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Results 1 - 6 of 6 for sgd (0.05 sec)
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tensorflow/compiler/jit/tests/keras_imagenet_main_graph_mode.pbtxt
} attr { key: "shape" value { shape { } } } attr { key: "shared_name" value { s: "training/SGD/iter" } } } node { name: "training/SGD/bn2a_branch1/beta/momentum" op: "VarHandleOp" device: "/job:localhost/replica:0/task:0/device:GPU:0" attr { key: "container" value { s: "" }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 30 02:52:54 UTC 2019 - 1.1M bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main.pbtxt
op: "ReadVariableOp" input: "training_lossscaleoptimizer_sgd_update_resourceapplykerasmomentum_readvariableop_1_resource" device: "/job:localhost/replica:0/task:0/device:GPU:0" attr { key: "dtype" value { type: DT_FLOAT } } } node {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 30 02:52:54 UTC 2019 - 1.3M bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow_to_stablehlo/python/integration_test/tensorflow_to_stablehlo_test.py
def call(self, x): return x + 1 model = AddOneModel() x_train = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32) y_train = tf.constant([2, 3, 4, 5, 6], dtype=tf.float32) model.compile(optimizer='sgd', loss='mse') model.fit(x_train, y_train, epochs=1) path = tempdir + '/add_one_model' model.save(path) return path class TensorflowToStableHLOTest(test.TestCase):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 22:58:42 UTC 2024 - 2.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td
); } def TF_XlaSparseDenseMatmulGradWithSgdAndStaticBufferSizeOp : TF_Op<"XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize", [Pure]> { let summary = "A XLA op which performs the SGD optimizer update for the dense-sparse matrix multiplication."; let arguments = (ins TF_Int32Tensor:$row_pointers, TF_Int32Tensor:$sorted_sample_ids, TF_Int32Tensor:$sorted_token_ids,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 90.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
let results = (outs); } def TF_LoadTPUEmbeddingStochasticGradientDescentParametersOp : TF_Op<"LoadTPUEmbeddingStochasticGradientDescentParameters", [TF_MustExecute, TF_TPUEmbeddingReadEffect]> { let summary = "Load SGD embedding parameters."; let description = [{ An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
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
* Added `tf.keras.optimizers.experimental.Optimizer`. The reworked optimizer gives more control over different phases of optimizer calls, and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and RMSprop optimizers based on `tf.keras.optimizers.experimental.Optimizer`. Generally the new optimizers work in the same way as the old ones, but support new
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0)