def testPrepareFeaturesForSQSS(self):
mode = model_fn_lib.ModeKeys.TRAIN
seq_feature_name = 'seq_feature'
ctx_feature_name = 'ctx_feature'
input_key_column_name = 'input_key_column'
sequence_length = 4
seq_feature = constant_op.constant(1.0, shape=[sequence_length])
ctx_feature = constant_op.constant(2.0)
input_key0 = constant_op.constant('input0')
features = {
input_key_column_name: input_key0,
seq_feature_name: seq_feature,
ctx_feature_name: ctx_feature
}
labels = constant_op.constant(5.0, shape=[sequence_length])
sequence_feature_columns = [
feature_column.real_valued_column(seq_feature_name, dimension=1)
]
context_feature_columns = [
feature_column.real_valued_column(ctx_feature_name, dimension=1)
]
expected_input_key = b'input0'
expected_sequence = {
ssre.RNNKeys.LABELS_KEY: np.array([5., 5., 5., 5.]),
seq_feature_name: np.array([1., 1., 1., 1.]),
}
expected_context = {ctx_feature_name: 2.}
input_key, sequence, context = ssre._prepare_features_for_sqss(
features, labels, mode, input_key_column_name,
sequence_feature_columns, context_feature_columns)
def assert_equal(a, b):
self.assertEqual(sorted(a), sorted(b))
for k, v in a.items():
self.assertAllEqual(v, b[k])
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(data_flow_ops.initialize_all_tables())
actual_input_key, actual_sequence, actual_context = sess.run(
[input_key, sequence, context])
self.assertEqual(expected_input_key, actual_input_key)
assert_equal(expected_sequence, actual_sequence)
assert_equal(expected_context, actual_context)
def testPrepareFeaturesForSQSS(self):
mode = model_fn_lib.ModeKeys.TRAIN
seq_feature_name = 'seq_feature'
sparse_seq_feature_name = 'wire_cast'
ctx_feature_name = 'ctx_feature'
sequence_length = 4
embedding_dimension = 8
features = {
sparse_seq_feature_name:
sparse_tensor.SparseTensor(
indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0], [1, 1, 1],
[2, 0, 0], [2, 1, 1]],
values=[
b'marlo', b'stringer', b'omar', b'stringer', b'marlo',
b'marlo', b'omar'
],
dense_shape=[3, 2, 2]),
seq_feature_name:
constant_op.constant(
1.0, shape=[sequence_length]),
ctx_feature_name:
constant_op.constant(2.0)
}
labels = constant_op.constant(5.0, shape=[sequence_length])
wire_cast = feature_column.sparse_column_with_keys(
'wire_cast', ['marlo', 'omar', 'stringer'])
sequence_feature_columns = [
feature_column.real_valued_column(
seq_feature_name, dimension=1), feature_column.embedding_column(
wire_cast,
dimension=embedding_dimension,
initializer=init_ops.ones_initializer())
]
context_feature_columns = [
feature_column.real_valued_column(
ctx_feature_name, dimension=1)
]
expected_sequence = {
rnn_common.RNNKeys.LABELS_KEY:
np.array([5., 5., 5., 5.]),
seq_feature_name:
np.array([1., 1., 1., 1.]),
sparse_seq_feature_name:
sparse_tensor.SparseTensor(
indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0], [1, 1, 1],
[2, 0, 0], [2, 1, 1]],
values=[
b'marlo', b'stringer', b'omar', b'stringer', b'marlo',
b'marlo', b'omar'
],
dense_shape=[3, 2, 2]),
}
expected_context = {ctx_feature_name: 2.}
sequence, context = ssre._prepare_features_for_sqss(
features, labels, mode, sequence_feature_columns,
context_feature_columns)
def assert_equal(expected, got):
self.assertEqual(sorted(expected), sorted(got))
for k, v in expected.items():
if isinstance(v, sparse_tensor.SparseTensor):
self.assertAllEqual(v.values.eval(), got[k].values)
self.assertAllEqual(v.indices.eval(), got[k].indices)
self.assertAllEqual(v.dense_shape.eval(), got[k].dense_shape)
else:
self.assertAllEqual(v, got[k])
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
actual_sequence, actual_context = sess.run(
[sequence, context])
assert_equal(expected_sequence, actual_sequence)
assert_equal(expected_context, actual_context)
def testPrepareFeaturesForSQSS(self):
mode = model_fn_lib.ModeKeys.TRAIN
seq_feature_name = 'seq_feature'
sparse_seq_feature_name = 'wire_cast'
ctx_feature_name = 'ctx_feature'
sequence_length = 4
embedding_dimension = 8
features = {
sparse_seq_feature_name:
sparse_tensor.SparseTensor(
indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0], [1, 1, 1],
[2, 0, 0], [2, 1, 1]],
values=[
b'marlo', b'stringer', b'omar', b'stringer', b'marlo',
b'marlo', b'omar'
],
dense_shape=[3, 2, 2]),
seq_feature_name:
constant_op.constant(
1.0, shape=[sequence_length]),
ctx_feature_name:
constant_op.constant(2.0)
}
labels = constant_op.constant(5.0, shape=[sequence_length])
wire_cast = feature_column.sparse_column_with_keys(
'wire_cast', ['marlo', 'omar', 'stringer'])
sequence_feature_columns = [
feature_column.real_valued_column(
seq_feature_name, dimension=1), feature_column.embedding_column(
wire_cast,
dimension=embedding_dimension,
initializer=init_ops.ones_initializer())
]
context_feature_columns = [
feature_column.real_valued_column(
ctx_feature_name, dimension=1)
]
expected_sequence = {
rnn_common.RNNKeys.LABELS_KEY:
np.array([5., 5., 5., 5.]),
seq_feature_name:
np.array([1., 1., 1., 1.]),
sparse_seq_feature_name:
sparse_tensor.SparseTensor(
indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0], [1, 1, 1],
[2, 0, 0], [2, 1, 1]],
values=[
b'marlo', b'stringer', b'omar', b'stringer', b'marlo',
b'marlo', b'omar'
],
dense_shape=[3, 2, 2]),
}
expected_context = {ctx_feature_name: 2.}
sequence, context = ssre._prepare_features_for_sqss(
features, labels, mode, sequence_feature_columns,
context_feature_columns)
def assert_equal(expected, got):
self.assertEqual(sorted(expected), sorted(got))
for k, v in expected.items():
if isinstance(v, sparse_tensor.SparseTensor):
self.assertAllEqual(v.values.eval(), got[k].values)
self.assertAllEqual(v.indices.eval(), got[k].indices)
self.assertAllEqual(v.dense_shape.eval(), got[k].dense_shape)
else:
self.assertAllEqual(v, got[k])
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(data_flow_ops.initialize_all_tables())
actual_sequence, actual_context = sess.run(
[sequence, context])
assert_equal(expected_sequence, actual_sequence)
assert_equal(expected_context, actual_context)
