def setUp(self):
unittest.TestCase.setUp(self)
self.SetBookkeeper(prettytensor.bookkeeper_for_new_graph())
def testArbitraryBatchSizeLstm(self):
# Tests whether the LSTM / Bookkeeper function when batch_size is not
# specified at graph creation time (i.e., None).
super(self.__class__, self).SetBookkeeper(
prettytensor.bookkeeper_for_new_graph())
# Build a graph. Specify None for the batch_size dimension.
placeholder = tf.placeholder(tf.float32, [None, 1])
input_pt = prettytensor.wrap_sequence([placeholder])
output, _ = (input_pt
.sequence_lstm(4)
.squash_sequence()
.softmax_classifier(2))
self.sess.run(tf.initialize_all_variables())
# Use RecurrentRunner for state saving and managing feeds.
recurrent_runner = recurrent_networks.RecurrentRunner(batch_size=1)
# Run with a batch size of 1 for 10 steps, save output for reference.
out_orig = []
for t in xrange(10):
outs = recurrent_runner.run(
[output.name],
{placeholder.name: numpy.array([[1.2]])},
sess=self.sess)
out = outs[0]
self.assertEqual(1, len(out))
self.assertEqual(2, len(out[0]))
out_orig.append(out[0])
# Test the reset functionality - after a reset, the results must be
# identical to what we just got above.
recurrent_runner.reset()
for t in xrange(10):
outs = recurrent_runner.run(
[output.name],
{placeholder.name: numpy.array([[1.2]])},
sess=self.sess)
out = outs[0]
self.assertEqual(1, len(out))
self.assertEqual(2, len(out[0]))
testing.assert_allclose(out[0], out_orig[t])
# Test whether the recurrent runner detects changes to the default graph.
# It should raise an Assertion because RecurrentRunner's state saver
# information (collected during __init__) is not valid anymore.
with tf.Graph().as_default():
placeholder2 = tf.placeholder(tf.float32, [None, 1])
input_pt2 = prettytensor.wrap_sequence([placeholder2])
output2, _ = (input_pt2
.sequence_lstm(4)
.squash_sequence()
.softmax_classifier(2))
self.assertRaises(ValueError,
recurrent_runner.run,
[output2.name], None, self.sess)
# Run with a batch size of 3; first and third input are identical and must
# yield identical output, and the same output as in the single batch run
# above (up to floating point rounding errors).
recurrent_runner = recurrent_networks.RecurrentRunner(batch_size=3)
for t in xrange(10):
outs = recurrent_runner.run(
[output.name],
{placeholder.name: numpy.array([[1.2], [3.4], [1.2]])},
sess=self.sess)
out = outs[0]
self.assertEqual(3, len(out))
self.assertEqual(2, len(out[0]))
testing.assert_allclose(out[0], out[2], rtol=TOLERANCE)
testing.assert_allclose(out[0], out_orig[t], rtol=TOLERANCE)
self.assertFalse((out[0] == out[1]).all())
def setUp(self): unittest.TestCase.setUp(self) self.SetBookkeeper(prettytensor.bookkeeper_for_new_graph())
def performTestArbitraryBatchSizeRnn(self, cell_type):
# Tests whether LSTM / GRU / Bookkeeper function when batch_size is not
# specified at graph creation time (i.e., None).
self.assertTrue(cell_type == 'lstm' or cell_type == 'gru')
super(self.__class__,
self).SetBookkeeper(prettytensor.bookkeeper_for_new_graph())
# Build a graph. Specify None for the batch_size dimension.
placeholder = tf.placeholder(tf.float32, [None, 1])
input_pt = prettytensor.wrap_sequence([placeholder])
if cell_type == 'lstm':
output, _ = (input_pt.sequence_lstm(
4).squash_sequence().softmax_classifier(2))
elif cell_type == 'gru':
output, _ = (input_pt.sequence_gru(
4).squash_sequence().softmax_classifier(2))
self.sess.run(tf.global_variables_initializer())
# Use RecurrentRunner for state saving and managing feeds.
recurrent_runner = recurrent_networks.RecurrentRunner(batch_size=1)
# Run with a batch size of 1 for 10 steps, save output for reference.
out_orig = []
for t in xrange(10):
outs = recurrent_runner.run(
[output.name], {placeholder.name: numpy.array([[1.2]])},
sess=self.sess)
out = outs[0]
self.assertEqual(1, len(out))
self.assertEqual(2, len(out[0]))
out_orig.append(out[0])
# Test the reset functionality - after a reset, the results must be
# identical to what we just got above.
recurrent_runner.reset()
for t in xrange(10):
outs = recurrent_runner.run(
[output.name], {placeholder.name: numpy.array([[1.2]])},
sess=self.sess)
out = outs[0]
self.assertEqual(1, len(out))
self.assertEqual(2, len(out[0]))
testing.assert_allclose(out[0], out_orig[t])
# Test whether the recurrent runner detects changes to the default graph.
# It should raise an Assertion because RecurrentRunner's state saver
# information (collected during __init__) is not valid anymore.
with tf.Graph().as_default():
placeholder2 = tf.placeholder(tf.float32, [None, 1])
input_pt2 = prettytensor.wrap_sequence([placeholder2])
if cell_type == 'lstm':
output2, _ = (input_pt2.sequence_lstm(
4).squash_sequence().softmax_classifier(2))
elif cell_type == 'gru':
output2, _ = (input_pt2.sequence_gru(
4).squash_sequence().softmax_classifier(2))
self.assertRaises(ValueError, recurrent_runner.run, [output2.name],
None, self.sess)
# Run with a batch size of 3; first and third input are identical and must
# yield identical output, and the same output as in the single batch run
# above (up to floating point rounding errors).
recurrent_runner = recurrent_networks.RecurrentRunner(batch_size=3)
for t in xrange(10):
outs = recurrent_runner.run(
[output.name],
{placeholder.name: numpy.array([[1.2], [3.4], [1.2]])},
sess=self.sess)
out = outs[0]
self.assertEqual(3, len(out))
self.assertEqual(2, len(out[0]))
testing.assert_allclose(out[0], out[2], rtol=TOLERANCE)
testing.assert_allclose(out[0], out_orig[t], rtol=TOLERANCE)
# Sanity check to protect against trivial outputs that might hide errors.
# Need to avoid checking after t = 2 since untrained GRUs have a
# tendency to converge to large state values, leading to outputs like
# 1.0, 0.0.
if cell_type == 'gru' and t > 2:
continue
self.assertFalse((out[0] == out[1]).all())
