import namedtuple
+
+def to_var(x):
+    if torch.cuda.is_available():
+        x = x.cuda()
+    return Variable(x)
+
+
+def test(net, data, opt):
+    net.eval()
+ 
+    test_input = torch.from_numpy(data.test_input)
+    test_label = torch.from_numpy(data.test_label)
+
+    num_seq = test_input.size()[0] // opt.lstm_seq_len
+    test_input = test_input[:num_seq*opt.lstm_seq_len, :]
+    # [num_seq, seq_len, max_word_len+2]
+    test_input = test_input.view(-1, opt.lstm_seq_len, opt.max_word_len+2)
+
+    criterion = nn.CrossEntropyLoss()
+
+    loss_list = []
+    num_hits = 0
+    total = 0
+    iterations = test_input.size()[0] // opt.lstm_batch_size
+    test_generator = batch_generator(test_input, opt.lstm_batch_size)
+    label_generator = batch_generator(test_label, opt.lstm_batch_size*opt.lstm_seq_len)
+
+    hidden = (to_var(torch.zeros(2, opt.lstm_batch_size, opt.word_embed_dim)), 
+              to_var(torch.zeros(2, opt.lstm_batch_size, opt.word_embed_dim)))
+    
+    add_loss = 0.0 
+    for t in range(iterations):
+        batch_input = test_generator.__next__ ()
+        batch_label = label_generator.__next__()
+        
+        net.zero_grad()
+        hidden = [state.detach() for state in hidden]
+        test_output, hidden = net(to_var(batch_input), hidden)
+        
+        test_loss = criterion(test_output, to_var(batch_label)).data
+        loss_list.append(test_loss)
+        add_loss += test_loss
+
+    print("Test Loss={0:.4f}".format(float(add_loss) / iterations))
+    print("Test PPL={0:.4f}".format(float(np.exp(add_loss / iterations))))
+
+
+#############################################################
+
+if __name__ == "__main__":
+
+    word_embed_dim = 300
+    char_embedding_dim = 15
+
+    if os.path.exists("cache/prep.pt") is False:
+        print("Cannot find prep.pt")
+
+    objetcs = torch.load("cache/prep.pt")
+
+    word_dict = objetcs["word_dict"]
+    char_dict = objetcs["char_dict"]
+    reverse_word_dict = objetcs["reverse_word_dict"]
+    max_word_len = objetcs["max_word_len"]
+    num_words = len(word_dict)
+
+    print("word/char dictionary built. Start making inputs.")
+
+
+    if os.path.exists("cache/data_sets.pt") is False:
+        
+        test_text  = read_data("./test.txt")
+        test_set  = np.array(text2vec(test_text,  char_dict, max_word_len))
+
+        # Labels are next-word index in word_dict with the same length as inputs
+        test_label  = np.array([word_dict[w] for w in test_text[1:]] + [word_dict[test_text[-1]]])
+
+        category = {"test": test_set, "tlabel":test_label}
+        torch.save(category, "cache/data_sets.pt") 
+    else:
+        data_sets = torch.load("cache/data_sets.pt")
+        test_set  = data_sets["test"]
+        test_label = data_sets["tlabel"]
+        train_set = data_sets["tdata"]
+        train_label = data_sets["trlabel"]
+
+
+    DataTuple = namedtuple("DataTuple", "test_input test_label train_input train_label ")
+    data = DataTuple( test_input=test_set,
+                     test_label=test_label, train_label=train_label, train_input=train_set)
+
+    print("Loaded data sets. Start building network.")
+
+
+
+    USE_GPU = True
+    cnn_batch_size = 700
+    lstm_seq_len = 35 
+    lstm_batch_size = 20
+    
+
+    net = torch.load("cache/net.pkl")
+    
+    Options = namedtuple("Options", [ "cnn_batch_size", "lstm_seq_len",
+            "max_word_len", "lstm_batch_size", "word_embed_dim"])
+    opt = Options(cnn_batch_size=lstm_seq_len*lstm_batch_size,
+                  lstm_seq_len=lstm_seq_len,
+                  max_word_len=max_word_len,
+                  lstm_batch_size=lstm_batch_size,
+                  word_embed_dim=word_embed_dim)
+
+
+    print("Network built. Start testing.")
+
+    test(net, data, opt)

Char-aware_NLM/train.py

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+
+import torch
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import numpy as np
+import os
+from model import charLM
+from utilities import *
+from collections import namedtuple
+from test import test
+
+
+def preprocess():
+    
+    word_dict, char_dict = create_word_char_dict("valid.txt", "train.txt", "test.txt")
+    num_words = len(word_dict)
+    num_char  = len(char_dict)
+    char_dict["BOW"] = num_char+1
+    char_dict["EOW"] = num_char+2
+    char_dict["PAD"] = 0
+    
+    #  dict of (int, string)
+    reverse_word_dict = {value:key for key, value in word_dict.items()}
+    max_word_len = max([len(word) for word in word_dict])
+
+    objects = {
+        "word_dict": word_dict,
+        "char_dict": char_dict,
+        "reverse_word_dict": reverse_word_dict,
+        "max_word_len": max_word_len
+    }
+    
+    torch.save(objects, "cache/prep.pt")
+    print("Preprocess done.")
+
+
+def to_var(x):
+    if torch.cuda.is_available():
+        x = x.cuda()
+    return Variable(x)
+
+
+def train(net, data, opt):
+    
+    torch.manual_seed(1024)
+
+    train_input = torch.from_numpy(data.train_input)
+    train_label = torch.from_numpy(data.train_label)
+    valid_input = torch.from_numpy(data.valid_input)
+    valid_label = torch.from_numpy(data.valid_label)
+
+    # [num_seq, seq_len, max_word_len+2]
+    num_seq = train_input.size()[0] // opt.lstm_seq_len
+    train_input = train_input[:num_seq*opt.lstm_seq_len, :]
+    train_input = train_input.view(-1, opt.lstm_seq_len, opt.max_word_len+2)
+
+    num_seq = valid_input.size()[0] // opt.lstm_seq_len
+    valid_input = valid_input[:num_seq*opt.lstm_seq_len, :]
+    valid_input = valid_input.view(-1, opt.lstm_seq_len, opt.max_word_len+2)
+
+    num_epoch = opt.epochs
+    num_iter_per_epoch = train_input.size()[0] // opt.lstm_batch_size
+    
+    learning_rate = opt.init_lr
+    old_PPL = 100000
+    best_PPL = 100000
+
+    # Log-SoftMax
+    criterion = nn.CrossEntropyLoss()
+    
+    # word_emb_dim == hidden_size / num of hidden units 
+    hidden = (to_var(torch.zeros(2, opt.lstm_batch_size, opt.word_embed_dim)), 
+              to_var(torch.zeros(2, opt.lstm_batch_size, opt.word_embed_dim)))
+
+
+    for epoch in range(num_epoch):
+
+        ################  Validation  ####################
+        net.eval()
+        loss_batch = []
+        PPL_batch = []
+        iterations = valid_input.size()[0] // opt.lstm_batch_size
+        
+        valid_generator = batch_generator(valid_input, opt.lstm_batch_size)
+        vlabel_generator = batch_generator(valid_label, opt.lstm_batch_size*opt.lstm_seq_len)
+
+
+        for t in range(iterations):
+            batch_input = valid_generator.__next__()
+            batch_label = vlabel_generator.__next__()
+
+            hidden = [state.detach() for state in hidden]
+            valid_output, hidden = net(to_var(batch_input), hidden)
+
+            length = valid_output.size()[0]
+
+            # [num_sample-1, len(word_dict)] vs [num_sample-1]
+            valid_loss = criterion(valid_output, to_var(batch_label))
+
+            PPL = torch.exp(valid_loss.data)
+
+            loss_batch.append(float(valid_loss))
+            PPL_batch.append(float(PPL))
+
+        PPL = np.mean(PPL_batch)
+        print("[epoch {}] valid PPL={}".format(epoch, PPL))
+        print("valid loss={}".format(np.mean(loss_batch)))
+        print("PPL decrease={}".format(float(old_PPL - PPL)))
+
+        # Preserve the best model
+        if best_PPL > PPL:
+            best_PPL = PPL
+            torch.save(net.state_dict(), "cache/model.pt")
+            torch.save(net, "cache/net.pkl")
+
+        # Adjust the learning rate
+        if float(old_PPL - PPL) <= 1.0:
+            learning_rate /= 2
+            print("halved lr:{}".format(learning_rate))
+
+        old_PPL = PPL
+
+        ##################################################
+        #################### Training ####################
+        net.train()
+        optimizer  = optim.SGD(net.parameters(), 
+                               lr = learning_rate, 
+                               momentum=0.85)
+
+        # split the first dim
+        input_generator = batch_generator(train_input, opt.lstm_batch_size)
+        label_generator = batch_generator(train_label, opt.lstm_batch_size*opt.lstm_seq_len)
+
+        for t in range(num_iter_per_epoch):
+            batch_input = input_generator.__next__()
+            batch_label = label_generator.__next__()
+
+            # detach hidden state of LSTM from last batch
+            hidden = [state.detach() for state in hidden]
+
+            output, hidden = net(to_var(batch_input), hidden)
+            # [num_word, vocab_size]
+            
+            loss = criterion(output, to_var(batch_label))
+
+            net.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_norm(net.parameters(), 5, norm_type=2)
+            optimizer.step()
+            
+            
+            if (t+1) % 100 == 0:
+                print("[epoch {} step {}] train loss={}, Perplexity={}".format(epoch+1, 
+                    t+1, float(loss.data), float(np.exp(loss.data))))
+
+
+    torch.save(net.state_dict(), "cache/model.pt")
+    print("Training finished.")
+
+
+################################################################
+
+if __name__=="__main__":
+
+    word_embed_dim = 300
+    char_embedding_dim = 15
+
+    if os.path.exists("cache/prep.pt") is False:
+        preprocess()
+
+    objetcs = torch.load("cache/prep.pt")
+
+    word_dict = objetcs["word_dict"]
+    char_dict = objetcs["char_dict"]
+    reverse_word_dict = objetcs["reverse_word_dict"]
+    max_word_len = objetcs["max_word_len"]
+    num_words = len(word_dict)
+
+    print("word/char dictionary built. Start making inputs.")
+
+
+    if os.path.exists("cache/data_sets.pt") is False:
+        train_text = read_data("./train.txt")
+        valid_text = read_data("./valid.txt")
+        test_text  = read_data("./test.txt")
+
+        train_set = np.array(text2vec(train_text, char_dict, max_word_len))
+        valid_set = np.array(text2vec(valid_text, char_dict, max_word_len))
+        test_set  = np.array(text2vec(test_text,  char_dict, max_word_len))
+
+        # Labels are next-word index in word_dict with the same length as inputs
+        train_label = np.array([word_dict[w] for w in train_text[1:]] + [word_dict[train_text[-1]]])
+        valid_label = np.array([word_dict[w] for w in valid_text[1:]] + [word_dict[valid_text[-1]]])
+        test_label  = np.array([word_dict[w] for w in test_text[1:]] + [word_dict[test_text[-1]]])
+
+        category = {"tdata":train_set, "vdata":valid_set, "test": test_set, 
+                    "trlabel":train_label, "vlabel":valid_label, "tlabel":test_label}
+        torch.save(category, "cache/data_sets.pt") 
+    else:
+        data_sets = torch.load("cache/data_sets.pt")
+        train_set = data_sets["tdata"]
+        valid_set = data_sets["vdata"]
+        test_set  = data_sets["test"]
+        train_label = data_sets["trlabel"]
+        valid_label = data_sets["vlabel"]
+        test_label = data_sets["tlabel"]
+
+
+    DataTuple = namedtuple("DataTuple", 
+                "train_input train_label valid_input valid_label test_input test_label")
+    data = DataTuple(train_input=train_set,
+                     train_label=train_label,
+                     valid_input=valid_set,
+                     valid_label=valid_label,
+                     test_input=test_set,
+                     test_label=test_label)
+
+    print("Loaded data sets. Start building network.")
+
+
+
+    USE_GPU = True
+    cnn_batch_size = 700
+    lstm_seq_len = 35
+    lstm_batch_size = 20
+    # cnn_batch_size == lstm_seq_len * lstm_batch_size
+
+    net = charLM(char_embedding_dim, 
+                word_embed_dim, 
+                num_words,
+                len(char_dict),
+                use_gpu=USE_GPU)
+
+    for param in net.parameters():
+        nn.init.uniform(param.data, -0.05, 0.05)
+
+
+    Options = namedtuple("Options", [
+            "cnn_batch_size", "init_lr", "lstm_seq_len",
+            "max_word_len", "lstm_batch_size", "epochs",
+            "word_embed_dim"])
+    opt = Options(cnn_batch_size=lstm_seq_len*lstm_batch_size,
+                  init_lr=1.0,
+                  lstm_seq_len=lstm_seq_len,
+                  max_word_len=max_word_len,
+                  lstm_batch_size=lstm_batch_size,
+                  epochs=35,
+                  word_embed_dim=word_embed_dim)
+
+
+    print("Network built. Start training.")
+
+
+    # You can stop training anytime by "ctrl+C"
+    try:
+        train(net, data, opt)
+    except KeyboardInterrupt:
+        print('-' * 89)
+        print('Exiting from training early')
+
+
+    torch.save(net, "cache/net.pkl")
+    print("save net")
+
+
+    test(net, data, opt)

Char-aware_NLM/utilities.py

@@ -0,0 +1,86 @@
+import torch
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+
+def batch_generator(x, batch_size):
+    # x: [num_words, in_channel, height, width]
+    # partitions x into batches
+    num_step = x.size()[0] // batch_size
+    for t in range(num_step):
+        yield x[t*batch_size:(t+1)*batch_size]
+
+
+def text2vec(words, char_dict, max_word_len):
+    """ Return list of list of int """
+    word_vec = []
+    for word in words:
+        vec = [char_dict[ch] for ch in word] 
+        if len(vec) < max_word_len:
+            vec += [char_dict["PAD"] for _ in range(max_word_len - len(vec))]
+        vec = [char_dict["BOW"]] + vec + [char_dict["EOW"]]
+        word_vec.append(vec)
+    return word_vec
+
+
+def seq2vec(input_words, char_embedding, char_embedding_dim, char_table):
+    """ convert the input strings into character embeddings """
+    # input_words == list of string
+    # char_embedding == torch.nn.Embedding
+    # char_embedding_dim == int
+    # char_table == list of unique chars
+    # Returns: tensor of shape [len(input_words), char_embedding_dim, max_word_len+2]
+    max_word_len = max([len(word) for word in input_words])
+    print("max_word_len={}".format(max_word_len))
+    tensor_list = []
+    
+    start_column = torch.ones(char_embedding_dim, 1)
+    end_column = torch.ones(char_embedding_dim, 1)
+
+    for word in input_words:
+        # convert string to word embedding
+        word_encoding = char_embedding_lookup(word, char_embedding, char_table)
+        # add start and end columns
+        word_encoding = torch.cat([start_column, word_encoding, end_column], 1)
+        # zero-pad right columns
+        word_encoding = F.pad(word_encoding, (0, max_word_len-word_encoding.size()[1]+2)).data
+        # create dimension
+        word_encoding = word_encoding.unsqueeze(0)
+
+        tensor_list.append(word_encoding)
+
+    return torch.cat(tensor_list, 0)
+
+
+def read_data(file_name):
+    # Return: list of strings
+    with open(file_name, 'r') as f:
+        corpus = f.read().lower()
+    import re
+    corpus = re.sub(r"<unk>", "unk", corpus)
+    return corpus.split()
+
+
+def get_char_dict(vocabulary):
+    # vocabulary == dict of (word, int)
+    # Return: dict of (char, int), starting from 1
+    char_dict = dict()
+    count = 1
+    for word in vocabulary:
+        for ch in word:
+            if ch not in char_dict:
+                char_dict[ch] = count
+                count += 1
+    return char_dict
+
+
+def create_word_char_dict(*file_name):
+    text = []
+    for file in file_name:
+        text += read_data(file)
+    word_dict = {word:ix for ix, word in enumerate(set(text))}
+    char_dict = get_char_dict(word_dict)
+    return word_dict, char_dict
+