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Searched refs:input_dim (Results 1 – 25 of 33) sorted by relevance

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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/
Dextract_image_patches_op.cc78 int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); in Compile() local
80 ctx, ksizes_[input_dim] >= 0, in Compile()
83 dilations_[input_dim])); in Compile()
84 OP_REQUIRES(ctx, strides_[input_dim] >= 1, in Compile()
87 dilations_[input_dim])); in Compile()
88 OP_REQUIRES(ctx, dilations_[input_dim] >= 1, in Compile()
91 dilations_[input_dim])); in Compile()
111 int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); in Compile() local
112 kernel_shape[i] = ksizes_[input_dim]; in Compile()
113 kernel_size *= ksizes_[input_dim]; in Compile()
Dconv_op_helpers.cc124 int input_dim = GetTensorSpatialDimIndex(num_dims, attrs.data_format, i); in CheckConvAttrs() local
125 if (attrs.dilations[input_dim] < 1) { in CheckConvAttrs()
128 attrs.dilations[input_dim]); in CheckConvAttrs()
/external/tensorflow/tensorflow/python/keras/layers/
Dembeddings.py107 input_dim, argument
121 if input_dim <= 0 or output_dim <= 0:
124 input_dim, output_dim))
137 self.input_dim = input_dim
150 shape=(self.input_dim, self.output_dim),
200 'input_dim': self.input_dim,
Dconvolutional.py976 input_dim = int(input_shape[channel_axis])
977 self.input_spec = InputSpec(ndim=3, axes={channel_axis: input_dim})
978 kernel_shape = self.kernel_size + (self.filters, input_dim)
1247 input_dim = int(input_shape[channel_axis])
1248 self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
1249 kernel_shape = self.kernel_size + (self.filters, input_dim)
1557 input_dim = int(input_shape[channel_axis])
1558 kernel_shape = self.kernel_size + (self.filters, input_dim)
1559 self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})
1821 input_dim = int(input_shape[channel_axis])
[all …]
Dcudnn_recurrent.py243 input_dim = int(input_shape[-1])
246 shape=(input_dim, self.units * 3),
429 input_dim = int(input_shape[-1])
432 shape=(input_dim, self.units * 4),
Dconvolutional_recurrent.py549 input_dim = input_shape[channel_axis]
550 kernel_shape = self.kernel_size + (input_dim, self.filters * 4)
Drecurrent.py1799 input_dim = input_shape[-1]
1802 shape=(input_dim, self.units * 3),
2362 input_dim = input_shape[-1]
2364 shape=(input_dim, self.units * 4),
/external/eigen/unsupported/Eigen/CXX11/src/Tensor/
DTensorConvolutionSycl.h328 const Index input_dim = input_dims[index];
330 const Index result_dim = input_dim - kernel_dim + 1;
394 const auto input_dim = std::array<size_t, 2>{numX, numP};
399 m_device.parallel_for_setup(input_dim, global_range, local_range);
412 indexMapper, kernel_size, cl::sycl::range<2>(input_dim[0], input_dim[1]));
424 auto input_dim = std::array<size_t, 3>{numX, numY, numP};
429 m_device.parallel_for_setup(input_dim, global_range, local_range);
443 indexMapper, kernel_size, cl::sycl::range<3>{input_dim[0], input_dim[1], input_dim[2]});
459 auto input_dim = std::array<size_t, 3>{numX, numY, numZ};
473 m_device.parallel_for_setup(input_dim, global_range, local_range);
[all …]
DTensorDeviceSycl.h546 const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range, in parallel_for_setup() argument
548 std::array<Index, 2> input_range = input_dim; in parallel_for_setup()
558 input_range[1] = input_dim[1]; in parallel_for_setup()
569 input_range[0] = input_dim[0]; in parallel_for_setup()
585 const std::array<Index, 3> &input_dim, cl::sycl::range<3> &global_range, in parallel_for_setup() argument
587 std::array<Index, 3> input_range = input_dim; in parallel_for_setup()
597 input_range[2] = input_dim[2]; in parallel_for_setup()
611 input_range[1] = input_dim[1]; in parallel_for_setup()
623 input_range[0] = input_dim[0]; in parallel_for_setup()
861 const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range, in parallel_for_setup()
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DTensorConvolution.h33 const Index input_dim = input_dims[index]; in IndexMapper() local
35 const Index result_dim = input_dim - kernel_dim + 1; in IndexMapper()
347 const Index input_dim = input_dims[index];
349 const Index result_dim = input_dim - kernel_dim + 1;
366 const Index input_dim = input_dims[index];
368 const Index result_dim = input_dim - kernel_dim + 1;
811 const Index input_dim = input_dims[index];
813 const Index result_dim = input_dim - kernel_dim + 1;
/external/XNNPACK/src/operators/
Dconstant-pad-nd.c147 const size_t input_dim = input_shape[num_dims - 1 - i]; in setup_constant_pad_nd() local
152 normalized_input_shape[XNN_MAX_TENSOR_DIMS - 1 - num_squeezed_dims] = input_dim; in setup_constant_pad_nd()
153 …output_shape[XNN_MAX_TENSOR_DIMS - 1 - num_squeezed_dims] = pre_padding + input_dim + post_padding; in setup_constant_pad_nd()
163 normalized_input_shape[XNN_MAX_TENSOR_DIMS - num_squeezed_dims] *= input_dim; in setup_constant_pad_nd()
164 normalized_output_shape[XNN_MAX_TENSOR_DIMS - num_squeezed_dims] *= input_dim; in setup_constant_pad_nd()
/external/tensorflow/tensorflow/lite/kernels/internal/
Dbatch_to_space_nd_test.cc25 int input_dim, int output_dim) { in GetIndexRange() argument
28 optimized_ops::GetIndexRange(spatial_index_dim, block_shape_dim, input_dim, in GetIndexRange()
/external/tensorflow/tensorflow/compiler/xla/service/
Ddynamic_padder.cc316 HloInstruction* reshape, int64_t input_dim, in GenerateBinaryMask() argument
325 ShapeUtil::MakeShape(xla::S32, {input_shape.dimensions(input_dim)}); in GenerateBinaryMask()
327 ShapeUtil::MakeShape(xla::PRED, {input_shape.dimensions(input_dim)}); in GenerateBinaryMask()
469 HloInstruction* reshape, int64_t input_dim, in RewriteDynamicReshapeSplitInput() argument
473 VLOG(2) << "Reshaping input dim " << input_dim << " to " in RewriteDynamicReshapeSplitInput()
479 ShapeUtil::MakeShape(xla::S32, {operand_shape.dimensions(input_dim)}); in RewriteDynamicReshapeSplitInput()
481 ShapeUtil::MakeShape(xla::PRED, {operand_shape.dimensions(input_dim)}); in RewriteDynamicReshapeSplitInput()
490 GenerateBinaryMask(reshape, input_dim, output_dims, output_dynamic_dims, in RewriteDynamicReshapeSplitInput()
515 dim->set_size(operand_shape.dimensions(input_dim)); in RewriteDynamicReshapeSplitInput()
517 dim->set_padding_low(operand_shape.dimensions(input_dim) - 1); in RewriteDynamicReshapeSplitInput()
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Dshape_inference.cc3115 for (int64_t input_dim = 0; input_dim < operand.rank(); ++input_dim) { in InferReshapeShape() local
3116 if (!operand.is_dynamic_dimension(input_dim)) { in InferReshapeShape()
3124 input_dim); in InferReshapeShape()
3134 if (input_dim >= start.first && input_dim < end.first) { in InferReshapeShape()
3186 if (operand.dimensions(input_dim) == 1 && !new_sizes.empty()) { in InferReshapeShape()
3189 if (input_dim == 0) { in InferReshapeShape()
3192 if (input_dim == operand.rank() - 1) { in InferReshapeShape()
/external/tensorflow/tensorflow/lite/delegates/xnnpack/
Dreshape_tester.h35 for (int32_t input_dim : input_shape) { in InputShape() local
36 EXPECT_GT(input_dim, 0); in InputShape()
/external/tensorflow/tensorflow/python/keras/
Dtesting_utils.py422 def get_small_sequential_mlp(num_hidden, num_classes, input_dim=None): argument
424 if input_dim:
425 model.add(layers.Dense(num_hidden, activation='relu', input_dim=input_dim))
433 def get_small_functional_mlp(num_hidden, num_classes, input_dim): argument
434 inputs = layers.Input(shape=(input_dim,))
499 def get_small_mlp(num_hidden, num_classes, input_dim): argument
507 return get_small_sequential_mlp(num_hidden, num_classes, input_dim)
509 return get_small_functional_mlp(num_hidden, num_classes, input_dim)
/external/tensorflow/tensorflow/python/kernel_tests/array_ops/
Dbroadcast_to_ops_test.py53 for input_dim in range(1, 6):
54 for output_dim in range(input_dim, 6):
56 input_shape = [2] * input_dim
/external/XNNPACK/test/
Dconstant-pad-operator-tester.h34 inline size_t input_dim(size_t i) const { in input_dim() function
84 return pre_padding(i) + input_dim(i) + post_padding(i); in output_dim()
123 input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i); in TestX8()
238 input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i); in TestX16()
353 input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i); in TestX32()
/external/tensorflow/tensorflow/python/debug/examples/v2/
Ddebug_mnist_v2.py160 def get_dense_weights(input_dim, output_dim): argument
164 kernel = tf.Variable(initial_kernel([input_dim, output_dim]))
/external/tensorflow/tensorflow/python/debug/examples/v1/
Ddebug_mnist_v1.py158 def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu): argument
164 weights = weight_variable([input_dim, output_dim])
/external/tensorflow/tensorflow/core/kernels/linalg/
Deinsum_op_impl.h83 const int64_t input_dim = input.dim_size(axis); in RecordLabelToDimension() local
86 label_to_dim_sizes->at(label) != input_dim) { in RecordLabelToDimension()
90 " but got dimension ", input_dim); in RecordLabelToDimension()
92 (*label_to_dim_sizes)[label] = input_dim; in RecordLabelToDimension()
/external/tensorflow/tensorflow/compiler/xla/
Dshape_util.cc1471 for (int64_t input_dim = 0; input_dim < input_shape.rank(); ++input_dim) { in ReshapeIsBitcast() local
1472 if (input_shape.dimensions(input_dim) <= 1) { in ReshapeIsBitcast()
1477 input_unit_index[input_dim] = 1; in ReshapeIsBitcast()
1567 const int64_t input_dim = input_shape.layout().minor_to_major(input_minor); in AlignLayouts() local
1568 const int64_t common_factor = input_to_factor[input_dim]; in AlignLayouts()
/external/tensorflow/tensorflow/python/ops/signal/
Dfft_ops.py86 paddings = [[0, max(fft_dim.value - input_dim.value, 0)]
87 for fft_dim, input_dim in zip(
/external/tensorflow/tensorflow/python/kernel_tests/nn_ops/
Ddepthwise_conv_op_base.py112 def PaddingsForDim(input_dim, filter_dim, stride): argument
114 if input_dim % stride == 0:
117 total_padding = max(filter_dim - (input_dim % stride), 0)
/external/tensorflow/tensorflow/compiler/mlir/tensorflow/ir/
Dtf_ops_a_m.cc411 int64_t input_dim = input_shape[spatial_dim_index]; in verify() local
413 if (!static_dims(input_dim, output_dim)) return success(); in verify()
415 int64_t input_dim_pad = input_dim * block_size; in verify()
418 if (crops_values.empty() && output_dim > input_dim * block_size) in verify()
424 << output_dim << ", input " << dim_name << " " << input_dim in verify()
438 << " " << input_dim << ", " << crop_a_name << " " << crop_a in verify()

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