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Results 21 - 30 of 188 for conv3d (0.12 sec)
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tensorflow/compiler/mlir/lite/tests/dilated-conv.mlir
// CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x128x128x8xf32> // CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[CONV]], [[BIAS]]) : (tensor<1x128x128x8xf32>, tensor<8xf32>) -> tensor<1x128x128x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 44.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-with-allowing-bf16-and-f16-type-legalization.mlir
%0 = "tf.Conv2D"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xbf16>, tensor<3x3x3x16xbf16>) -> tensor<256x8x7x16xbf16> func.return %0 : tensor<256x8x7x16xbf16> // CHECK: "tfl.conv_2d" } // CHECK-LABEL: fused_batch_norm_v3_bf16
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 26 23:53:32 UTC 2022 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_functional_ops.mlir
// Verify unused if with functions without side-effects is removed. // CHECK-LABEL: main func.func @main(%arg0: tensor<3x15x14x3xf32>) -> tensor<3x15x14x8xf32> attributes {tf.entry_function = {inputs = "input", outputs = "Conv2D"}} { %cst = arith.constant dense<[0, 1, 2, 3]> : tensor<4xi32> %cst_0 = arith.constant dense<1.000000e+00> : tensor<f32> %cst_1 = arith.constant dense<0.000000e+00> : tensor<8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 30 10:34:48 UTC 2022 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel.pbtxt
key: "narrow_range" value { b: true } } attr { key: "num_bits" value { i: 8 } } } node { name: "BoxPredictor_4/ClassPredictor/Conv2D" op: "Conv2D" input: "input" input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel" attr { key: "T" value { type: DT_FLOAT } } attr {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
// Unsupported data format %1 = "tf.Conv2D"(%arg2, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NCHW", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x3x32x32xf32>, tensor<3x3x3x16xf32>) -> tensor<256x16x32x32xf32> // OK
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/decompose-hybrid-quantization.mlir
// CHECK: %[[VAL2:.+]] = "tfl.dequantize"(%[[VAL1]]) : (tensor<1x1x1x8x16x!quant.uniform<{{.+}}>>) -> tensor<1x1x1x8x16xf32> // CHECK: %[[VAL3:.+]] = "tfl.conv_3d"(%arg0, %[[VAL2]], %[[VAL0]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/tf-tfl-translate-serialize-stablehlo-conv.mlir
module { func.func @main(%arg0: tensor<4x68x68x3xf32>, %arg1: tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32> { %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32> func.return %0 : tensor<4x64x64x8xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Feb 27 23:35:37 UTC 2023 - 425 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel_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 - 18.1K bytes - Viewed (0) -
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)