def test_integer_context_input_throws_error(self):
seq_input = ops.convert_to_tensor(np.arange(100).reshape(5, 5, 4))
context_input = ops.convert_to_tensor(np.arange(100).reshape(10, 10))
seq_input = math_ops.cast(seq_input, dtype=dtypes.float32)
with self.assertRaisesRegexp(TypeError,
'context_input must have dtype float32'):
sfc.concatenate_context_input(context_input, seq_input)
def test_context_input_throws_error(self, context_input_arg):
context_input = ops.convert_to_tensor(context_input_arg)
seq_input = ops.convert_to_tensor(np.arange(100).reshape(5, 5, 4))
seq_input = math_ops.cast(seq_input, dtype=dtypes.float32)
context_input = math_ops.cast(context_input, dtype=dtypes.float32)
with self.assertRaisesRegex(ValueError, 'context_input must have rank 2'):
sfc.concatenate_context_input(context_input, seq_input)
def test_concatenate_context_input(self):
seq_input = ops.convert_to_tensor(np.arange(12).reshape(2, 3, 2))
context_input = ops.convert_to_tensor(np.arange(10).reshape(2, 5))
seq_input = math_ops.cast(seq_input, dtype=dtypes.float32)
context_input = math_ops.cast(context_input, dtype=dtypes.float32)
input_layer = sfc.concatenate_context_input(context_input, seq_input)
expected = np.array([
[[0, 1, 0, 1, 2, 3, 4], [2, 3, 0, 1, 2, 3, 4], [4, 5, 0, 1, 2, 3, 4]],
[[6, 7, 5, 6, 7, 8, 9], [8, 9, 5, 6, 7, 8, 9], [10, 11, 5, 6, 7, 8, 9]]
], dtype=np.float32)
output = self.evaluate(input_layer)
self.assertAllEqual(expected, output)
def test_sequence_example_into_input_layer(self):
examples = [_make_sequence_example().SerializeToString()] * 100
ctx_cols, seq_cols = self._build_feature_columns()
def _parse_example(example):
ctx, seq = parsing_ops.parse_single_sequence_example(
example,
context_features=fc.make_parse_example_spec_v2(ctx_cols),
sequence_features=fc.make_parse_example_spec_v2(seq_cols))
ctx.update(seq)
return ctx
ds = dataset_ops.Dataset.from_tensor_slices(examples)
ds = ds.map(_parse_example)
ds = ds.batch(20)
# Test on a single batch
features = dataset_ops.make_one_shot_iterator(ds).get_next()
# Tile the context features across the sequence features
sequence_input_layer = ksfc.SequenceFeatures(seq_cols)
seq_layer, _ = sequence_input_layer(features)
input_layer = dense_features.DenseFeatures(ctx_cols)
ctx_layer = input_layer(features)
input_layer = sfc.concatenate_context_input(ctx_layer, seq_layer)
rnn_layer = recurrent.RNN(recurrent.SimpleRNNCell(10))
output = rnn_layer(input_layer)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
features_r = sess.run(features)
self.assertAllEqual(features_r['int_list'].dense_shape, [20, 3, 6])
output_r = sess.run(output)
self.assertAllEqual(output_r.shape, [20, 10])
def test_sequence_example_into_input_layer(self):
examples = [_make_sequence_example().SerializeToString()] * 100
ctx_cols, seq_cols = self._build_feature_columns()
def _parse_example(example):
ctx, seq = parsing_ops.parse_single_sequence_example(
example,
context_features=fc.make_parse_example_spec_v2(ctx_cols),
sequence_features=fc.make_parse_example_spec_v2(seq_cols))
ctx.update(seq)
return ctx
ds = dataset_ops.Dataset.from_tensor_slices(examples)
ds = ds.map(_parse_example)
ds = ds.batch(20)
# Test on a single batch
features = ds.make_one_shot_iterator().get_next()
# Tile the context features across the sequence features
sequence_input_layer = sfc.SequenceFeatures(seq_cols)
seq_layer, _ = sequence_input_layer(features)
input_layer = fc.DenseFeatures(ctx_cols)
ctx_layer = input_layer(features)
input_layer = sfc.concatenate_context_input(ctx_layer, seq_layer)
rnn_layer = recurrent.RNN(recurrent.SimpleRNNCell(10))
output = rnn_layer(input_layer)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
features_r = sess.run(features)
self.assertAllEqual(features_r['int_list'].dense_shape, [20, 3, 6])
output_r = sess.run(output)
self.assertAllEqual(output_r.shape, [20, 10])
def call(self, tensors: Tuple[tf.Tensor, tf.Tensor], **kwargs):
ctx, seq = tensors
return sfc.concatenate_context_input(tf.cast(ctx, tf.float32),
tf.cast(seq, tf.float32))
