/external/tensorflow/tensorflow/python/keras/utils/ |
D | conv_utils_test.py | 168 kernel_shape = input_shape 177 kernel_shape, 185 kernel_shape = (1,) * ndims 195 kernel_shape, 204 kernel_shape = (1,) * ndims 216 kernel_shape, 225 kernel_shape = (1,) * ndims 239 kernel_shape, 251 kernel_shape = [1] * ndims 252 kernel_shape[d] = input_shape[d] [all …]
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D | conv_utils.py | 214 def conv_kernel_mask(input_shape, kernel_shape, strides, padding): argument 266 if isinstance(kernel_shape, int): 267 kernel_shape = (kernel_shape,) * in_dims 271 kernel_dims = len(kernel_shape) 278 output_shape = conv_output_shape(input_shape, kernel_shape, strides, padding) 285 input_axes_ticks = conv_connected_inputs(input_shape, kernel_shape, 293 def conv_kernel_idxs(input_shape, kernel_shape, strides, padding, filters_in, argument 348 if isinstance(kernel_shape, int): 349 kernel_shape = (kernel_shape,) * in_dims 353 kernel_dims = len(kernel_shape) [all …]
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/external/tensorflow/tensorflow/python/kernel_tests/ |
D | morphological_ops_test.py | 185 def _ConstructAndTestGradient(self, image_shape, kernel_shape, strides, rates, argument 197 assert image_shape[3] == kernel_shape[2] 201 kernel = np.random.random_sample(kernel_shape).astype(np.float32) 208 kernel, shape=kernel_shape, name="filter") 236 kernel_shape=[1, 1, 1], 245 kernel_shape=[1, 1, 1], 254 kernel_shape=[1, 1, 2], 263 kernel_shape=[2, 2, 1], 272 kernel_shape=[2, 2, 1], 281 kernel_shape=[2, 2, 1], [all …]
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/external/tensorflow/tensorflow/compiler/xla/service/cpu/ |
D | ir_emission_utils.cc | 54 const Shape& kernel_shape = convolution.operand(1)->shape(); in PotentiallyImplementedAsEigenConvolution() local 62 if (!is_aligned(input_shape) || !is_aligned(kernel_shape) || in PotentiallyImplementedAsEigenConvolution() 68 ShapeUtil::IsZeroElementArray(kernel_shape)) { in PotentiallyImplementedAsEigenConvolution() 73 ShapeUtil::SameElementTypeIgnoringFpPrecision(input_shape, kernel_shape)); in PotentiallyImplementedAsEigenConvolution() 110 kernel_shape.dimensions_size() - 2 && in PotentiallyImplementedAsEigenConvolution() 112 kernel_shape.dimensions_size() - 1; in PotentiallyImplementedAsEigenConvolution()
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D | ir_emitter.cc | 913 const Shape& kernel_shape = convolution->operand(1)->shape(); in HandleConvolution() local 915 kernel_shape.dimensions(dnums.kernel_spatial_dimensions(0)); in HandleConvolution() 919 : kernel_shape.dimensions(dnums.kernel_spatial_dimensions(1)); in HandleConvolution() 921 kernel_shape.dimensions(dnums.kernel_input_feature_dimension()); in HandleConvolution() 923 kernel_shape.dimensions(dnums.kernel_output_feature_dimension()); in HandleConvolution()
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/external/tensorflow/tensorflow/python/keras/layers/ |
D | local.py | 175 self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim, 179 shape=self.kernel_shape, 187 self.kernel_shape = (input_dim, input_length, self.filters, 190 self.kernel_shape = (input_length, input_dim, self.output_length, 194 shape=self.kernel_shape, 202 kernel_shape=self.kernel_size, 209 self.kernel_shape = (self.output_length * self.filters, 215 kernel_shape=self.kernel_size, 275 self.kernel_shape, 481 self.kernel_shape = (output_row * output_col, self.kernel_size[0] * [all …]
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D | convolutional_recurrent.py | 282 shape = list(self.cell.kernel_shape) 553 kernel_shape = self.kernel_size + (input_dim, self.filters * 4) 554 self.kernel_shape = kernel_shape 557 self.kernel = self.add_weight(shape=kernel_shape,
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D | einsum_dense.py | 146 kernel_shape, bias_shape, self.full_output_shape = shape_data 149 shape=kernel_shape,
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D | convolutional.py | 196 kernel_shape = self.kernel_size + (input_channel // self.groups, 201 shape=kernel_shape, 975 kernel_shape = self.kernel_size + (self.filters, input_dim) 979 shape=kernel_shape, 1246 kernel_shape = self.kernel_size + (self.filters, input_dim) 1250 shape=kernel_shape, 1555 kernel_shape = self.kernel_size + (self.filters, input_dim) 1560 shape=kernel_shape,
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/external/tensorflow/tensorflow/python/profiler/internal/ |
D | flops_registry.py | 319 kernel_shape = list(node.attr["ksize"].list.i) 320 kernel_area = _list_product(kernel_shape) 344 kernel_shape = list(node.attr["ksize"].list.i) 345 kernel_area = _list_product(kernel_shape) 372 kernel_shape = list(node.attr["ksize"].list.i) 373 kernel_area = _list_product(kernel_shape) 401 kernel_shape = graph_util.tensor_shape_from_node_def_name(graph, 403 kernel_shape.assert_is_fully_defined() 409 * kernel_shape.num_elements() 425 kernel_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) [all …]
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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
D | extract_image_patches_op.cc | 109 std::vector<int64> kernel_shape(num_dims, 1); in Compile() local 112 kernel_shape[i] = ksizes_[input_dim]; in Compile() 115 kernel_shape[num_spatial_dims] = 1; in Compile() 116 kernel_shape[num_spatial_dims + 1] = kernel_size * depth; in Compile() 124 kernel_shape); in Compile()
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/external/tensorflow/tensorflow/python/keras/ |
D | constraints.py | 242 kernel_shape = K.shape(kernel)[0] 243 start = K.cast(kernel_shape / 2, 'int32') 246 K.cast(math_ops.floormod(kernel_shape, 2), 'bool'), 251 K.cast(math_ops.floormod(kernel_shape, 2), 'bool'),
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D | backend.py | 5355 kernel_shape = kernel.shape.as_list() 5358 left_pad = dilation_rate * (kernel_shape[0] - 1) 5935 kernel_shape = int_shape(kernel) 5936 feature_dim = kernel_shape[1] 5937 channels_out = kernel_shape[-1]
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D | backend_test.py | 942 kernel_shape = (np.prod(output_shape), 949 kernel_cf = np.reshape(kernel, kernel_shape) 962 kernel_shape)
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/external/XNNPACK/bench/ |
D | convolution.cc | 875 arm_compute::TensorShape kernel_shape( in armcl_convolution_f32() local 881 kernel_shape, in armcl_convolution_f32() 924 reinterpret_cast<float*>(kernelTensor.buffer()) + kernel_shape.total_size(), in armcl_convolution_f32() 1023 kernel_shape.total_size(), in armcl_convolution_f32()
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/external/tensorflow/tensorflow/compiler/tests/ |
D | randomized_tests.cc | 1632 Tensor kernel_shape = test::AsTensor<int32>(AsInt32s( in TEST_F() local 1638 .Input(kernel_shape) in TEST_F() 1706 Tensor kernel_shape = test::AsTensor<int32>( in TEST_F() local 1713 .Input(kernel_shape) in TEST_F() 1811 Tensor kernel_shape = test::AsTensor<int32>(AsInt32s( in TEST_F() local 1818 .Input(kernel_shape) in TEST_F()
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/external/tensorflow/tensorflow/compiler/xrt/tests/ |
D | raw_api_test.cc | 1504 auto kernel_shape = xla::ShapeUtil::MakeShape(xla::BF16, {3, 3, 5, 5}); in TEST() local 1508 xla::LayoutUtil::ClearLayout(&kernel_shape); in TEST() 1510 xla::ShapeUtil::MakeTupleShape({input_shape, kernel_shape}); in TEST()
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/external/tensorflow/tensorflow/compiler/xla/service/ |
D | space_to_batch_converter.cc | 2596 const auto& kernel_shape = kernel->shape(); in GetConvolutionDetails() local 2598 kernel_shape.dimensions(dim_numbers.kernel_spatial_dimensions( in GetConvolutionDetails()
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