CBOW

CBOW.py

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

torch.manual_seed(1)

CONTEXT_SIZE = 2  # 2 words to the left, 2 to the right
EMBEDDING_DIM=10
raw_text = """We are about to study the idea of a computational process.
Computational processes are abstract beings that inhabit computers.
As they evolve, processes manipulate other abstract things called data.
The evolution of a process is directed by a pattern of rules
called a program. People create programs to direct processes. In effect,
we conjure the spirits of the computer with our spells.""".split()

# By deriving a set from `raw_text`, we deduplicate the array
vocab = set(raw_text)
vocab_size = len(vocab)

word_to_ix = {word: i for i, word in enumerate(vocab)}
data = []
for i in range(2, len(raw_text) - 2):
    context = [raw_text[i - 2], raw_text[i - 1],
               raw_text[i + 1], raw_text[i + 2]]
    target = raw_text[i]
    data.append((context, target))

class CBOW(nn.Module):

    def __init__(self,vocab_size,embedding_dim,context_size):
    	super(CBOW,self).__init__()
    	self.embeddings=nn.Embedding(vocab_size,embedding_dim)
    	self.linear1=nn.Linear(embedding_dim,vocab_size)

    def forward(self, inputs):
    	embeds = self.embeddings(inputs).sum(dim=0).view((1,-1))
        out = self.linear1(embeds)
        log_probs = F.log_softmax(out)
        return log_probs


def make_context_vector(context, word_to_ix):
    idxs = [word_to_ix[w] for w in context]
    tensor = torch.LongTensor(idxs)
    return autograd.Variable(tensor)


context=make_context_vector(data[0][0], word_to_ix)  # example



# create your model and train.  here are some functions to help you make
# the data ready for use by your module
losses = []
loss_function = nn.NLLLoss()
model = CBOW(vocab_size, EMBEDDING_DIM, CONTEXT_SIZE)
optimizer = optim.SGD(model.parameters(), lr=0.001)

for epoch in range(20000):
    total_loss = torch.Tensor([0])
    for context, target in data:

        # Step 1. Prepare the inputs to be passed to the model (i.e, turn the words
        # into integer indices and wrap them in variables)
        context_idxs = [word_to_ix[w] for w in context]
        context_var = autograd.Variable(torch.LongTensor(context_idxs))
        # Step 2. Recall that torch *accumulates* gradients. Before passing in a
        # new instance, you need to zero out the gradients from the old
        # instance
        model.zero_grad()

        # Step 3. Run the forward pass, getting log probabilities over next
        # words
        log_probs = model(context_var)

        # Step 4. Compute your loss function. (Again, Torch wants the target
        # word wrapped in a variable)
        loss = loss_function(log_probs, autograd.Variable(
            torch.LongTensor([word_to_ix[target]])))

        # Step 5. Do the backward pass and update the gradient
        loss.backward()
        optimizer.step()

        total_loss += loss.data
    losses.append(total_loss)
print(losses)  # The loss decreased every iteration over the training data!

#For sanity check, let's see what our model predicts
acc=0
for context, target in data:

        # Step 1. Prepare the inputs to be passed to the model (i.e, turn the words
        # into integer indices and wrap them in variables)
        context_idxs = [word_to_ix[w] for w in context]
        context_var = autograd.Variable(torch.LongTensor(context_idxs))

        # Step 3. Run the forward pass, getting log probabilities over next
        # words
        log_probs = model(context_var)
        _,idx= torch.min(log_probs,-1)
        print (context,word_to_ix[target],idx.data[0])