| /external/tflite-support/tensorflow_lite_support/custom_ops/kernel/ |
| D | ngrams_test.py | 76 flat_values=values, nested_row_splits=tuple(row_splits))
79 output = [output_tensor.flat_values]
159 interpreter.resize_tensor_input(0, input_tensor.flat_values.shape)
165 input_tensor.flat_values.numpy())
172 self.assertEqual(tf_output.flat_values.numpy().tolist(),
190 interpreter.resize_tensor_input(0, input_tensor.flat_values.shape)
196 input_tensor.flat_values.numpy())
203 self.assertEqual(tf_output.flat_values.numpy().tolist(),
220 interpreter.resize_tensor_input(0, input_tensor.flat_values.shape)
226 input_tensor.flat_values.numpy())
[all …]
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| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_tensor_supported_values_test.py | 210 self.assertAllEqual(rt_int.flat_values, tensor_int)
213 self.assertIsInstance(rt_wrapped_int.flat_values, WrappedTensor)
214 self.assertAllEqual(rt_wrapped_int.flat_values.value, tensor_int)
285 WrappedTensor(x.flat_values), x.nested_row_splits)
290 self.assertIsInstance(wrapped_res.flat_values, WrappedTensor)
291 self.assertAllEqual(wrapped_res.flat_values.value, res.flat_values)
316 WrappedTensor(x.flat_values), x.nested_row_splits)
318 WrappedTensor(y.flat_values), y.nested_row_splits)
321 self.assertIsInstance(wrapped_res.flat_values, WrappedTensor)
322 self.assertAllEqual(wrapped_res.flat_values.value, res.flat_values)
[all …]
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| D | ragged_factory_ops.py | 300 flat_values = pylist
303 isinstance(v, (list, tuple)) or np.ndim(v) != 0 for v in flat_values):
307 flat_values = sum((list(v) for v in flat_values), [])
311 inner_shape = get_inner_shape(flat_values)
312 check_inner_shape(flat_values, inner_shape)
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| D | ragged_math_ops.py | 310 array_ops.ones_like(data.flat_values),
315 return total.with_flat_values(total.flat_values / count.flat_values)
326 array_ops.ones_like(data.flat_values),
331 return total.with_flat_values(total.flat_values /
332 math_ops.sqrt(count.flat_values))
497 result = reduce_op(rt_input.flat_values, None, keepdims=keepdims, name=name)
618 array_ops.ones_like(input_tensor.flat_values),
626 total.flat_values / count.flat_values,
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| D | ragged_string_ops.py | 73 return input.with_flat_values(string_bytes_split(input.flat_values))
137 if input_tensor.flat_values.shape.ndims > 1:
142 unicode_encode(input_tensor.flat_values, output_encoding, errors,
428 input.flat_values,
433 flat_input = array_ops.reshape(input.flat_values, [-1])
514 string_split_v2(input.flat_values, sep, maxsplit))
791 return array_ops.reshape(output.flat_values,
810 data=data.flat_values,
822 return array_ops.reshape(output.flat_values,
888 escaped = string_ops.regex_replace(rt.flat_values, r"(['\\])", r"\\\1")
[all …]
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| D | ragged_tensor.py | 650 flat_values, argument
695 with ops.name_scope(name, "RaggedFromNestedValueRowIds", [flat_values] +
697 result = flat_values
707 flat_values, argument
738 [flat_values] + list(nested_row_splits)):
739 result = flat_values
747 flat_values, argument
778 [flat_values] + list(nested_row_lengths)):
779 result = flat_values
979 def flat_values(self): member in RaggedTensor
[all …]
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| D | ragged_from_tensor_op_test.py | 373 rt.flat_values, rt.nested_row_splits, validate=True))
389 rt.flat_values, rt.nested_row_splits, validate=True))
506 rt.flat_values, rt.nested_row_splits, validate=True))
566 rt.flat_values, rt.nested_row_splits, validate=True))
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| D | ragged_dispatch.py | 143 flat_values = [
144 elt.flat_values if ragged_tensor.is_ragged(elt) else elt
150 self._original_op(flat_values, *args, **kwargs))
157 mapped_values = self._original_op(x.flat_values, *args, **kwargs)
215 (x_is_ragged and x.flat_values.shape.ndims <= y.shape.ndims) or
216 (y_is_ragged and y.flat_values.shape.ndims <= x.shape.ndims)):
225 x_values = x.flat_values if ragged_tensor.is_ragged(x) else x
226 y_values = y.flat_values if ragged_tensor.is_ragged(y) else y
467 return x.with_flat_values(nn_ops.dropout(x.flat_values, keep_prob=keep_prob,
476 return x.with_flat_values(nn_ops.dropout_v2(x.flat_values, rate=rate,
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| D | ragged_dispatch_test.py | 55 array_ops.shape(x.flat_values), array_ops.shape(y.flat_values))
150 dense_x = x.flat_values if ragged_tensor.is_ragged(x) else x
158 result_flat_values = array_ops.reshape(result.flat_values, [-1])
270 dense_x = x.flat_values if ragged_tensor.is_ragged(x) else x
271 dense_y = y.flat_values if ragged_tensor.is_ragged(y) else y
280 result_flat_values = array_ops.reshape(result.flat_values, [-1])
332 x.flat_values if ragged_tensor.is_ragged(x) else x for x in inputs
342 result_flat_values = array_ops.reshape(result.flat_values, [-1])
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| D | ragged_functional_ops.py | 163 nrows = tensor_shape.dimension_at_index(value.flat_values.shape, 0).value
166 return value.flat_values
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| D | ragged_concat_ops.py | 219 flat_values = [rt.flat_values for rt in rt_inputs]
220 concatenated_flat_values = array_ops.concat(flat_values, axis=0)
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| D | ragged_tensor_shape.py | 192 array_ops.shape(rt_input.flat_values)[1:],
558 rt_input.flat_values.shape.ndims - 1 - dst_shape.num_inner_dimensions)
562 rt_input.flat_values, ragged_rank=inner_rank_diff,
587 rt_input.flat_values, out_type=dst_shape.dim_size_dtype),
590 array_ops.broadcast_to(rt_input.flat_values, new_shape))
625 dst_values = ragged_util.repeat_ranges(rt_input.flat_values, splits,
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| D | ragged_tensor_test.py | 99 flat_values=[3, 1, 4, 1, 5, 9, 2, 6],
129 self.assertAllEqual(values, rt_value.flat_values)
143 self.assertAllEqual(values, rt_value.flat_values)
540 flat_values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g'])
547 flat_values, nested_row_splits, validate=False)
557 self.assertIs(rt_values_values, flat_values)
564 flat_values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g'])
571 flat_values, nested_row_splits, validate=False)
584 self.assertAllEqual(rt_values_values, flat_values)
670 self.assertAllEqual(rt.flat_values,
[all …]
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| D | ragged_conversion_ops.py | 64 flat_values = op.inputs[1]
68 flat_value_shape = array_ops.shape(flat_values)
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| D | ragged_gather_op_test.py | 295 out.flat_values,
296 (params.nested_row_splits + (params.flat_values, indices,)),
297 out_grad.flat_values)
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| D | ragged_to_sparse_op_test.py | 194 [rt1.flat_values, rt2.flat_values])
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| /external/tflite-support/tensorflow_lite_support/custom_ops/python/ |
| D | sentencepiece_tokenizer.py | 59 tokens = self.tokenize(input_tensor.flat_values)
78 flat_values=output_values,
99 if input_tensor.flat_values.shape.ndims > 1:
103 tokens = self.detokenize(input_tensor.flat_values)
111 self._converted_model_detokenizer, input_tensor.flat_values,
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| D | tflite_text_api.py | 115 flat_values=values, nested_row_splits=args)
119 return ragged_func(data.flat_values, *data.nested_row_splits)
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| /external/tensorflow/tensorflow/python/util/ |
| D | nest.cc | 97 auto flat_values = make_safe(swig::Flatten(value, false)); in FlattenDictItems() local
99 size_t flat_values_sz = PyList_Size(flat_values.get()); in FlattenDictItems()
107 ", value: ", PyObject_ToString(flat_values.get()), ".") in FlattenDictItems()
114 auto* flat_value = PyList_GetItem(flat_values.get(), i); in FlattenDictItems()
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | metrics_utils.py | 522 mask = array_ops.expand_dims(mask.flat_values, -1)
525 flat_values = []
528 flat_values.append(array_ops.expand_dims(value.flat_values, -1))
530 values = flat_values[0] if to_be_stripped else flat_values
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_RaggedGather.pbtxt | 14 The `flat_values` for the `params` RaggedTensor. There was a terminology change
15 at the python level from dense_values to flat_values, so dense_values is the
35 description: "The `flat_values` for the returned RaggedTensor."
|
| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.-ragged-tensor.pbtxt | 12 name: "flat_values"
53 …argspec: "args=[\'cls\', \'flat_values\', \'nested_row_lengths\', \'name\', \'validate\'], varargs…
57 …argspec: "args=[\'cls\', \'flat_values\', \'nested_row_splits\', \'name\', \'validate\'], varargs=…
61 …argspec: "args=[\'cls\', \'flat_values\', \'nested_value_rowids\', \'nested_nrows\', \'name\', \'v…
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| D | tensorflow.ragged.-ragged-tensor-value.pbtxt | 10 name: "flat_values"
|
| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.-ragged-tensor.pbtxt | 12 name: "flat_values"
53 …argspec: "args=[\'cls\', \'flat_values\', \'nested_row_lengths\', \'name\', \'validate\'], varargs…
57 …argspec: "args=[\'cls\', \'flat_values\', \'nested_row_splits\', \'name\', \'validate\'], varargs=…
61 …argspec: "args=[\'cls\', \'flat_values\', \'nested_value_rowids\', \'nested_nrows\', \'name\', \'v…
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| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | table_utils.py | 184 flat_values = ops.convert_to_tensor_v2_with_dispatch(
185 value=inputs.flat_values,
188 flat_values, inputs.nested_row_splits, validate=False)
|