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Results 1 - 10 of 14 for reduce_max (0.17 sec)
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tensorflow/compiler/mlir/lite/stablehlo/tests/tfl_legalize_hlo.mlir
// CHECK: %3 = "mhlo.broadcast_in_dim"(%2) <{broadcast_dimensions = dense<2> : tensor<1xi64>}> : (tensor<256xi32>) -> tensor<4x32x256xi32> // CHECK: %cst = arith.constant dense<2> : tensor<1xi32> // CHECK: %4 = "tfl.reduce_max"(%arg0, %cst) <{keep_dims = false}> : (tensor<4x32x256xf32>, tensor<1xi32>) -> tensor<4x32xf32> // CHECK: %5 = "tfl.arg_max"(%arg0, %cst) : (tensor<4x32x256xf32>, tensor<1xi32>) -> tensor<4x32xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 40.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
%cst_1 = arith.constant dense<[1, 128]> : tensor<2xi32> %0 = "tfl.reduce_max"(%arg0, %cst) {keep_dims = false} : (tensor<8x128xf32>, tensor<1xi32>) -> tensor<128xf32> %1 = "tfl.reshape"(%0, %cst_1) : (tensor<128xf32>, tensor<2xi32>) -> tensor<1x128xf32> func.return %1 : tensor<1x128xf32> // CHECK-LABEL: FoldReduceMaxKeepDim
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
} func.func @reduce_min(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { %0 = "tf.Min"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> func.return %0 : tensor<?xf32> // CHECK-LABEL: reduce_min // CHECK: "tfl.reduce_min"(%arg0, %arg1) <{keep_dims = false}> : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_v3b.fbs
SUM = 74, SQRT = 75, RSQRT = 76, SHAPE = 77, POW = 78, ARG_MIN = 79, FAKE_QUANT = 80, REDUCE_PROD = 81, REDUCE_MAX = 82, PACK = 83, LOGICAL_OR = 84, ONE_HOT = 85, LOGICAL_AND = 86, LOGICAL_NOT = 87, UNPACK = 88, REDUCE_MIN = 89, FLOOR_DIV = 90, REDUCE_ANY = 91, SQUARE = 92, ZEROS_LIKE = 93, FILL = 94, FLOOR_MOD = 95, RANGE = 96,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 14:28:27 UTC 2024 - 30K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
SUM = 74, SQRT = 75, RSQRT = 76, SHAPE = 77, POW = 78, ARG_MIN = 79, FAKE_QUANT = 80, REDUCE_PROD = 81, REDUCE_MAX = 82, PACK = 83, LOGICAL_OR = 84, ONE_HOT = 85, LOGICAL_AND = 86, LOGICAL_NOT = 87, UNPACK = 88, REDUCE_MIN = 89, FLOOR_DIV = 90, REDUCE_ANY = 91, SQUARE = 92, ZEROS_LIKE = 93, FILL = 94, FLOOR_MOD = 95, RANGE = 96,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
let results = (outs TFL_TensorOf<[F32, I32, I64, QI8, QUI8, TFL_Quint8, QI16]>:$output); let hasOptions = 1; let customOption = "ReducerOptions"; } def TFL_ReduceMaxOp: TFL_Op<"reduce_max", [ PredOpTrait<"input and output must have same element type", TFL_TCresVTEtIsSameAsOp<0, 0>>, Pure, QuantizableResult, SameOperandsAndResultsScale]> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/mnist/mnist_train.py
with tf.GradientTape() as tape: logits = model(inputs) loss_value = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels, logits)) grads = tape.gradient(loss_value, model.trainable_variables) correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Oct 20 03:05:18 UTC 2021 - 6.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.h
using OpRewritePattern<TFL::MeanOp>::OpRewritePattern; LogicalResult matchAndRewrite(TFL::MeanOp mean_op, PatternRewriter& rewriter) const override; }; // Insert Requant ops for reduce_mean. struct InsertRequantForReduceMean : public OpRewritePattern<TFL::MeanOp> { using OpRewritePattern<TFL::MeanOp>::OpRewritePattern; LogicalResult matchAndRewrite(TFL::MeanOp mean_op,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 03 16:37:16 UTC 2022 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
values. ```python def stable_softmax(x): z = x - tf.reduce_max(x) numerator = tf.exp(z) denominator = tf.reduce_sum(numerator) return numerator / denominator ``` However, when we backprop through the softmax to x, we dont want to backprop through the `tf.reduce_max(x)` (if the max values are not unique then the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0) -
RELEASE.md
`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`: `reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices` becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` * `tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`: `reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0)