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RELEASE.md
* Keras: * `tf.keras.layers.Conv` now includes a public `convolution_op` method. This method can be used to simplify the implementation of Conv subclasses. There are two primary ways to use this new method. The first is to use the method directly in your own `call` method: `python class StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/add_quantization_unit_loc.mlir
%2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x3x2x2xbf16>) -> tensor<1x3x2x2xf32> %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32> return %3 : tensor<1x3x2x2xf32> // CHECK: tf.Conv2D // CHECK-SAME: loc(callsite("Model/conv2d@conv2d_with_valid_loc"("Conv2D") at "QuantizationUnit({{.*}})")) }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 03 02:39:10 UTC 2023 - 3.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_without_identity.pbtxt
key: "narrow_range" value { b: true } } attr { key: "num_bits" value { i: 8 } } } node { name: "BoxPredictor_4/ClassPredictor/Conv2D" op: "Conv2D" input: "input" input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel" attr { key: "T" value { type: DT_FLOAT } } attr {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_without_identity_4bit.pbtxt
key: "narrow_range" value { b: true } } attr { key: "num_bits" value { i: 4 } } } node { name: "BoxPredictor_4/ClassPredictor/Conv2D" op: "Conv2D" input: "input" input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel" attr { key: "T" value { type: DT_FLOAT } } attr {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_weights.mlir
// CHECK: %[[CONV_1:.*]] = "tf.Conv2D"(%[[GATHER]], %[[DEQUANTIZED_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true}> {device = ""} : (tensor<1x3x4x3xf32>, tensor<2x3x3x1024xf32>) -> tensor<1x3x2x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 42K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_lifting.mlir
// CHECK: %[[CONV2D:.*]] = "tf.Conv2D"(%arg0, %[[CONST]]) <{data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true}> : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 33.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_xla_selective_quantization.mlir
%1 = "tf.Conv2D"(%0, %cst) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32> loc(fused["Conv2D:", "Model/conv2d"]) %2 = "tf.IdentityN"(%1) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tpu_space_to_depth_pass.cc
} } // Handle Conv2D input, stride and filter. HandleConv2DInput(conv2d, block_size); HandleConv2DStride(conv2d); HandleConv2DFilter(conv2d, block_size); // Book keeping new filter shape for backprop filter rewrite. // Filter shape is defined in HandleConv2DFilter, thus it is RankedTensorType. filter_shape = mlir::cast<RankedTensorType>(conv2d.getFilter().getType()).getShape();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 29.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_to_nchw.mlir
// Check that Conv2D computed in NCHW format, and all redundant transpose // operations removed from the function. // CHECK: %[[CONV:[0-9]*]] = "tf.Conv2D"(%arg0, %arg1) // CHECK-SAME: data_format = "NCHW" // CHECK-SAME: -> tensor<1x8x32x32xf32> // CHECK: return %[[CONV]] func.return %4 : tensor<1x8x32x32xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:47:26 UTC 2022 - 1.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant-4bit.mlir
// CHECK: %[[DEQUANTIZE:.*]] = "tfl.dequantize"(%[[QUANTIZE]]) // CHECK: %[[CONV:.*]] = "tfl.conv_2d"(%arg0, %[[DEQUANTIZE]], %[[CONSTANT]]) // CHECK: return %[[CONV]] } // CHECK-LABEL: perChannelFakeQuantWithConv2D func.func @perChannelFakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 22K bytes - Viewed (0)