def test_with_scope_validation(self): categorical_column = fc_lib.categorical_column_with_identity( key='aaa', num_buckets=3) embedding_dimension = 2 initializer = init_ops.truncated_normal_initializer(mean=0.0, stddev=.5) embedding_column = tpu_fc._TPUEmbeddingColumnV2( categorical_column=categorical_column, dimension=embedding_dimension, combiner='mean', initializer=initializer, max_sequence_length=0, learning_rate_fn=None, use_safe_embedding_lookup=True, bypass_scope_validation=False) self.assertIs(categorical_column, embedding_column.categorical_column) self.assertEqual(embedding_dimension, embedding_column.dimension) state_manager = _TestStateManager() with tpu_function.tpu_shard_context(1): with variable_scope.variable_scope('tower1/scope1'): embedding_column.create_state(state_manager) with variable_scope.variable_scope('tower2/scope2'): # With default scope validation, the same column cannot be used in a new # variable scope. with self.assertRaisesRegex( ValueError, 'the variable scope name is different'): embedding_column.create_state(state_manager)
def test_empty_row(self): # Inputs. vocabulary_size = 3 input_sparse_tensor = sparse_tensor.SparseTensorValue( # example 0, ids [] # example 1, ids [0, 1, 3] indices=((1, 0), (1, 1), (1, 4)), values=(0, 1, 3), dense_shape=(2, 5)) input_features = {'inp': input_sparse_tensor} # Embedding variable. embedding_dimension = 2 embedding_values = ( (1., 2.), # id 0 (3., 5.), # id 1 (7., 11.), # id 2 (13., 17.) # id 3 ) def _initializer(shape, dtype, partition_info=None): self.assertAllEqual((vocabulary_size, embedding_dimension), shape) self.assertEqual(dtypes.float32, dtype) self.assertIsNone(partition_info) return embedding_values # Build columns. categorical_column_input = fc_lib.categorical_column_with_identity( key='inp', num_buckets=vocabulary_size) # Set tensor_core_shape to be [None, 20] to ensure some padding and # dynamic batch size. embedding_column = tpu_fc.embedding_column_v2( categorical_column_input, dimension=embedding_dimension, initializer=_initializer, combiner='mean', embedding_lookup_device='tpu_tensor_core', tensor_core_shape=[None, 3]) # Run in TPUContexts so that we hit the intended densification case. context = tpu._TPUInferenceContext('tpu_inference') context.Enter() with tpu_function.tpu_shard_context(1): dense_features = fc_lib.DenseFeatures(embedding_column) expected_lookups = ( # example 0: (0., 0.), # ids [], embedding = [0, 0] # example 1: (2., 3.5 ), # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5] ) embedding_lookup = dense_features(input_features) # Assert expected embedding variable and lookups. global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) self.assertCountEqual( ('dense_features/inp_embedding/embedding_weights:0', ), tuple([v.name for v in global_vars])) embedding_var = global_vars[0] with _initialized_session(): self.assertAllEqual(embedding_values, embedding_var) eval_res = embedding_lookup.eval() self.assertAllEqual(expected_lookups, eval_res) context.Exit()