/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 | 230 def conv_kernel_mask(input_shape, kernel_shape, strides, padding): argument 279 if isinstance(kernel_shape, int): 280 kernel_shape = (kernel_shape,) * in_dims 284 kernel_dims = len(kernel_shape) 291 output_shape = conv_output_shape(input_shape, kernel_shape, strides, padding) 299 kernel_shape, 310 kernel_shape, argument 352 left_shift = int(kernel_shape[d] / 2) 353 right_shift = kernel_shape[d] - left_shift 368 def conv_output_shape(input_shape, kernel_shape, strides, padding): argument [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) 203 kernel_init = np.random.random_sample(kernel_shape).astype(np.float32) 212 kernel, shape=kernel_shape, name="filter") 224 [image_tensor, kernel_tensor], [image_shape, kernel_shape], 235 kernel_shape=[1, 1, 1], 244 kernel_shape=[1, 1, 1], 253 kernel_shape=[1, 1, 2], 262 kernel_shape=[2, 2, 1], [all …]
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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/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 | 1136 const Shape& kernel_shape = convolution->operand(1)->shape(); in HandleConvolution() local 1138 kernel_shape.dimensions(dnums.kernel_spatial_dimensions(0)); in HandleConvolution() 1142 : kernel_shape.dimensions(dnums.kernel_spatial_dimensions(1)); in HandleConvolution() 1144 kernel_shape.dimensions(dnums.kernel_input_feature_dimension()); in HandleConvolution() 1146 kernel_shape.dimensions(dnums.kernel_output_feature_dimension()); in HandleConvolution()
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/external/tensorflow/tensorflow/python/keras/layers/ |
D | local.py | 172 self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim, 176 shape=self.kernel_shape, 184 self.kernel_shape = (input_dim, input_length, 187 self.kernel_shape = (input_length, input_dim, 190 self.kernel = self.add_weight(shape=self.kernel_shape, 198 kernel_shape=self.kernel_size, 454 self.kernel_shape = ( 460 shape=self.kernel_shape, 468 self.kernel_shape = (input_filter, input_row, input_col, 471 self.kernel_shape = (input_row, input_col, input_filter, [all …]
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D | convolutional_recurrent.py | 283 shape = list(self.cell.kernel_shape) 611 kernel_shape = self.kernel_size + (input_dim, self.filters * 4) 612 self.kernel_shape = kernel_shape 615 self.kernel = self.add_weight(shape=kernel_shape,
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D | convolutional.py | 156 kernel_shape = self.kernel_size + (input_dim, self.filters) 160 shape=kernel_shape, 755 kernel_shape = self.kernel_size + (self.filters, input_dim) 759 shape=kernel_shape, 1027 kernel_shape = self.kernel_size + (self.filters, input_dim) 1032 shape=kernel_shape,
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/external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
D | core_layers.py | 132 kernel_shape = self.kernel_size + (input_dim, self.filters) 135 shape=kernel_shape, 142 shape=kernel_shape,
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/external/tensorflow/tensorflow/python/keras/ |
D | backend.py | 4233 kernel_shape = kernel.shape.as_list() 4236 left_pad = dilation_rate * (kernel_shape[0] - 1) 4806 kernel_shape = int_shape(kernel) 4807 feature_dim = kernel_shape[1] 4808 channels_out = kernel_shape[-1]
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D | backend_test.py | 804 kernel_shape = (np.prod(output_shape), 814 kernel_cf = np.reshape(kernel, kernel_shape) 830 kernel_shape
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/external/tensorflow/tensorflow/compiler/tests/ |
D | randomized_tests.cc | 1631 Tensor kernel_shape = test::AsTensor<int32>(AsInt32s( in TEST_F() local 1637 .Input(kernel_shape) in TEST_F() 1705 Tensor kernel_shape = test::AsTensor<int32>( in TEST_F() local 1712 .Input(kernel_shape) in TEST_F() 1810 Tensor kernel_shape = test::AsTensor<int32>(AsInt32s( in TEST_F() local 1817 .Input(kernel_shape) in TEST_F()
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/external/tensorflow/tensorflow/compiler/xrt/tests/ |
D | raw_api_test.cc | 800 auto kernel_shape = xla::ShapeUtil::MakeShape(xla::BF16, {3, 3, 5, 5}); in TEST() local 804 xla::LayoutUtil::ClearLayout(&kernel_shape); in TEST() 806 xla::ShapeUtil::MakeTupleShape({input_shape, kernel_shape}); in TEST()
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/external/tensorflow/tensorflow/contrib/rnn/python/kernel_tests/ |
D | rnn_cell_test.py | 1220 kernel_shape=filter_size, 1257 kernel_shape=filter_size, 1304 kernel_shape=filter_size,
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/external/tensorflow/tensorflow/contrib/rnn/python/ops/ |
D | rnn_cell.py | 2075 kernel_shape, argument 2108 self._kernel_shape = list(kernel_shape)
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