def instances():
lengths = numpy.asarray([5, 4], dtype="int32")
keys = numpy.arange(9, dtype="uint64")
values = numpy.ones(9, dtype="float32")
X = (keys, values, lengths)
y = numpy.asarray([0, 2], dtype="int32")
return X, to_categorical(y, n_classes=3)
def get_characters_loss(ops, docs, prediction, nr_char):
"""Compute a loss based on a number of characters predicted from the docs."""
target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
target_ids = target_ids.reshape((-1,))
target = ops.asarray(to_categorical(target_ids, n_classes=256), dtype="f")
target = target.reshape((-1, 256 * nr_char))
diff = prediction - target
loss = (diff ** 2).sum()
d_target = diff / float(prediction.shape[0])
return loss, d_target
def get_dummy_data(n_samples, n_tags, n_vocab, length_mean, length_variance):
Xs = []
Ys = []
for _ in range(n_samples):
length = numpy.random.normal(size=1,
scale=length_variance) + length_mean
shape = (max(1, int(length)), )
X = numpy.random.uniform(0, n_vocab - 1, shape)
Y = numpy.random.uniform(0, n_tags - 1, shape)
assert X.size, length
assert Y.size, length
Xs.append(X.reshape((-1, 1)).astype("i"))
Ys.append(to_categorical(Y.astype("i")))
return Xs, Ys
