trivial word embeddings eg

theano_word_embeddings.py

#!/usr/bin/env python
# see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/
import theano
import theano.tensor as T
import numpy as np
import random

E = np.asarray(np.random.randn(6, 2), dtype='float32')
t_E = theano.shared(E)
t_idxs = T.ivector()
t_embedding_output = t_E[t_idxs]
t_dot_product = T.dot(t_embedding_output[0], t_embedding_output[1])
t_label = T.iscalar()
gradient = T.grad(cost=abs(t_label - t_dot_product), wrt=t_E)
updates = [(t_E, t_E - 0.01 * gradient)]
train = theano.function(inputs=[t_idxs, t_label], outputs=[], updates=updates)
print "i n d0 d1"
for i in range(0, 10000):
    v1, v2 = random.randint(0, 5), random.randint(0, 5)
    label = 1.0 if (v1/2 == v2/2) else 0.0
    train([v1, v2], label)
    if i % 100 == 0:
        for n, embedding in enumerate(t_E.get_value()):
            print i, n, embedding[0], embedding[1]

theano_word_embeddings__inc.py

#!/usr/bin/env python
import theano
import theano.tensor as T
import numpy as np
import random

E = np.asarray(np.random.randn(6, 2), dtype='float32')
t_E = theano.shared(E)
t_idxs = T.ivector()
t_embedding_output = t_E[t_idxs]
t_dot_product = T.dot(t_embedding_output[0], t_embedding_output[1])
t_label = T.iscalar()
gradient = T.grad(cost=abs(t_label - t_dot_product), wrt=t_embedding_output)
updates = [(t_E, T.inc_subtensor(t_embedding_output, -0.01 * gradient))]
train = theano.function(inputs=[t_idxs, t_label], outputs=[], updates=updates)
print "i n d0 d1"
for i in range(0, 10000):
    v1, v2 = random.randint(0, 5), random.randint(0, 5)
    label = 1.0 if (v1/2 == v2/2) else 0.0
    train([v1, v2], label)
    if i % 100 == 0:
        for n, embedding in enumerate(t_E.get_value()):
            print i, n, embedding[0], embedding[1]