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docs/metrics/prometheus/grafana/node/minio-node.json
"editable": true, "fiscalYearStartMonth": 0, "gnetId": 15306, "graphTooltip": 0, "id": 267, "links": [ { "icon": "external link", "includeVars": true, "keepTime": true, "tags": [ "minio" ], "type": "dashboards" } ], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus",
Registered: Sun Nov 03 19:28:11 UTC 2024 - Last Modified: Tue Jun 04 13:24:37 UTC 2024 - 22.4K bytes - Viewed (0) -
docs/metrics/prometheus/grafana/minio-dashboard.json
"editable": true, "fiscalYearStartMonth": 0, "gnetId": 13502, "graphTooltip": 0, "id": 292, "links": [ { "icon": "external link", "includeVars": true, "keepTime": true, "tags": [ "minio" ], "type": "dashboards" } ], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus",
Registered: Sun Nov 03 19:28:11 UTC 2024 - Last Modified: Wed Jul 24 14:51:03 UTC 2024 - 93K bytes - Viewed (0) -
docs/metrics/prometheus/grafana/replication/minio-replication-node.json
"editable": true, "fiscalYearStartMonth": 0, "gnetId": 15306, "graphTooltip": 0, "id": 283, "links": [ { "icon": "external link", "includeVars": true, "keepTime": true, "tags": [ "minio" ], "type": "dashboards" } ], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus",
Registered: Sun Nov 03 19:28:11 UTC 2024 - Last Modified: Thu Jun 13 22:26:54 UTC 2024 - 57.4K bytes - Viewed (0) -
docs/metrics/prometheus/grafana/bucket/minio-bucket.json
"editable": true, "fiscalYearStartMonth": 0, "gnetId": 15306, "graphTooltip": 0, "id": 296, "links": [ { "icon": "external link", "includeVars": true, "keepTime": true, "tags": [ "minio" ], "type": "dashboards" } ], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus",
Registered: Sun Nov 03 19:28:11 UTC 2024 - Last Modified: Fri May 24 11:11:51 UTC 2024 - 101.8K bytes - Viewed (0) -
src/main/webapp/js/admin/moment-with-locales.min.js.map
units","normalizeObjectUnits","inputObject","normalizedProp","normalizedInput","priorities","addUnitPriority","priority","isLeapYear","year","absFloor","ceil","floor","toInt","argumentForCoercion","coercedNumber","value","isFinite","makeGetSet","keepTime","set$1","get","month","date","daysInMonth","regexes","match1","match2","match3","match4","match6","match1to2","match3to4","match5to6","match1to3","match1to4","match1to6","matchUnsigned","matchSigned","matchOffset","matchShortOffset","matchWord"...
Registered: Thu Oct 31 13:40:30 UTC 2024 - Last Modified: Sat Oct 26 01:49:09 UTC 2024 - 224.8K bytes - Viewed (0) -
RELEASE.md
mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True) return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var + 1e-10))` Alternatively, you can override `convolution_op`: `python class StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs, kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2], keepdims=True) # Author code uses std + 1e-5 return
Registered: Tue Nov 05 12:39:12 UTC 2024 - Last Modified: Tue Oct 22 14:33:53 UTC 2024 - 735.3K bytes - Viewed (0)