| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/ |
| D | rebatch_dataset_test.py | 168 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=True)
176 combinations.combine(drop_remainder=[True, False])))
177 def testShapeInferenceInputBatchDimDivisible(self, drop_remainder): argument
178 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=True)
180 dataset, batch_sizes=[2, 2], drop_remainder=drop_remainder)
187 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=False)
189 dataset, batch_sizes=[2, 2], drop_remainder=False)
196 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=False)
198 dataset, batch_sizes=[2, 2], drop_remainder=True)
205 dataset = dataset_ops.Dataset.range(10).batch(5, drop_remainder=True)
[all …]
|
| D | dense_to_ragged_batch_test.py | 115 drop_remainder=[True, False])))
117 drop_remainder): argument
127 batching.dense_to_ragged_batch(batch_size, drop_remainder))
131 if end_row > nrows and drop_remainder:
|
| D | map_and_batch_test.py | 121 combinations.combine(drop_remainder=[True, False])))
122 def testMapAndBatchPartialBatch(self, drop_remainder): argument
128 drop_remainder=drop_remainder)))
130 if drop_remainder:
137 if not drop_remainder:
183 lambda x: x, batch_size=100, drop_remainder=True))
|
| /external/tensorflow/tensorflow/python/data/experimental/ops/ |
| D | batching.py | 37 drop_remainder=False, argument
96 ragged_dataset, batch_size=batch_size, drop_remainder=drop_remainder)
156 drop_remainder=False, argument
201 num_parallel_calls, drop_remainder,
216 drop_remainder=False, argument
262 num_parallel_calls, drop_remainder)
331 drop_remainder, use_legacy_function=False): argument
344 drop_remainder, dtype=dtypes.bool, name="drop_remainder")
367 drop_remainder=self._drop_remainder_t,
|
| /external/tensorflow/tensorflow/core/kernels/data/ |
| D | batch_dataset_op.cc | 50 Dataset(OpKernelContext* ctx, int64 batch_size, bool drop_remainder, in Dataset() argument
58 reserve_size_(drop_remainder ? batch_size in Dataset()
60 drop_remainder_(drop_remainder), in Dataset()
67 {"drop_remainder", drop_remainder ? "true" : "false"}, in Dataset()
137 Node* drop_remainder = nullptr; in AsGraphDefInternal() local
138 TF_RETURN_IF_ERROR(b->AddScalar(drop_remainder_, &drop_remainder)); in AsGraphDefInternal()
142 b->AddDataset(this, {input_graph_node, batch_size, drop_remainder}, in AsGraphDefInternal()
271 bool drop_remainder = false; in MakeDataset() local
274 ctx, ParseScalarArgument<bool>(ctx, kDropRemainder, &drop_remainder)); in MakeDataset()
277 *output = new Dataset(ctx, batch_size, drop_remainder, parallel_copy_, input, in MakeDataset()
|
| D | padded_batch_dataset_op.cc | 51 Dataset(OpKernelContext* ctx, int64 batch_size, bool drop_remainder, in Dataset() argument
57 drop_remainder_(drop_remainder), in Dataset()
66 {"drop_remainder", drop_remainder ? "true" : "false"}}) { in Dataset()
160 Node* drop_remainder = nullptr; in AsGraphDefInternal() local
161 TF_RETURN_IF_ERROR(b->AddScalar(drop_remainder_, &drop_remainder)); in AsGraphDefInternal()
173 this, {{0, input_graph_node}, {1, batch_size}, {4, drop_remainder}}, in AsGraphDefInternal()
409 bool drop_remainder = false; in MakeDataset() local
412 ctx, ParseScalarArgument<bool>(ctx, kDropRemainder, &drop_remainder)); in MakeDataset()
456 *output = new Dataset(ctx, batch_size, drop_remainder, parallel_copy_, in MakeDataset()
|
| D | parallel_batch_dataset_op.cc | 67 bool drop_remainder, const DatasetBase* input) in Dataset() argument
74 reserve_size_(drop_remainder ? batch_size in Dataset()
77 drop_remainder_(drop_remainder), in Dataset()
84 {"drop_remainder", drop_remainder ? "true" : "false"}}) { in Dataset()
156 Node* drop_remainder = nullptr; in AsGraphDefInternal() local
157 TF_RETURN_IF_ERROR(b->AddScalar(drop_remainder_, &drop_remainder)); in AsGraphDefInternal()
161 {input_graph_node, batch_size, num_parallel_calls, drop_remainder}, {}, in AsGraphDefInternal()
539 bool drop_remainder = false; in MakeDataset() local
541 ctx, ParseScalarArgument<bool>(ctx, kDropRemainder, &drop_remainder)); in MakeDataset()
544 new Dataset(ctx, batch_size, num_parallel_calls, drop_remainder, input); in MakeDataset()
|
| D | parallel_batch_dataset_op_test.cc | 30 int64 num_parallel_calls, bool drop_remainder, in ParallelBatchDatasetParams() argument
38 drop_remainder_(drop_remainder) { in ParallelBatchDatasetParams()
50 Tensor drop_remainder = in GetInputTensors() local
52 return {batch_size, num_parallel_calls, drop_remainder}; in GetInputTensors()
|
| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/ |
| D | map_and_batch_dataset_serialization_test.py | 48 def build_ds(range_start, drop_remainder=False): argument
59 drop_remainder=drop_remainder))
74 def build_ds(range_start, drop_remainder=False): argument
85 drop_remainder=drop_remainder))
|
| D | rebatch_dataset_serialization_test.py | 40 4 * batch_size, drop_remainder=True),
56 2 * batch_size, drop_remainder=True),
|
| /external/tensorflow/tensorflow/python/data/kernel_tests/ |
| D | window_test.py | 46 drop_remainder=[True, False]) + combinations.combine(
51 drop_remainder=[True, False])))
52 def testWindowDataset(self, count, size, shift, stride, drop_remainder): argument
71 drop_remainder=drop_remainder).flat_map(_flat_map_fn)
86 if not drop_remainder:
132 drop_remainder=True).flat_map(lambda x: x.batch(batch_size=5))
155 drop_remainder=True).flat_map(lambda x: x.batch(batch_size=5))
182 drop_remainder=True).flat_map(lambda x: x.batch(batch_size=4)).window(
184 drop_remainder=True).flat_map(lambda x: x.batch(batch_size=3))
226 drop_remainder=True).flat_map(lambda x: x.batch(batch_size=2))
[all …]
|
| D | cardinality_test.py | 45 lambda: dataset_ops.Dataset.range(5).batch(2, drop_remainder=True), 2),
47 lambda: dataset_ops.Dataset.range(5).batch(2, drop_remainder=False), 3),
102 2, [], drop_remainder=True), 2),
104 2, [], drop_remainder=False), 3),
148 size=2, shift=2, drop_remainder=True), 2),
150 size=2, shift=2, drop_remainder=False), 3),
|
| D | batch_test.py | 48 drop_remainder=[True, False],
50 def testBasic(self, count, batch_size, drop_remainder, num_parallel_calls): argument
72 _map_fn).repeat(count).batch(batch_size, drop_remainder,
76 if drop_remainder:
92 if not drop_remainder and (count * 7) % batch_size > 0:
|
| /external/tensorflow/tensorflow/python/distribute/ |
| D | input_lib_test.py | 572 drop_remainder=[True, False],
578 drop_remainder, distribution): argument
582 2, drop_remainder=drop_remainder)
587 if drop_remainder:
607 drop_remainder=[True, False],
613 iteration_type, drop_remainder, argument
633 return dataset.batch(2, drop_remainder=drop_remainder)
638 if drop_remainder and input_type == "dataset":
667 drop_remainder=[True, False],
673 drop_remainder, argument
[all …]
|
| D | input_lib_type_spec_test.py | 125 drop_remainder=[True, False],
130 drop_remainder): argument
143 2, drop_remainder=drop_remainder)
176 drop_remainder=[True, False],
180 drop_remainder): argument
185 4, drop_remainder=drop_remainder)
|
| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | keras_metrics_test.py | 39 4, drop_remainder=True)
52 3, drop_remainder=True)
65 3, drop_remainder=True)
120 4, drop_remainder=True)
|
| /external/tensorflow/tensorflow/core/ops/compat/ops_history_v2/ |
| D | BatchDatasetV2.pbtxt | 12 name: "drop_remainder"
43 name: "drop_remainder"
|
| D | PaddedBatchDatasetV2.pbtxt | 21 name: "drop_remainder"
67 name: "drop_remainder"
|
| D | ExperimentalMapAndBatchDataset.pbtxt | 20 name: "drop_remainder"
68 name: "drop_remainder"
|
| /external/tensorflow/tensorflow/core/ops/compat/ops_history_v1/ |
| D | BatchDatasetV2.pbtxt | 12 name: "drop_remainder"
43 name: "drop_remainder"
|
| D | PaddedBatchDatasetV2.pbtxt | 21 name: "drop_remainder"
67 name: "drop_remainder"
|
| D | ExperimentalMapAndBatchDataset.pbtxt | 20 name: "drop_remainder"
68 name: "drop_remainder"
|
| /external/tensorflow/tensorflow/core/kernels/data/experimental/ |
| D | rebatch_dataset_op.cc | 302 bool drop_remainder; in MakeDataset() local
304 ctx, ParseScalarArgument<bool>(ctx, "drop_remainder", &drop_remainder)); in MakeDataset()
306 *output = new Dataset(ctx, input, std::move(batch_sizes), drop_remainder, in MakeDataset()
314 std::vector<int64>&& batch_sizes, bool drop_remainder, in Dataset() argument
320 drop_remainder_(drop_remainder), in Dataset()
361 Node* drop_remainder = nullptr; in AsGraphDefInternal() local
362 TF_RETURN_IF_ERROR(b->AddScalar(drop_remainder_, &drop_remainder)); in AsGraphDefInternal()
364 this, {input_graph_node, batch_sizes, drop_remainder}, output)); in AsGraphDefInternal()
|
| /external/tensorflow/tensorflow/compiler/mlir/tensorflow/transforms/ |
| D | tf_data_optimization.td | 27 $batch_size, $drop_remainder, $parallel_copy, $batch_output_types,
30 (TF_ConstOp (GetI64ScalarElementsAttr<1>)), $drop_remainder, $f,
|
| /external/tensorflow/tensorflow/python/profiler/integration_test/ |
| D | mnist_testing_utils.py | 33 train_ds = train_ds.batch(64, drop_remainder=True)
42 eval_ds = eval_ds.batch(64, drop_remainder=True)
|