def testGrid2LSTMCellLegacy(self):
"""Test for legacy case (when state_is_tuple=False)."""
with self.test_session() as sess:
with variable_scope.variable_scope(
'root', initializer=init_ops.constant_initializer(0.5)):
x = array_ops.zeros([1, 3])
m = array_ops.zeros([1, 8])
cell = grid_rnn_cell.Grid2LSTMCell(2,
use_peepholes=True,
state_is_tuple=False,
output_is_tuple=False)
self.assertEqual(cell.state_size, 8)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 8))
sess.run([variables.global_variables_initializer()])
res = sess.run(
[g, s], {
x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]])
})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 8))
self.assertAllClose(res[0], [[0.95686918, 0.95686918]])
self.assertAllClose(res[1], [[
2.41515064, 2.41515064, 0.95686918, 0.95686918, 1.38917875,
1.49043763, 0.83884692, 0.86036491
]])
def testGrid2LSTMCellWithRelu(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
'root', initializer=init_ops.constant_initializer(0.5)):
x = array_ops.zeros([1, 3])
m = ((array_ops.zeros([1, 2]), array_ops.zeros([1, 2])), )
cell = grid_rnn_cell.Grid2LSTMCell(
2, use_peepholes=True, non_recurrent_fn=nn_ops.relu)
self.assertEqual(cell.state_size, ((2, 2), ))
g, s = cell(x, m)
self.assertEqual(g[0].get_shape(), (1, 2))
self.assertEqual(s[0].c.get_shape(), (1, 2))
self.assertEqual(s[0].h.get_shape(), (1, 2))
sess.run([variables.global_variables_initializer()])
res_g, res_s = sess.run(
[g, s], {
x: np.array([[1., 1., 1.]]),
m: ((np.array([[0.1, 0.2]]), np.array([[0.3, 0.4]])), )
})
self.assertEqual(res_g[0].shape, (1, 2))
self.assertAllClose(res_g[0], [[2.1831727, 2.1831727]])
self.assertAllClose(res_s,
(([[0.92270052, 1.02325559]
], [[0.66159075, 0.70475441]]), ))
def testGrid2LSTMCellTied(self):
with self.test_session(use_gpu=False) as sess:
with variable_scope.variable_scope('root',
initializer=init_ops.constant_initializer(0.5)):
x = array_ops.zeros([1, 3])
m = ((array_ops.zeros([1, 2]), array_ops.zeros([1, 2])),
(array_ops.zeros([1, 2]), array_ops.zeros([1, 2])))
cell = grid_rnn_cell.Grid2LSTMCell(2, tied=True, use_peepholes=True)
self.assertEqual(cell.state_size, ((2, 2), (2, 2)))
g, s = cell(x, m)
self.assertEqual(g[0].get_shape(), (1, 2))
self.assertEqual(s[0].c.get_shape(), (1, 2))
self.assertEqual(s[0].h.get_shape(), (1, 2))
self.assertEqual(s[1].c.get_shape(), (1, 2))
self.assertEqual(s[1].h.get_shape(), (1, 2))
sess.run([variables.global_variables_initializer()])
res_g, res_s = sess.run(
[g, s], {x: np.array([[1., 1., 1.]]),
m: ((np.array([[0.1, 0.2]]), np.array([[0.3, 0.4]])),
(np.array([[0.5, 0.6]]), np.array([[0.7, 0.8]])))})
self.assertEqual(res_g[0].shape, (1, 2))
self.assertEqual(res_s[0].c.shape, (1, 2))
self.assertEqual(res_s[0].h.shape, (1, 2))
self.assertEqual(res_s[1].c.shape, (1, 2))
self.assertEqual(res_s[1].h.shape, (1, 2))
self.assertAllClose(res_g[0], [[0.95686918, 0.95686918]])
self.assertAllClose(res_s, (([[2.41515064, 2.41515064]],
[[0.95686918, 0.95686918]]),
([[1.38917875, 1.49043763]],
[[0.83884692, 0.86036491]])))
def testGrid2LSTMCellReLUWithRNN(self):
batch_size = 3
input_size = 5
max_length = 6 # unrolled up to this length
num_units = 2
with variable_scope.variable_scope(
'root', initializer=init_ops.constant_initializer(0.5)):
cell = grid_rnn_cell.Grid2LSTMCell(
num_units=num_units, non_recurrent_fn=nn_ops.relu)
inputs = max_length * [
array_ops.placeholder(
dtypes.float32, shape=(batch_size, input_size))
]
outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
self.assertEqual(state.get_shape(), (batch_size, 4))
for out, inp in zip(outputs, inputs):
self.assertEqual(out.get_shape()[0], inp.get_shape()[0])
self.assertEqual(out.get_shape()[1], num_units)
self.assertEqual(out.dtype, inp.dtype)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
input_value = np.ones((batch_size, input_size))
values = sess.run(outputs + [state], feed_dict={inputs[0]: input_value})
for v in values:
self.assertTrue(np.all(np.isfinite(v)))
def testGrid2LSTMCellWithRNNAndDynamicBatchSize(self):
"""Test for #4296
"""
input_size = 5
max_length = 6 # unrolled up to this length
num_units = 2
with variable_scope.variable_scope('root',
initializer=init_ops.constant_initializer(0.5)):
cell = grid_rnn_cell.Grid2LSTMCell(num_units=num_units)
inputs = max_length * [
array_ops.placeholder(
dtypes.float32, shape=(None, input_size))
]
outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
for out, inp in zip(outputs, inputs):
self.assertEqual(len(out), 1)
self.assertTrue(out[0].get_shape()[0].value is None)
self.assertEqual(out[0].get_shape()[1], num_units)
self.assertEqual(out[0].dtype, inp.dtype)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
input_value = np.ones((3, input_size))
values = sess.run(outputs + [state],
feed_dict={inputs[0]: input_value})
for tp in values[:-1]:
for v in tp:
self.assertTrue(np.all(np.isfinite(v)))
for tp in values[-1]:
for st in tp:
for v in st:
self.assertTrue(np.all(np.isfinite(v)))
