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tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-prefer-tf2xla.mlir
// CHECK: %[[v7:.*]] = mhlo.convolution(%[[v5]], %[[v6]]) // CHECK-SAME{LITERAL}: dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f] // CHECK-SAME{LITERAL}: window = {stride = [1, 1], pad = [[1, 1], [1, 1]], lhs_dilate = [1, 1], rhs_dilate = [1, 1], reverse = [0, 0]} // CHECK-SAME: batch_group_count = 1 // CHECK-SAME: feature_group_count = 1 // CHECK-NEXT: %[[v8:.*]] = mhlo.convert %7 : tensor<1x300x300x40xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 15.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
func.return %conv : tensor<1x5x5x3xf32> // CHECK: %[[cst:.*]] = arith.constant dense<1.270000e+02> : tensor<3x3x3x3xf32> // CHECK: %[[q:.*]] = "tfl.quantize"(%[[cst]]) <{qtype = tensor<3x3x3x3x!quant.uniform<i8<-127:127>:f32:0 // CHECK-SAME: {1.000000e+00,1.000000e+00,1.000000e+00}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.4K bytes - Viewed (0) -
docs/en/docs/alternatives.md
And these same full-stack generators were the base of the [**FastAPI** Project Generators](project-generation.md){.internal-link target=_blank}. !!! info Flask-apispec was created by the same Marshmallow developers. !!! check "Inspired **FastAPI** to" Generate the OpenAPI schema automatically, from the same code that defines serialization and validation.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Thu Apr 18 19:53:19 UTC 2024 - 23.2K bytes - Viewed (0) -
docs/en/docs/python-types.md
=== "Python 3.10+" You can use the same builtin types as generics (with square brackets and types inside): * `list` * `tuple` * `set` * `dict` And the same as with Python 3.8, from the `typing` module: * `Union` * `Optional` (the same as with Python 3.8) * ...and others.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Fri May 31 02:38:05 UTC 2024 - 17.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir
// CHECK: %[[tconv:.*]] = "tfl.transpose_conv"(%arg1, %[[dq_w:.*]], %arg0, %[[b:.*]]) <{ // CHECK-NOT: asymmetric_quantize_inputs = true // CHECK-SAME: padding = "SAME" // CHECK: return %[[tconv:.*]] // PerTensor-DAG: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<1x32x42x128xf32> // PerTensor-DAG: %[[b:.*]]= arith.constant dense<0.000000e+00> : tensor<1x32x42x128xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_op_with_region.mlir
return %0 : tensor<2x3x1x3xf32> } } // ----- // Tests if reduce_window op following quantized same-scale op is quantized. module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1722 : i32}, tf_saved_model.semantics} { // CHECK-LABEL: main_00 // CHECK-SAME: %[[ARG0:.*]]: tensor<2x3x1x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 18.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/pipelines/process_nchw_tensor.mlir
// Transpose ops are inserted to the activation and output of // `stablehlo.convolution`. The weight constants is transposed. // CHECK-LABEL: nchw_conv_with_nonconst_bias_add // CHECK-SAME: %[[ARG_0:.+]]: tensor<1x2x5x5xf32> // CHECK-SAME: %[[ARG_1:.+]]: tensor<1x4x5x5xf32> func.func @nchw_conv_with_nonconst_bias_add(%arg0: tensor<1x2x5x5xf32>, %arg1: tensor<1x4x5x5xf32>) -> tensor<1x4x5x5xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 12.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_hashtable_ops_as_args.mlir
return %5 : tensor<*xi64> } // Check that HashTable op is lifted. // CHECK: func.func private @serving_default // CHECK-SAME: (%arg0: tensor<?x!tf_type.string>, %arg1: tensor<!tf_type.resource>) -> tensor<*xi64> // CHECK-SAME: tf.entry_function = {control_outputs = "", inputs = "input_vocabs:0,hash_table_1:0", outputs = "FakeQuantWithMinMaxArgs_2:0"} // CHECK: "tf.LookupTableSizeV2"(%arg1)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 15 05:41:44 UTC 2024 - 13.5K bytes - Viewed (0) -
docs/en/docs/tutorial/response-model.md
{!> ../../../docs_src/response_model/tutorial002.py!} ``` Now, whenever a browser is creating a user with a password, the API will return the same password in the response. In this case, it might not be a problem, because it's the same user sending the password. But if we use the same model for another *path operation*, we could be sending our user's passwords to every client. !!! danger
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Thu Apr 18 19:53:19 UTC 2024 - 17.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/shape-inference.mlir
%0 = "tfl.conv_2d"(%arg0, %arg1, %arg2) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>)...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 11.5K bytes - Viewed (0)