Searched refs:evaluation (Results 1 – 25 of 396) sorted by relevance
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130 from tensorflow.contrib.training.python.training import evaluation142 wait_for_new_checkpoint = evaluation.wait_for_new_checkpoint143 checkpoints_iterator = evaluation.checkpoints_iterator195 all_hooks = [evaluation.StopAfterNEvalsHook(num_evals),]198 all_hooks.append(evaluation.SummaryAtEndHook(207 return evaluation.evaluate_once(282 all_hooks = [evaluation.StopAfterNEvalsHook(num_evals),]285 all_hooks.append(evaluation.SummaryAtEndHook(296 return evaluation.evaluate_repeatedly(
29 from tensorflow.contrib.slim.python.slim import evaluation30 from tensorflow.contrib.training.python.training import evaluation as evaluation_lib124 accuracy_value = evaluation.evaluation_loop(188 evaluation.evaluation_loop('', '', '', timeout=0, timeout_fn=_TimeoutFn)219 accuracy_value = evaluation.evaluation_loop(245 evaluation.evaluate_once('', checkpoint_path, log_dir)267 accuracy_value = evaluation.evaluate_once(286 accuracy_value = evaluation.evaluate_once(
30 from tensorflow.contrib.training.python.training import evaluation58 for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0):76 for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0):105 for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0):116 evaluation.checkpoints_iterator(126 ret = evaluation.wait_for_new_checkpoint(202 checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)204 final_ops_values = evaluation.evaluate_once(209 evaluation.StopAfterNEvalsHook(1),218 checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)[all …]
146 from tensorflow.python.training import evaluation163 StopAfterNEvalsHook = evaluation._StopAfterNEvalsHook164 evaluate_once = evaluation._evaluate_once165 get_or_create_eval_step = evaluation._get_or_create_eval_step
7 ERROR: 0:81: 'preprocessor evaluation' : bad expression 11 ERROR: 0:88: 'preprocessor evaluation' : can't evaluate expression 12 ERROR: 0:88: 'preprocessor evaluation' : bad expression 15 ERROR: 0:93: 'preprocessor evaluation' : can't evaluate expression 16 ERROR: 0:93: 'preprocessor evaluation' : bad expression 19 ERROR: 0:100: 'preprocessor evaluation' : can't evaluate expression 20 ERROR: 0:100: 'preprocessor evaluation' : bad expression 23 ERROR: 0:102: 'preprocessor evaluation' : can't evaluate expression 24 ERROR: 0:102: 'preprocessor evaluation' : bad expression 26 ERROR: 0:108: 'preprocessor evaluation' : undefined macro in expression not allowed in es profile U…[all …]
8 ERROR: 0:100: 'preprocessor evaluation' : expected ')' 12 ERROR: 0:108: 'preprocessor evaluation' : expected ')' 16 ERROR: 0:116: 'preprocessor evaluation' : expected ')' 74 ERROR: 12:20051: '#error' : good evaluation 1 75 ERROR: 12:20055: '#error' : good evaluation 2 76 ERROR: 12:9000: 'preprocessor evaluation' : expected ')' 80 ERROR: 12:9015: 'preprocessor evaluation' : can't evaluate expression 81 ERROR: 12:9016: 'preprocessor evaluation' : bad expression 82 ERROR: 12:9500: 'preprocessor evaluation' : bad expression 84 ERROR: 12:9502: 'preprocessor evaluation' : bad expression [all …]
50 from tensorflow.contrib.training.python.training.evaluation import checkpoints_iterator51 from tensorflow.contrib.training.python.training.evaluation import evaluate_once52 from tensorflow.contrib.training.python.training.evaluation import evaluate_repeatedly53 from tensorflow.contrib.training.python.training.evaluation import get_or_create_eval_step54 from tensorflow.contrib.training.python.training.evaluation import StopAfterNEvalsHook55 from tensorflow.contrib.training.python.training.evaluation import SummaryAtEndHook56 from tensorflow.contrib.training.python.training.evaluation import wait_for_new_checkpoint
5 Executive summary: Eigen has intelligent compile-time mechanisms to enable lazy evaluation and remo…21 …evaluation</i> as an expression is getting evaluated as late as possible, instead of immediately. …23 …nt compile-time mechanisms to determine automatically when to use lazy evaluation, and when on the…29 …chooses lazy evaluation. Thus the arrays are traversed only once, producing optimized code. If you…37 Eigen chooses lazy evaluation at every stage in that example, which is clearly the correct choice. …39 <b>The first circumstance</b> in which Eigen chooses immediate evaluation, is when it sees an assig…43 … \c matrix. This guarantees a correct result as we saw above that lazy evaluation gives wrong resu…45 …result does no alias the operand of the product and want to force lazy evaluation? Then use \link …49 …the same matrix as matrix1, we know that lazy evaluation is not dangerous, so we may force lazy ev…51 <b>The second circumstance</b> in which Eigen chooses immediate evaluation, is when it sees a neste…[all …]
38 from tensorflow.python.training import evaluation125 final_ops_values = evaluation._evaluate_once(130 evaluation._StopAfterNEvalsHook(1),158 final_ops_values = evaluation._evaluate_once(163 'eval_steps': evaluation._get_or_create_eval_step()166 evaluation._StopAfterNEvalsHook(None),192 final_hooks = [evaluation._StopAfterNEvalsHook(num_evals),]194 final_ops_values = evaluation._evaluate_once(219 evaluation._get_or_create_eval_step(), 1, use_locking=True)]224 final_ops_values = evaluation._evaluate_once([all …]
38 "//tensorflow/lite/tools/evaluation/proto:evaluation_config_proto_cc",39 "//tensorflow/lite/tools/evaluation/proto:evaluation_stages_proto_cc",53 "//tensorflow/lite/tools/evaluation/proto:evaluation_config_proto_cc",54 "//tensorflow/lite/tools/evaluation/proto:evaluation_stages_proto_cc",67 "//tensorflow/lite/tools/evaluation/proto:evaluation_config_proto_cc",68 "//tensorflow/lite/tools/evaluation/proto:evaluation_stages_proto_cc",
META-INF/ META-INF/MANIFEST.MF proguard/ proguard/FileWordReader.class FileWordReader ...
85 evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)89 evaluation, steps=200)))90 times = evaluation["times"][0]91 observed = evaluation["observed"][0, :, 0]93 [evaluation["mean"][0], predictions["mean"]], axis=0))95 [evaluation["covariance"][0], predictions["covariance"]], axis=0))
116 evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)124 evaluation, steps=100,127 times = evaluation["times"][0]128 observed = evaluation["observed"][0, :, 0]130 [evaluation["mean"][0], predictions["mean"]], axis=0))132 [evaluation["covariance"][0], predictions["covariance"]], axis=0))
221 evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)231 evaluation, steps=100,233 times = evaluation["times"][0]234 observed = evaluation["observed"][0, :, :]236 [evaluation["mean"][0], predictions["mean"]], axis=0))
34 import com.intellij.debugger.engine.evaluation.EvaluateException;35 import com.intellij.debugger.engine.evaluation.EvaluationContext;36 import com.intellij.debugger.engine.evaluation.expression.ExpressionEvaluator;37 import com.intellij.debugger.engine.evaluation.expression.Modifier;
16 Vector4d evaluation; in main() local18 evaluation[i] = poly_eval( polynomial, roots[i] ); } in main()19 cout << "Evaluation of the polynomial at the roots: " << evaluation.transpose(); in main()
21 package proguard.optimize.evaluation;25 import proguard.evaluation.BasicBranchUnit;26 import proguard.evaluation.value.Value;
21 package proguard.optimize.evaluation;25 import proguard.evaluation.BasicInvocationUnit;26 import proguard.evaluation.value.*;
22 # Gcov evaluation is dependend on the used compiler. Check gcov support for46 # evaluation tools, but these versions are compatible with the gcc82 message("-- Found gcov evaluation for "103 # This function will add gcov evaluation for target <TNAME>. Only sources of129 message(WARNING "No coverage evaluation binary found for ${TCOMPILER}.")152 # add target for gcov evaluation of <TNAME>155 # add evaluation target to the global gcov target.
3 This library provides evaluation pipelines that can be used to evaluate8 The tool provides an evaluation pipeline with different stages. Each31 For further examples, check the usage in [imagenet accuracy evaluation binary](ilsvrc/imagenet_mode…
5 default "stm32f429-evaluation"17 default "stm32f429-evaluation"
4 F: board/st/stm32f429-evaluation/5 F: include/configs/stm32f429-evaluation.h
1 # TensorFlow evaluation metrics and summary statistics5 Metrics are used in evaluation to assess the quality of a model. Most are34 print "evaluation score: ", value.eval()
21 package proguard.evaluation;25 import proguard.evaluation.value.ValueFactory;
122 evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)124 evaluation, times=[[7, 8, 9]],146 evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)148 evaluation, times=[[7, 8, 9]])