| /external/tensorflow/tensorflow/core/kernels/ |
| reshape_util.h | 25 void Reshape(OpKernelContext *context, const Tensor &input_indices_in,
|
| sparse_reshape_op.cc | 37 Reshape(context, context->input(0), context->input(1), context->input(2),
|
| quantized_reshape_op_test.cc | 43 TEST_F(QuantizedReshapeTest, Reshape) {
|
| /external/tensorflow/tensorflow/compiler/xla/service/ |
| batch_dot_simplification_test.cc | 47 op::Reshape(op::Dot(
48 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
71 op::Reshape(op::Dot(
72 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
95 op::Reshape(op::Dot(
96 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
119 op::Reshape(op::Dot
[all...] |
| dynamic_index_splitter_test.cc | 54 op::Reshape(op::Slice(op::Parameter(1))),
55 op::Reshape(op::Slice(op::Parameter(1))),
56 op::Reshape(op::Slice(op::Parameter(1)))));
92 op::Reshape(op::Slice(op::Parameter(1))),
93 op::Reshape(op::Slice(op::Parameter(1))),
94 op::Reshape(op::Slice(op::Parameter(1)))));
|
| reshape_mover_test.cc | 57 op::Add(op::Reshape(param0), op::Reshape(param1)));
62 op::Add(op::Reshape(param0), op::Reshape(param1)));
75 // Verifies that the reshape is not moved, since rng0 is trivially reshapable
100 op::Add(op::Reshape(rng0), const1));
105 op::Add(op::Reshape(rng0), const1));
126 op::Add(op::Reshape(param0), op::Reshape(param1)));
132 op::Add(op::Reshape(op::Parameter()), op::Reshape(op::Parameter())))
[all...] |
| pattern_matcher_test.cc | 473 using match::Reshape;
482 r1 = u32[1] reshape(c1)
483 r2 = u32[1] reshape(c2)
484 r3 = u32[1] reshape(c3)
485 r4 = u32[1] reshape(c4)
492 Concatenate(Reshape(ConstantScalar(1)), Reshape(ConstantScalar(2)),
493 Reshape(ConstantScalar(3)), Reshape(ConstantScalar(4)))));
496 Concatenate(Reshape(ConstantScalar(2)), Reshape(ConstantScalar(1))
899 auto reshape = local
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| reshape_motion_test.cc | 49 auto c = Reshape(a, {6});
50 auto d = Reshape(b, {6});
|
| reshape_test.cc | 104 auto reshape = Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, local
106 auto new_shape = builder.GetShape(reshape).ConsumeValueOrDie();
121 Reshape(/*operand=*/a, /*dimensions=*/{}, /*new_sizes=*/{1});
201 Reshape(/*operand=*/parameter, /*dimensions=*/{0},
216 Reshape(/*operand=*/parameter, /*dimensions=*/{0},
231 Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1},
246 Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1},
263 Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0},
309 Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}
[all...] |
| convolution_test.cc | 445 auto input_r5 = input_r1.Reshape(input_dims).ConsumeValueOrDie();
450 auto filter_r5 = filter_r1.Reshape(filter_dims).ConsumeValueOrDie();
455 auto expected_r5 = expected_r1.Reshape({1, 3, 1, 2, 3}).ConsumeValueOrDie();
508 auto input_r4 = input_r1.Reshape(input_dims).ConsumeValueOrDie();
513 auto filter_r4 = filter_r1.Reshape(filter_dims).ConsumeValueOrDie();
517 auto expected_r4 = expected_r1.Reshape({1, 1, 1, 3}).ConsumeValueOrDie();
568 auto input_r4 = input_r1.Reshape(input_dims).ConsumeValueOrDie();
573 auto filter_r4 = filter_r1.Reshape(filter_dims).ConsumeValueOrDie();
581 auto expected_r4 = expected_r1.Reshape({1, 1, 1, 15}).ConsumeValueOrDie();
634 auto input_r4 = input_r1.Reshape(input_dims).ConsumeValueOrDie()
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ |
| reshape_test.py | 15 """Tests for Reshape Bijector."""
23 from tensorflow.contrib.distributions.python.ops.bijectors.reshape import Reshape
32 """Base class for testing the reshape transformation.
51 expected_y = np.reshape(expected_x, [4, 6])
55 bijector = Reshape(
68 self.assertEqual("reshape", bijector.name)
81 bijector = Reshape(
100 expected_y = np.reshape(expected_x, [4, 3])
107 bijector = Reshape(
[all...] |
| /external/tensorflow/tensorflow/cc/gradients/ |
| data_flow_grad.cc | 78 // reshape(range(partitions_size), [5]) = [0, 1, 2, 3, 4]
81 auto original_indices = Reshape(
100 // reshape back into a data-shaped tensor to propagate gradients for the data
102 grad_outputs->push_back(Reshape(scope, reconstructed, Shape(scope, data)));
|
| /external/tensorflow/tensorflow/compiler/tf2xla/lib/ |
| data_format.cc | 40 // Now merge the adjacent dimensions with a reshape.
45 return xla::Reshape(xla::Transpose(input, permutation), contracted_shape);
58 // Split the `dim` into two dimensions with a reshape. The size of the new
74 return xla::Transpose(xla::Reshape(input, expanded_shape), permutation);
|
| broadcast.cc | 86 output = xla::Reshape(output, output_dims);
|
| /external/tensorflow/tensorflow/compiler/tf2xla/ |
| const_analysis_test.cc | 40 auto c = ops::Reshape(root, arg2, b);
51 // Arg 1 must be constant because it flows to the shape argument of a Reshape.
52 // Arg 2 is used only as the value input to a Reshape and need not be const.
71 auto a = ops::Reshape(root, arg0, arg1);
72 auto b = ops::Reshape(root, arg2, a);
100 Output reshape = ops::Reshape(root, arg1, add); local
122 // Force const analysis to pretend that the shape argument to `reshape` does
124 Output reshape = ops::Reshape(root, arg1, add) local
[all...] |
| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| diag_op.cc | 62 // into a "middle" dimension. We can do this with a reshape + implicit
68 xla::XlaOp input_broadcast = xla::Reshape(input, broadcast_dims);
103 input = xla::Reshape(input, {size});
113 diag = xla::Reshape(diag, new_dims);
153 xla::XlaOp output = xla::Reshape(
154 xla::GetMatrixDiagonal(xla::Reshape(input, {new_size, new_size})),
|
| categorical_op.cc | 106 argmax = xla::Reshape(argmax, {batch_size, 1});
143 auto seed0 = xla::Reshape(xla::Slice(seed, {0}, {1}, {1}), {});
144 auto seed1 = xla::Reshape(xla::Slice(seed, {1}, {2}, {1}), {});
|
| pack_op.cc | 78 // Reshape the inputs to have an extra dimension of size 1.
79 reshaped_inputs[i] = xla::Reshape(values[i], child_shape.dim_sizes());
|
| unpack_op.cc | 78 // Reshape to drop the 'axis' dimension.
79 auto result = xla::Reshape(slice, output_shape.dim_sizes());
|
| depthtospace_op.cc | 131 // 1. Reshape `input` to `reshaped` of shape:
146 xla::XlaOp reshaped = xla::Reshape(input, reshaped_shape);
159 // 3. Reshape `permuted_reshaped` to flatten `block_shape` into the
167 xla::XlaOp output = xla::Reshape(permuted_reshaped, output_shape);
|
| reshape_op.cc | 16 // XLA-specific reshape Op.
74 errors::InvalidArgument("Reshape cannot infer the missing input size "
81 "Input to reshape is a tensor with ", input_shape.num_elements(),
87 errors::InvalidArgument("Input to reshape is a tensor with ",
92 VLOG(1) << "Reshape " << input_shape.DebugString() << " "
95 ctx->SetOutput(0, xla::Reshape(ctx->Input(0), shape.dim_sizes()));
99 REGISTER_XLA_OP(Name("Reshape").CompileTimeConstantInput("shape"), ReshapeOp);
|
| stateful_random_ops.cc | 65 return std::make_pair(Reshape(result, xla::AsInt64Slice(shape.dimensions())),
77 return std::make_pair(Reshape(result, xla::AsInt64Slice(shape.dimensions())),
174 [](xla::XlaOp x) { return xla::Reshape(x, {1}); }, scalars),
218 xla::Reshape(xla::Slice(var, {0}, {COUNTER_SIZE}, {1}), {}), xla::U64);
220 xla::Reshape(xla::Slice(var, {COUNTER_SIZE}, {COUNTER_SIZE + 1}, {1}),
|
| /external/tensorflow/tensorflow/core/grappler/optimizers/data/vectorization/ |
| reshape_vectorizer.cc | 29 const char* const kReshapePrefix = "vectorized/reshape";
61 ops::Reshape(s, tensor, GetVectorizedShape(&s, tensor, shape));
71 REGISTER_VECTORIZER("Reshape", ReshapeVectorizer);
|
| /external/tensorflow/tensorflow/examples/saved_model/integration_tests/ |
| use_model_in_sequential_keras.py | 44 model.add(l.Reshape((), batch_input_shape=[None, 1], dtype=tf.string))
|
| /external/tensorflow/tensorflow/compiler/xla/client/lib/ |
| qr.cc | 89 XlaOp alpha = Reshape(DynamicSliceInMinorDims(x, {k}, {1}), batch_dims);
191 auto v_broadcast = Reshape(v, shape);
203 auto iota = Reshape(Iota(a.builder(), S32, m), {m, 1});
214 vs, Reshape(v, ConcatVectors(batch_dims, {m, 1})), {j});
217 taus, Reshape(tau, ConcatVectors(batch_dims, {1})), {j});
|
