def fit(self, data_train, *args):
if self.preprocessing:
data_train = self.preprocessing(data_train, *args)
with tf.Graph().as_default() as _:
# create sampler and variables
variables, sampler = machine_reading_sampler(data_train, batch_size=None)
# main graph
objective, _, _ = embedding_updater_model(variables, rank=self.rank,
n_ents=self.n_ents, n_slots=self.n_slots, reg=self.reg)
# tf_debug_gradient(emb0, objective, verbose=False) # This creates new variables...
# train the model
optimizer = tf.train.AdamOptimizer(learning_rate=0.1)
hooks = []
if self.verbose:
hooks += [lambda it, e, xy, f: it and ((it % 100) == 0 or it is 1) and print("%d) loss=%f" % (it, f[0]))]
self.params = learn(objective, sampler, optimizer=optimizer, hooks=hooks, max_epochs=self.max_epochs)
def test_matrix_factorization(verbose=False):
np.random.seed(1)
n, m, rank = 7, 6, 3
mat = np.random.randn(n, rank).dot(np.random.randn(rank, m))
tuples = [([i, n + j], mat[i, j]) for i in range(n) for j in range(m)]
tuple_iterable = data_to_batches(tuples, minibatch_size=n * m)
sampler, (x, y) = feed_dict_sampler(tuple_iterable, types=[np.int64, np.float32])
emb_var = tf.Variable(tf.cast(np.random.randn(n + m, rank), 'float32'))
offset = tf.Variable(tf.cast(1.0, 'float32'))
loss_op = tf.reduce_mean(tf.square(tf.reduce_sum(tf.reduce_prod(tf.gather(emb_var, x), 1), 1) + offset - y))
emb, offset_val = learn(loss_op, sampler, max_epochs=200, variables=[emb_var, offset])
mat_est = emb[:n, :].dot(emb[n:, :].T)
if verbose:
print(np.linalg.norm(mat_est - mat) ** 2) # we should have recovered the low-rank matrix
else:
assert (np.linalg.norm(mat_est - mat) < 1e-3)
def test_multitask_learning(verbose=False):
# factorization of the parameters with multiple linear regressions
np.random.seed(1)
n_data, n_features, n_labels, rank = 6, 5, 4, 3
batch_size = n_data
n_emb = 1 + n_features + n_labels
x_mat = np.random.randn(n_data, n_features)
w_mat = np.random.randn(n_features, rank).dot(np.random.randn(rank, n_labels))
y_mat = x_mat.dot(w_mat)
if False:
x = tf.placeholder(np.float32, [batch_size, n_features], name='answer')
y = tf.placeholder(np.float32, [batch_size, n_labels], name='answer')
sampler = placeholder_feeder((x, y), [(x_mat, y_mat)]) # only one sample
emb = tf.Variable(tf.cast(np.random.randn(n_features + n_labels, rank), 'float32'))
w = tf.matmul(emb[:n_features, :], emb[n_features:, :], transpose_b=True)
preds = tf.matmul(x, w)
objective = tf.reduce_sum(tf.square(preds - y))
hooks = [lambda it, e, xy, f: it and ((it % 10) == 0) and print("%d) loss=%f" % (it, f))]
emb_val, = learn(objective, sampler, hooks=hooks, max_epochs=2000, variables=[emb])
mat_est = x_mat.dot(emb_val[:n_features, :]).dot(emb_val[n_features:, :].T)
else:
data = []
for i in range(n_data):
data.append(([((NEW_ID, j + 1), x_mat[i, j]) for j in range(n_features)],
[((NEW_ID, k + 1 + n_features), y_mat[i, k]) for k in range(n_labels)]))
data_arr = vectorize_samples(data)
batches = data_to_batches(data_arr, minibatch_size=batch_size)
qc = tf.placeholder(np.int64, [batch_size, n_features, 2], name='question_in_context')
yc = tf.placeholder(np.float32, [batch_size, n_features], name='answer_in_context')
q = tf.placeholder(np.int64, [batch_size, n_labels, 2], name='question')
y = tf.placeholder(np.float32, [batch_size, n_labels], name='answer')
sampler = placeholder_feeder((qc, yc, q, y), batches)
[print(s) for s in sampler]
emb0 = tf.Variable(tf.cast(np.random.randn(n_emb, rank), 'float32'))
def reader(context_inputs, context_ouputs):
context_embs = tf.gather(emb0, context_inputs[:, :, 1])
# preds = tf.reshape(tf.matmul(
# tf.reshape(context_embs, (n_data * n_features, rank)),
# tf.reshape(emb0[0, :], [rank, 1])),
# (n_data, n_features))
# residues = tf.tile(tf.reshape(preds - yc, (n_data, n_features, 1)), [1, 1, rank])
# embs_after_reading = tf.tile(tf.reshape(emb0[0, :], (1, rank)), (n_data, 1)) \
# + tf.reduce_mean(context_embs * residues, 1) * step_size
# step_size = tf.Variable(tf.cast(1.0, 'float32'), trainable=True)
yc_rep = tf.tile(tf.reshape(context_ouputs, (n_data, n_features, 1)), (1, 1, rank))
embs_after_reading = 0 * tf.tile(tf.reshape(emb0[0, :], (1, rank)), (n_data, 1)) \
+ tf.reduce_sum(context_embs * yc_rep, 1) # * step_size
return embs_after_reading
def answerer(embeddings, question):
embs_after_reading_mat = tf.tile(tf.reshape(embeddings, [n_data, 1, 1, rank]), [1, n_labels, 1, 1])
fixed_embs = tf.reshape(tf.gather(emb0, question[:, :, 1]), [n_data, n_labels, 1, rank])
emb1_question = tf.concat(2, [fixed_embs, embs_after_reading_mat])
return tf.reduce_sum(tf.reduce_prod(emb1_question, 2), 2)
def loss(pred, gold):
return tf.nn.l2_loss(pred - gold)
objective = loss(answerer(reader(qc, yc), q), y)
hooks = [lambda it, e, xy, f: it and ((it % 10) == 0) and print("%d) loss=%f" % (it, f))]
emb_val, = learn(objective, sampler, hooks=hooks, max_epochs=2000, variables=[emb0])
mat_est = x_mat.dot(emb_val[1:n_features + 1, :]).dot(emb_val[n_features + 1:, :].T)
if verbose:
print(0.5 * np.linalg.norm(mat_est - y_mat) ** 2) # we should have recovered the low-rank matrix
else:
assert (np.linalg.norm(mat_est - y_mat) < 1e-3)
def test_larcqy(verbose=False):
# factorization of the parameters with multiple linear regressions
# input_types = {'index', 'features'}
input_types = {'index'}
input_types = {'features'}
n1, n2, d1, d2, rank_gold = 7, 6, 5, 4, 3
# random data generation
np.random.seed(1)
emb_noise, noise = 1, 0
batch_size = n1 * n2
t = lambda x: np.round(x, 1)
data_emb1 = t(np.random.randn(n1, rank_gold) * emb_noise)
data_emb2 = t(np.random.randn(n2, rank_gold) * emb_noise)
feat_emb1 = t(np.random.randn(d1, rank_gold) * emb_noise)
feat_emb2 = t(np.random.randn(d2, rank_gold) * emb_noise)
x1_mat = data_emb1.dot(feat_emb1.T)
x2_mat = data_emb2.dot(feat_emb2.T)
a1 = x1_mat.dot(feat_emb1)
a2 = x2_mat.dot(feat_emb2)
y_mat = a1.dot(a2.T) + np.random.randn(n1, n2) * noise
# data stuff
data = []
n_ents = 2
for i in range(n1):
for j in range(n2):
inputs = []
if 'features' in input_types:
inputs += [((0, k + 2), x1_mat[i, k]) for k in range(d1)] \
+ [((1, k + 2 + d1), x2_mat[j, k]) for k in range(d2)]
n_ents = d1 + d2 + 2
if 'index' in input_types:
inputs += [((0, d1 + d2 + 2 + i), 1.0), ((1, d1 + d2 + 2 + n1 + j), 1.0)]
n_ents = d1 + d2 + n1 + n2 + 2
outputs = [((0, 1), y_mat[i, j])]
data.append((inputs, outputs))
data_arr = vectorize_samples(data)
batches = data_to_batches(data_arr, batch_size, dtypes=[np.int64, np.float32, np.int64, np.float32])
if False:
qc = tf.placeholder(np.int64, (batch_size, n_ents, 2), name='question_in_context')
yc = tf.placeholder(np.float32, (batch_size, n_ents), name='answer_in_context')
q = tf.placeholder(np.int64, (batch_size, 1, 2), name='question')
y = tf.placeholder(np.float32, (batch_size, 1), name='answer')
sampler = placeholder_feeder((qc, yc, q, y), batches)
sampler = [x for x in sampler]
else:
qc0, yc0, q0, y0 = [x for x in batches][0]
# print(qc0, yc0, q0, y0)
qc = tf.Variable(qc0)
yc = tf.Variable(yc0)
q = tf.Variable(q0)
y = tf.Variable(y0)
# model definition
rank = min(rank_gold, max(min(n1, n2), min(d1, d2)))
print(rank)
if False:
emb0_val = np.concatenate((np.zeros((2, rank)), feat_emb1, feat_emb2))
emb0_val += np.random.randn(n_ents, rank) * 0.1
# emb0_val = np.round(emb0_val, 1)
else:
emb0_val = np.random.randn(n_ents, rank)
emb0 = tf.constant(emb0_val.tolist(), dtype=np.float32, shape=(n_ents, rank))
loss = total_loss_quadratic
# reading step
emb1 = reader(emb0=emb0, context=(qc, yc), n_slots=2)
# answering step
objective = loss(answerer(emb1, q), y) # + 1e-6 * tf.nn.l2_loss(emb0)
# tf_debug_gradient(emb0, objective, verbose=False) # This creates new variables...
# train the model
optimizer = tf.train.AdamOptimizer(learning_rate=0.1)
hooks = [lambda it, e, xy, f: it and ((it % 100) == 0) and print("%d) loss=%f" % (it, f[0]))]
# hooks += [OperatorHook(tf_show(grads[0]), sampler[0][1])]
params = learn(objective, optimizer=optimizer, hooks=hooks, max_epochs=300, variables=[emb0])
# mat_est = model.numeric_eval(params, x_mat)
#
if verbose:
# print(0.5 * np.linalg.norm(mat_est - y_mat) ** 2) # we should have recovered the low-rank matrix
print(params[-1])
pass
