def testConv(self):
np.random.seed(7)
with self.test_session() as sess:
for _ in range(16):
sizes = np.random.randint(3, size=1 + np.random.randint(3))
depth = np.random.randint(3)
dims = np.random.randint(3, 6, size=1 + depth)
seqs = jagged.pack([np.random.randn(s, dims[0]).astype(np.float32)
for s in sizes])
widths = np.array([1, 3, 5, 7])[np.random.randint(4, size=depth)]
filters = [np.random.randn(widths[i], dims[i],
dims[i + 1]).astype(np.float32)
for i in range(depth)]
activations = np.array(
[None, tf.nn.relu, tf.sigmoid])[np.random.randint(
3, size=depth)]
out = jagged.conv1d_stack(seqs, filters, activations)
self.assertEqual(type(out), jagged.Jagged)
offsets, flat = sess.run([out.offsets, out.flat])
for i in range(len(sizes)):
x = seqs[i][None]
for filt, activation in zip(filters, activations):
x = tf.nn.conv1d(x, filt, stride=1, padding='SAME')
if activation is not None:
x = activation(x)
x = tf.squeeze(x, [0])
self.assertAllClose(flat[offsets[i]:offsets[i + 1]], x.eval())
def testConvEmpty(self):
with self.test_session() as sess:
sizes = tf.constant([], dtype=tf.int32)
seqs = jagged.jagged(sizes, tf.zeros([0, 7]))
filters = [tf.zeros([3, 7, 13])]
activations = [tf.nn.relu]
out = jagged.conv1d_stack(seqs, filters, activations)
out_sizes, out_flat = sess.run([out.sizes, out.flat])
self.assertAllEqual(out_sizes, [])
self.assertAllEqual(out_flat, np.zeros([0, 13]))
def embed_clauses(clauses, name):
with tf.variable_scope(name):
filters, activations = [], []
dim = hparams.embedding_size
for i in range(conv_layers):
filters.append(
tf.get_variable('filter%d' % i,
shape=(hparams.filter_width, dim,
hidden_size),
initializer=layers.xavier_initializer()))
bias = tf.get_variable('bias%d' % i,
shape=(hidden_size, ),
initializer=tf.constant_initializer(0))
activations.append(functools.partial(bias_relu, bias=bias))
dim = hidden_size
clauses = jagged.conv1d_stack(clauses, filters, activations)
return jagged.reduce_max(clauses)