Merge pull request #561 from Luis-Fernando-Molina/develop

Contribution: TensorFlow project implementation (part 1)

Client/httpInterface.js

@@ -1139,22 +1139,24 @@ exports.newHttpInterface = function newHttpInterface(WEB_SERVER, DATA_FILE_SERVE
                                 fs.readdir(dirPath, onDirRead)
 
                                 function onDirRead(err, fileList) {
+                                    let updatedFileList = []
+
                                     if (err) {
-                                        console.log('[WARN] Error reading a directory content. filePath = ' + dirPath)
-                                        respondWithContent(JSON.stringify(global.DEFAULT_FAIL_RESPONSE), httpResponse)
-                                        return
+                                        /*
+                                        If we have a problem reading this folder we will assume that it is
+                                        because this project does not need this folder and that's it.
+                                        */
+                                        //console.log('[WARN] Error reading a directory content. filePath = ' + dirPath)
                                     } else {
-                                        let updatedFileList = []
                                         for (let i = 0; i < fileList.length; i++) {
                                             let name = 'Plugin \u2192 ' + fileList[i]
                                             updatedFileList.push([project, name])
                                         }
-                                        allWorkspaces = allWorkspaces.concat(updatedFileList)
-                                        projectsCount++
-                                        if (projectsCount === projects.length) {
-                                            readMyWorkspaces()
-                                        }
-                                        return
+                                    }
+                                    allWorkspaces = allWorkspaces.concat(updatedFileList)
+                                    projectsCount++
+                                    if (projectsCount === projects.length) {
+                                        readMyWorkspaces()
                                     }
                                 }
                             } catch (err) {
@@ -1441,21 +1443,23 @@ exports.newHttpInterface = function newHttpInterface(WEB_SERVER, DATA_FILE_SERVE
                         fs.readdir(dirPath, onDirRead)
 
                         function onDirRead(err, fileList) {
+                            let updatedFileList = []
                             if (err) {
-                                console.log('[WARN] Error reading a directory content. filePath = ' + dirPath)
-                                return
+                                /*
+                                If we have a problem reading this folder we will assume that it is
+                                because this project does not need this folder and that's it.
+                                */
+                                // console.log('[WARN] Error reading a directory content. filePath = ' + dirPath)
                             } else {
-                                let updatedFileList = []
                                 for (let i = 0; i < fileList.length; i++) {
                                     let name = fileList[i]
                                     updatedFileList.push([project, name])
                                 }
-                                allLibraries = allLibraries.concat(updatedFileList)
-                                projectsCount++
-                                if (projectsCount === projects.length) {
-                                    respondWithContent(JSON.stringify(allLibraries), httpResponse)
-                                }
-                                return
+                            }
+                            allLibraries = allLibraries.concat(updatedFileList)
+                            projectsCount++
+                            if (projectsCount === projects.length) {
+                                respondWithContent(JSON.stringify(allLibraries), httpResponse)
                             }
                         }
                     } catch (err) {
@@ -1722,9 +1726,13 @@ exports.newHttpInterface = function newHttpInterface(WEB_SERVER, DATA_FILE_SERVE
     }
 
     function getDirectories(path) {
-        const fs = require('fs')
-        return fs.readdirSync(path).filter(function (file) {
-            return fs.statSync(path + '/' + file).isDirectory();
-        });
+        try {
+            const fs = require('fs')
+            return fs.readdirSync(path).filter(function (file) {
+                return fs.statSync(path + '/' + file).isDirectory();
+            });
+        } catch (err) {
+            return []
+        }
     }
 }

Projects/ProjectsSchema.json

@@ -914,5 +914,15 @@
         },
         "TS": {},
         "CL": {}
+    },
+    {
+        "name": "TensorFlow",
+        "propertyName": "tensorflow",
+        "UI": {
+            "globals": [],
+            "functionLibraries": [],
+            "utilities": [],
+            "spaces": []
+        }
     }
 ]

Projects/Superalgos/Plugins/Workspaces/Machine-Learning-Demo.json

@@ -1 +0,0 @@
-{"type":"Workspace","name":"Machine-Learning-Demo","rootNodes":[{"type":"Network","name":"My Network","project":"Superalgos","networkNodes":[{"type":"Network Node","name":"My Computer","config":"{ \n\"host\": \"localhost\", \n\"webPort\": \"34248\", \n\"webSocketsPort\": \"18041\"\n}","project":"Superalgos","dataTasks":{"type":"Data Tasks","name":"New Data Tasks","project":"Superalgos","projectDataTasks":[{"type":"Project Data Tasks","name":"Superalgos","project":"Superalgos","exchangeDataTasks":[{"type":"Exchange Data Tasks","name":"Binance","project":"Superalgos","marketDataTasks":[{"type":"Market Data Tasks","name":"Binance BTC/USDT","project":"Superalgos","dataMineTasks":[{"type":"Data Mine Tasks","name":"Masters","project":"Superalgos","taskManagers":[{"type":"Task Manager","name":"Masters","project":"Superalgos","tasks":[{"type":"Task","name":"Exchange Raw Data","project":"Superalgos","bot":{"type":"Sensor Bot Instance","name":"Exchange Raw Data","config":"{ \n\"startDate\": \"2020-01-01\"\n}","project":"Superalgos","processes":[{"type":"Sensor Process Instance","name":"Exchange Raw Data Historic-OHLCVs","project":"Superalgos","id":"31e87840-fc8a-4305-a97d-3a78b8e4be61","savedPayload":{"position":{"x":24977.356993272086,"y":30782.11115747996},"targetPosition":{"x":24995.356993272086,"y":30827.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process Definition","name":"Historic-OHLCVs","id":"5eb11d6a-022e-4f60-97c4-d6cf90b249dc"}}}],"id":"ddab2649-221f-40c4-8b8e-b3cbceca0c4b","savedPayload":{"position":{"x":24995.356993272086,"y":30827.11115747996},"targetPosition":{"x":24900.356993272086,"y":30922.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},"id":"1bca451f-f059-4dc2-87cb-ba2bb1c16ef4","savedPayload":{"position":{"x":24900.356993272086,"y":30922.11115747996},"targetPosition":{"x":24938.356993272086,"y":30991.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},{"type":"Task","name":"Webhooks","project":"Superalgos","bot":{"type":"Sensor Bot Instance","name":"Webhooks","config":"{ \n\"startDate\": \"2020-01-01\"\n}","project":"Superalgos","processes":[{"type":"Sensor Process Instance","name":"Webhooks Check-Webhook","project":"Superalgos","id":"4595558a-d0ba-49b1-9c81-67696f23261c","savedPayload":{"position":{"x":24958.356993272086,"y":30985.11115747996},"targetPosition":{"x":24998.356993272086,"y":30997.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process Definition","name":"Check-Webhook","id":"69f37820-ab16-4e19-bfd8-386dd6e249a3"}}}],"id":"a7978877-bdef-4e69-a97d-af0560ef219e","savedPayload":{"position":{"x":24998.356993272086,"y":30997.11115747996},"targetPosition":{"x":24980.356993272086,"y":31030.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},"id":"bf34c6df-d1ac-4f7c-8a6c-7ed9b6de2e00","savedPayload":{"position":{"x":24980.356993272086,"y":31030.11115747996},"targetPosition":{"x":24938.356993272086,"y":30991.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},{"type":"Task","name":"Candles Volumes","project":"Superalgos","bot":{"type":"Indicator Bot Instance","name":"Candles Volumes","project":"Superalgos","processes":[{"type":"Indicator Process Instance","name":"Candles Volumes Multi-Period-Market","project":"Superalgos","id":"7cdc33e0-c56b-43ce-95f5-db03a171f021","savedPayload":{"position":{"x":24917.356993272086,"y":30838.11115747996},"targetPosition":{"x":25001.356993272086,"y":30917.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process Definition","name":"Multi-Period-Market","id":"9f683143-a33f-42c1-9fa8-c601f01ee4e1"}}},{"type":"Indicator Process Instance","name":"Candles Volumes Multi-Period-Daily","project":"Superalgos","id":"4da5ecc7-48e6-4283-8a2f-512528a28fe4","savedPayload":{"position":{"x":25093.356993272086,"y":30990.11115747996},"targetPosition":{"x":25001.356993272086,"y":30917.11115747996},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process Definition","name":"MLine truncated

Projects/Superalgos/Schemas/AppSchema.json

@@ -12684,6 +12684,11 @@
                 "name": "trainingData",
                 "type": "array",
                 "childType": "Training Data"
+            },
+            {
+                "name": "machineLearningLibrary",
+                "type": "node",
+                "childType": "TensorFlow Library"
             }
         ]
     },

Projects/Superalgos/UI/Spaces/Docs-Space/DocumentPage.js

@@ -855,6 +855,9 @@ function newSuperalgosDocsDocumentPage() {
                     for (let i = 0; i < appSchemaDocument.menuItems.length; i++) {
                         let menuItem = appSchemaDocument.menuItems[i]
                         let collectionImage = getIcon()
+                        if (collectionImage === undefined) {
+                            continue
+                        }
                         let imageElement = collectionImage.cloneNode()
 
                         imageElement.className = "docs-collapsible-image"
@@ -882,6 +885,9 @@ function newSuperalgosDocsDocumentPage() {
                     for (let i = 0; i < appSchemaDocument.childrenNodesProperties.length; i++) {
                         let childrenNodesProperty = appSchemaDocument.childrenNodesProperties[i]
                         let collectionImage = getIcon()
+                        if (collectionImage === undefined) {
+                            continue
+                        }
                         let imageElement = collectionImage.cloneNode()
 
                         imageElement.className = "docs-collapsible-image"
@@ -971,6 +977,9 @@ function newSuperalgosDocsDocumentPage() {
                         let dummyImage = imageArray[i]
                         let parentElement = dummyImage.parentNode
                         let collectionImage = UI.projects.superalgos.spaces.designSpace.getIconByProjectAndName('Superalgos', 'configuration')
+                        if (collectionImage === undefined) {
+                            continue
+                        }
                         let imageElement = collectionImage.cloneNode()
 
                         imageElement.className = "docs-collapsible-image"

Projects/Superalgos/UI/Spaces/Floating-Space/CircularMenu.js

@@ -127,8 +127,8 @@ function newCircularMenu() {
             }
         }
         /* Text and Icon */
-        let amplitude = 140
-        let initialAngle = -70
+        let amplitude = 60
+        let initialAngle = -30
         step = amplitude / 7
         let currentItem = (7 - iconAndTextArray.length) / 2 + 1
         for (let i = 0; i < iconAndTextArray.length; i++) {

Projects/Superalgos/UI/Spaces/Floating-Space/CircularMenuItem.js

@@ -14,7 +14,7 @@ function newCircularMenuItem() {
         disableIfPropertyIsDefined: undefined,
         propertyToCheckFor: undefined,
         action: undefined,
-        actionProject: undefined, 
+        actionProject: undefined,
         actionFunction: undefined,
         actionStatus: undefined,
         label: undefined,
@@ -194,17 +194,32 @@ function newCircularMenuItem() {
         }
         radiusGrowthFactor = radiusGrowthFactor * UI.projects.superalgos.spaces.floatingSpace.settings.node.menuItem.radiusPercentage / 100
 
-        thisObject.container.frame.position.x =
-            thisObject.payload.floatingObject.targetRadius *
-            radiusGrowthFactor / 7
-            * Math.cos(toRadians(thisObject.angle)) -
-            thisObject.currentRadius * 1.5
+        if (thisObject.label !== undefined) {
+            thisObject.container.frame.position.x =
+                thisObject.payload.floatingObject.targetRadius *
+                radiusGrowthFactor / 7
+                * Math.cos(toRadians(thisObject.angle)) * 1.5
+                - thisObject.currentRadius * 1.5
+                - thisObject.payload.floatingObject.targetRadius / 4
 
-        thisObject.container.frame.position.y =
-            thisObject.payload.floatingObject.targetRadius *
-            radiusGrowthFactor / 7
-            * Math.sin(toRadians(thisObject.angle))
-            - thisObject.container.frame.height / 2
+            thisObject.container.frame.position.y =
+                thisObject.payload.floatingObject.targetRadius *
+                radiusGrowthFactor / 7
+                * Math.sin(toRadians(thisObject.angle)) * 2
+                - thisObject.container.frame.height / 2
+        } else {
+            thisObject.container.frame.position.x =
+                thisObject.payload.floatingObject.targetRadius *
+                radiusGrowthFactor / 7
+                * Math.cos(toRadians(thisObject.angle))
+                - thisObject.currentRadius * 1.5
+
+            thisObject.container.frame.position.y =
+                thisObject.payload.floatingObject.targetRadius *
+                radiusGrowthFactor / 7
+                * Math.sin(toRadians(thisObject.angle))
+                - thisObject.container.frame.height / 2
+        }
     }
 
     function disablePhysics() {

Projects/TensorFlow/Plugins/Workspaces/Machine-Learning-Demo.json

@@ -0,0 +1 @@
+{"type":"Workspace","name":"Machine-Learning-Demo","rootNodes":[{"type":"Network","name":"My Network","project":"Superalgos","networkNodes":[{"type":"Network Node","name":"My Computer","config":"{ \n\"host\": \"localhost\", \n\"webPort\": \"34248\", \n\"webSocketsPort\": \"18041\"\n}","project":"Superalgos","dataTasks":{"type":"Data Tasks","name":"New Data Tasks","project":"Superalgos","projectDataTasks":[{"type":"Project Data Tasks","name":"Superalgos","project":"Superalgos","exchangeDataTasks":[{"type":"Exchange Data Tasks","name":"Binance","project":"Superalgos","marketDataTasks":[{"type":"Market Data Tasks","name":"Binance BTC/USDT","project":"Superalgos","dataMineTasks":[{"type":"Data Mine Tasks","name":"Masters","project":"Superalgos","taskManagers":[{"type":"Task Manager","name":"Masters","project":"Superalgos","tasks":[{"type":"Task","name":"Exchange Raw Data","project":"Superalgos","bot":{"type":"Sensor Bot Instance","name":"Exchange Raw Data","config":"{\n    \"startDate\": \"2021-01-01\"\n}","project":"Superalgos","processes":[{"type":"Sensor Process Instance","name":"Exchange Raw Data Historic-OHLCVs","project":"Superalgos","id":"31e87840-fc8a-4305-a97d-3a78b8e4be61","savedPayload":{"position":{"x":23464.726581780902,"y":29681.25609376178},"targetPosition":{"x":23756.270052277963,"y":29610.528413108954},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process Definition","name":"Historic-OHLCVs","id":"5eb11d6a-022e-4f60-97c4-d6cf90b249dc"}}}],"id":"ddab2649-221f-40c4-8b8e-b3cbceca0c4b","savedPayload":{"position":{"x":23756.270052277963,"y":29610.528413108954},"targetPosition":{"x":24047.813522775024,"y":29539.800732456126},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},"id":"1bca451f-f059-4dc2-87cb-ba2bb1c16ef4","savedPayload":{"position":{"x":24047.813522775024,"y":29539.800732456126},"targetPosition":{"x":24339.356993272086,"y":29469.073051803298},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},{"type":"Task","name":"Webhooks","project":"Superalgos","bot":{"type":"Sensor Bot Instance","name":"Webhooks","config":"{ \n\"startDate\": \"2020-01-01\"\n}","project":"Superalgos","processes":[{"type":"Sensor Process Instance","name":"Webhooks Check-Webhook","project":"Superalgos","id":"4595558a-d0ba-49b1-9c81-67696f23261c","savedPayload":{"position":{"x":23440.376388007382,"y":29426.249327561927},"targetPosition":{"x":23740.036589762283,"y":29440.52390230905},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process Definition","name":"Check-Webhook","id":"69f37820-ab16-4e19-bfd8-386dd6e249a3"}}}],"id":"a7978877-bdef-4e69-a97d-af0560ef219e","savedPayload":{"position":{"x":23740.036589762283,"y":29440.52390230905},"targetPosition":{"x":24039.696791517184,"y":29454.798477056174},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},"id":"bf34c6df-d1ac-4f7c-8a6c-7ed9b6de2e00","savedPayload":{"position":{"x":24039.696791517184,"y":29454.798477056174},"targetPosition":{"x":24339.356993272086,"y":29469.073051803298},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":2,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false}}},{"type":"Task","name":"Candles Volumes","project":"Superalgos","bot":{"type":"Indicator Bot Instance","name":"Candles Volumes","project":"Superalgos","processes":[{"type":"Indicator Process Instance","name":"Candles Volumes Multi-Period-Market","project":"Superalgos","id":"7cdc33e0-c56b-43ce-95f5-db03a171f021","savedPayload":{"position":{"x":23473.12086766021,"y":29251.430518853074},"targetPosition":{"x":23772.356502043287,"y":29272.832273812845},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process Definition","name":"Multi-Period-Market","id":"9f683143-a33f-42c1-9fa8-c601f01ee4e1"}}},{"type":"Indicator Process Instance","name":"Candles Volumes Multi-Period-Daily","project":"Superalgos","id":"4da5ecc7-48e6-4283-8a2f-512528a28fe4","savedPayload":{"position":{"x":23523.911718429878,"y":29104.679993140577},"targetPosition":{"x":23772.356502043287,"y":29272.832273812845},"floatingObject":{"isPinned":false,"isFrozen":false,"isCollapsed":false,"angleToParent":3,"distanceToParent":3,"arrangementStyle":0},"uiObject":{"isRunning":false},"referenceParent":{"type":"Process DefiniLine truncated

Projects/TensorFlow/Schemas/Docs-Concepts/backend.json

@@ -0,0 +1,13 @@
+{
+    "type": "Backend",
+    "definition": {
+        "text": "TensorFlow.js support multiple different backends that implement tensor storage and mathematical operations. At any given time, only one backend is active. Most of the time, TensorFlow.js will automatically choose the best backend for you given the current Execution Environmet.",
+        "updated": 1613059266937
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and Edit to enter edit mode and change this text. ENTER to write new paragraphs. ESC to exit edit mode. Then you can use doscs.save to save your changes and app.contribute to send it to the main repository."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/activation-function.json

@@ -0,0 +1,23 @@
+{
+    "type": "Activation Function",
+    "definition": {
+        "text": "A function (for example, ReLU or sigmoid) that takes in the weighted sum of all of the inputs from the previous layer and then generates and passes an output value (typically nonlinear) to the next layer.",
+        "updated": 1613243485558
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Activation function to use. If unspecified, no activation is applied.",
+            "updated": 1613243465795
+        },
+        {
+            "style": "Subtitle",
+            "text": "Possible Values"
+        },
+        {
+            "style": "Javascript",
+            "text": "elu\nhardSigmoid\nlinear\nrelu\nrelu6\nselu\nsigmoid\nsoftmax\nsoftplus\nsoftsign\ntanh",
+            "updated": 1613244131007
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/activation-layer.json

@@ -0,0 +1,14 @@
+{
+    "type": "Activation Layer",
+    "definition": {
+        "text": "Applies an activation function to an output. ",
+        "updated": 1613213636961
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "This layer applies element-wise activation function. Other layers, notably dense can also apply activation functions. Use this isolated activation function to extract the values before and after the activation.",
+            "updated": 1613213641004
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/advanced-activation-layers.json

@@ -0,0 +1,13 @@
+{
+    "type": "Advanced Activation Layers",
+    "definition": {
+        "text": "Use this type of layer when you want to apply an advanced activation function.",
+        "updated": 1613210798694
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/artificial-neural-network.json

@@ -0,0 +1,14 @@
+{
+    "type": "Artificial Neural Network",
+    "definition": {
+        "text": "An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.",
+        "updated": 1613198337105
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "The \"signal\" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.",
+            "updated": 1613198350617
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/basic-layers.json

@@ -0,0 +1,13 @@
+{
+    "type": "Basic Layers",
+    "definition": {
+        "text": "Basic Layers are the most common layers you can use.",
+        "updated": 1613210641501
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/batch-input-shape.json

@@ -0,0 +1,18 @@
+{
+    "type": "Batch Input Shape",
+    "definition": {
+        "text": "If defined, will be used to create an input layer to insert before this layer. If both Input Shape and batchInputShape are defined, Batch Input Shape will be used. This argument is only applicable to input layers (the first layer of a model).",
+        "updated": 1613243097313
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613243125301
+        },
+        {
+            "style": "Javascript",
+            "text": "null | number"
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/batch-size.json

@@ -0,0 +1,22 @@
+{
+    "type": "Batch Size",
+    "definition": {
+        "text": "The number of examples in a batch. For example, the batch size of SGD is 1, while the batch size of a mini-batch is usually between 10 and 1000. Batch size is usually fixed during training and inference; however, TensorFlow does permit dynamic batch sizes.",
+        "updated": 1613243372713
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: [batchSize,...inputShape]",
+            "updated": 1613243409493
+        },
+        {
+            "style": "Subtitle",
+            "text": "Possible Values"
+        },
+        {
+            "style": "Javascript",
+            "text": "number"
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/bias-constraint.json

@@ -0,0 +1,19 @@
+{
+    "type": "Bias Constraint",
+    "definition": {
+        "text": "Constraint for the bias vector.",
+        "updated": 1613244677505
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613244686071
+        },
+        {
+            "style": "Javascript",
+            "text": "maxNorm\nminMaxNorm\nnonNeg\nunitNorm\nstring",
+            "updated": 1613244735313
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/bias-initializer.json

@@ -0,0 +1,19 @@
+{
+    "type": "Bias Initializer",
+    "definition": {
+        "text": "Initializer for the bias vector.",
+        "updated": 1613244555001
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613244570066
+        },
+        {
+            "style": "Javascript",
+            "text": "constant \nglorotNormal\nglorotUniform\nheNormal\nheUniform\nidentity\nleCunNormal\nleCunUniform\nones\northogonal\nrandomNormal\nrandomUniform\ntruncatedNormal\nvarianceScaling\nzeros\nstring",
+            "updated": 1613244651997
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/bias-regularizer.json

@@ -0,0 +1,19 @@
+{
+    "type": "Bias Regularizer",
+    "definition": {
+        "text": "Regularizer function applied to the bias vector.",
+        "updated": 1613244759755
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Possible Values",
+            "updated": 1613244768649
+        },
+        {
+            "style": "Javascript",
+            "text": "l1l2\nstring\nRegularizer",
+            "updated": 1613244794672
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/bias.json

@@ -0,0 +1,13 @@
+{
+    "type": "Bias",
+    "definition": {
+        "text": "A value indicating how far apart the average of predictions is from the average of labels in the dataset.",
+        "updated": 1613243679048
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/conv-1d-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Conv 1D Layer",
+    "definition": {
+        "text": "1D convolution layer (e.g., temporal convolution). This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs.",
+        "updated": 1613218586883
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/conv-2d-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Conv 2D Layer",
+    "definition": {
+        "text": "2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.",
+        "updated": 1613218624550
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/conv-2d-transpose-layer.json

@@ -0,0 +1,22 @@
+{
+    "type": "Conv 2D Transpose Layer",
+    "definition": {
+        "text": "Transposed convolutional layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.",
+        "updated": 1613218678919
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "When using this layer as the first layer in a model, provide the configuration inputShape (Array of integers, does not include the sample axis), e.g., inputShape: [128, 128, 3] for 128x128 RGB pictures in dataFormat: 'channelsLast'.",
+            "updated": 1613218856414
+        },
+        {
+            "style": "List",
+            "text": "Input shape: 4D tensor with shape: [batch, channels, rows, cols] if dataFormat is 'channelsFirst'. or 4D tensor with shape [batch, rows, cols, channels] if dataFormat is 'channelsLast."
+        },
+        {
+            "style": "List",
+            "text": "Output shape: 4D tensor with shape: [batch, filters, newRows, newCols] if dataFormat is 'channelsFirst'. or 4D tensor with shape: [batch, newRows, newCols, filters] if dataFormat is 'channelsLast'."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/conv-3d-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Conv 3D Layer",
+    "definition": {
+        "text": "3D convolution layer (e.g. spatial convolution over volumes). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.",
+        "updated": 1613218709328
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/convolutional-layers.json

@@ -0,0 +1,13 @@
+{
+    "type": "Convolutional Layers",
+    "definition": {
+        "text": "Use this type of layer to create a convolution kernel.",
+        "updated": 1613210887837
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/core-api.json

@@ -0,0 +1,13 @@
+{
+    "type": "Core API",
+    "definition": {
+        "text": "The Core API is a lower-level API for building models.",
+        "updated": 1613201747129
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/cpu-backend.json

@@ -0,0 +1,13 @@
+{
+    "type": "CPU Backend",
+    "definition": {
+        "text": "The CPU Backend,'cpu', is the least performant backend, however it is the simplest. Operations are all implemented in vanilla JavaScript, which makes them less parallelizable.",
+        "updated": 1613059028403
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/cropping-2d-layer.json

@@ -0,0 +1,34 @@
+{
+    "type": "Cropping 2D Layer",
+    "definition": {
+        "text": "Cropping layer for 2D input (e.g., image). This layer can crop an input at the top, bottom, left and right side of an image tensor.",
+        "updated": 1613218755900
+    },
+    "paragraphs": [
+        {
+            "style": "List",
+            "text": "Input shape: 4D tensor with shape:",
+            "updated": 1613218787273
+        },
+        {
+            "style": "Text",
+            "text": "If dataFormat is \"channelsLast\": [batch, rows, cols, channels]"
+        },
+        {
+            "style": "Text",
+            "text": "If data_format is \"channels_first\": [batch, channels, rows, cols]."
+        },
+        {
+            "style": "List",
+            "text": "Output shape: 4D with shape:"
+        },
+        {
+            "style": "Text",
+            "text": "If dataFormat is \"channelsLast\": [batch, croppedRows, croppedCols, channels]"
+        },
+        {
+            "style": "Text",
+            "text": "If dataFormat is \"channelsFirst\": [batch, channels, croppedRows, croppedCols]."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/debug-mode.json

@@ -0,0 +1,13 @@
+{
+    "type": "Debug Mode",
+    "definition": {
+        "text": "Debug Mode will log to the console every operation that is executed, as well as runtime performance information like memory footprint and total kernel execution time. Note that this will greatly slow down your application, do not use this in production.",
+        "updated": 1613062911672
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/dense-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Dense Layer",
+    "definition": {
+        "text": "Creates a dense (fully connected) layer.",
+        "updated": 1613213671494
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/depthwise-conv-2d-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Depthwise Conv 2D Layer",
+    "definition": {
+        "text": "Depthwise separable 2D convolution. Depthwise Separable convolutions consists in performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depthMultplier argument controls how many output channels are generated per input channel in the depthwise step.",
+        "updated": 1613218906419
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/dimensionality-units.json

@@ -0,0 +1,18 @@
+{
+    "type": "Dimensionality Units",
+    "definition": {
+        "text": "Positive integer, dimensionality of the output space.",
+        "updated": 1613243190046
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613243500472
+        },
+        {
+            "style": "Javascript",
+            "text": "number"
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/dropout-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Dropout Layer",
+    "definition": {
+        "text": "Applies dropout to the input. Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting.",
+        "updated": 1613213791502
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/dtype.json

@@ -0,0 +1,19 @@
+{
+    "type": "Dtype",
+    "definition": {
+        "text": "The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).",
+        "updated": 1613243770062
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613243792602
+        },
+        {
+            "style": "Javascript",
+            "text": "float32\nint32 \nbool\ncomplex64\nstring",
+            "updated": 1613243879019
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/elu-layer.json

@@ -0,0 +1,26 @@
+{
+    "type": "Elu Layer",
+    "definition": {
+        "text": "Exponetial Linear Unit (ELU).",
+        "updated": 1613216664718
+    },
+    "paragraphs": [
+        {
+            "style": "Title",
+            "text": "It Follows",
+            "updated": 1613216728404
+        },
+        {
+            "style": "Javascript",
+            "text": "f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0."
+        },
+        {
+            "style": "List",
+            "text": "Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model."
+        },
+        {
+            "style": "List",
+            "text": "Output shape: Same shape as the input."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/embedding-layer.json

@@ -0,0 +1,26 @@
+{
+    "type": "Embedding Layer",
+    "definition": {
+        "text": "Maps positive integers (indices) into dense vectors of fixed size. ",
+        "updated": 1613213823616
+    },
+    "paragraphs": [
+        {
+            "style": "Title",
+            "text": "Example",
+            "updated": 1613213848804
+        },
+        {
+            "style": "Javascript",
+            "text": "[[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]"
+        },
+        {
+            "style": "Text",
+            "text": "Input shape:* 2D tensor with shape: [batchSize, sequenceLength]."
+        },
+        {
+            "style": "Text",
+            "text": "Output shape:* 3D tensor with shape: [batchSize, sequenceLength, outputDim]."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/environment-flags.json

@@ -0,0 +1,13 @@
+{
+    "type": "Environment Flags",
+    "definition": {
+        "text": "TensorFlow.js has a set of environment flags that are automatically evaluated and determine the best configuration in the current platform. These flags are mostly internal, but a few global flags can be controlled with public API.",
+        "updated": 1613062805784
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/execution-environment.json

@@ -0,0 +1,13 @@
+{
+    "type": "Execution Environment",
+    "definition": {
+        "text": "When a TensorFlow.js program is executed, the specific configuration is called the execution environment. The Execution Environment is comprised of a single global backend as well as a set of flags that control fine-grained features of TensorFlow.js.",
+        "updated": 1613060399329
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/feature-formula.json

@@ -0,0 +1,13 @@
+{
+    "type": "Feature Formula",
+    "definition": {
+        "text": "A formula that allows users to specify an input value using any of the Data Structures provided by Superalgos to access indicators.",
+        "updated": 1613299711281
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/feature.json

@@ -0,0 +1,13 @@
+{
+    "type": "Feature",
+    "definition": {
+        "text": "An input variable used in making predictions.",
+        "updated": 1613299625520
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/flatten-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Flatten Layer",
+    "definition": {
+        "text": "Flattens the input. Does not affect the batch size. A Flatten layer flattens each batch in its inputs to 1D (making the output 2D).",
+        "updated": 1613213956160
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/functional-model.json

@@ -0,0 +1,13 @@
+{
+    "type": "Functional Model",
+    "definition": {
+        "text": "The key difference between a Functional and Secuential Model is that Functional Models allows you to create an arbitrary graph of layers, as long as they don't have cycles.",
+        "updated": 1613207678780
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/input-features.json

@@ -0,0 +1,13 @@
+{
+    "type": "Input Features",
+    "definition": {
+        "text": "Features are input variables used in making predictions.",
+        "updated": 1613297817985
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/input-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Input Layer",
+    "definition": {
+        "text": "The first layer (the one that receives the input data) in an Artificial Neural Network.",
+        "updated": 1613209053300
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/input-shape.json

@@ -0,0 +1,13 @@
+{
+    "type": "Input Shape",
+    "definition": {
+        "text": "The first layer in the model needs an inputShape. Make sure you exclude the batch size when providing the inputShape. For example, if you plan to feed the model tensors of shape [B, 784], where B can be any batch size, specify inputShape as [784] when creating the model.",
+        "updated": 1613220197721
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/kernel-constraint.json

@@ -0,0 +1,19 @@
+{
+    "type": "Kernel Constraint",
+    "definition": {
+        "text": "Constraint for the kernel weights.",
+        "updated": 1613244365425
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613244375424
+        },
+        {
+            "style": "Javascript",
+            "text": "maxNorm\nminMaxNorm\nnonNeg\nunitNorm\nstring",
+            "updated": 1613244425597
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/kernel-initializer.json

@@ -0,0 +1,19 @@
+{
+    "type": "Kernel Initializer",
+    "definition": {
+        "text": "Initializer for the kernel weights matrix.",
+        "updated": 1613244248363
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613244268885
+        },
+        {
+            "style": "Javascript",
+            "text": "constant\nglorotNormal\nglorotUniform\nheNormal\nheUniform\nidentity\nleCunNormal\nleCunUniform\nones\northogonal\nrandomNormal\nrandomUniform\ntruncatedNormal\nvarianceScaling\nzeros\nstring",
+            "updated": 1613244326784
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/kernel-regularizer.json

@@ -0,0 +1,19 @@
+{
+    "type": "Kernel Regularizer",
+    "definition": {
+        "text": "Regularizer function applied to the kernel weights matrix.",
+        "updated": 1613244470025
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613244479696
+        },
+        {
+            "style": "Javascript",
+            "text": "l1l2\nstring\nRegularizer",
+            "updated": 1613244531951
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/layers-api.json

@@ -0,0 +1,13 @@
+{
+    "type": "Layers API",
+    "definition": {
+        "text": "The Layers API is a higher-level API for building models.",
+        "updated": 1613201561078
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/leaky-relu-layer.json

@@ -0,0 +1,26 @@
+{
+    "type": "Leaky Relu Layer",
+    "definition": {
+        "text": "Leaky version of a rectified linear unit.",
+        "updated": 1613216814199
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "It allows a small gradient when the unit is not active: ",
+            "updated": 1613216841876
+        },
+        {
+            "style": "Javascript",
+            "text": "f(x) = alpha * x for x < 0. f(x) = x for x >= 0."
+        },
+        {
+            "style": "List",
+            "text": "Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model."
+        },
+        {
+            "style": "List",
+            "text": "Output shape: Same shape as the input."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/machine-learning.json

@@ -0,0 +1,13 @@
+{
+    "type": "Machine Learning",
+    "definition": {
+        "text": "A program or system that builds (trains) a predictive model from input data. The system uses the learned model to make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model. Machine learning also refers to the field of study concerned with these programs or systems.",
+        "updated": 1613055672003
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/model.json

@@ -0,0 +1,22 @@
+{
+    "type": "Model",
+    "definition": {
+        "text": "In machine learning, a model is a function with learnable parameters that maps an input to an output. The optimal parameters are obtained by training the model on data. A well-trained model will provide an accurate mapping from the input to the desired output.",
+        "updated": 1613200965382
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "In TensorFlow.js there are two ways to create a machine learning model:",
+            "updated": 1613201440028
+        },
+        {
+            "style": "List",
+            "text": "using the Layers API where you build a model using layers."
+        },
+        {
+            "style": "List",
+            "text": "using the Core API with lower-level ops such as tf.matMul(), tf.add(), etc."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/nodejs-backend.json

@@ -0,0 +1,13 @@
+{
+    "type": "NodeJS Backend",
+    "definition": {
+        "text": "In the TensorFlow Node.js backend, 'node', the TensorFlow C API is used to accelerate operations. This will use the machine's available hardware acceleration, like CUDA, if available.",
+        "updated": 1613058717641
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/output-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Output Layer",
+    "definition": {
+        "text": "The \"final\" layer of an Artificial Neural Network. The layer containing the answer(s).",
+        "updated": 1613209100738
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/permute-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Permute Layer",
+    "definition": {
+        "text": "Permutes the dimensions of the input according to a given pattern. Useful for, e.g., connecting RNNs and convnets together.",
+        "updated": 1613214015429
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/prelu-layer.json

@@ -0,0 +1,27 @@
+{
+    "type": "Prelu Layer",
+    "definition": {
+        "text": "Parameterized version of a leaky rectified linear unit.",
+        "updated": 1613216882813
+    },
+    "paragraphs": [
+        {
+            "style": "Title",
+            "text": "It Follows ",
+            "updated": 1613216907058
+        },
+        {
+            "style": "Javascript",
+            "text": "f(x) = alpha * x for x < 0. f(x) = x for x >= 0. wherein alpha is a trainable weight",
+            "updated": 1613216938490
+        },
+        {
+            "style": "List",
+            "text": "Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model."
+        },
+        {
+            "style": "List",
+            "text": "Output shape: Same shape as the input."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/production-mode.json

@@ -0,0 +1,13 @@
+{
+    "type": "Production Mode",
+    "definition": {
+        "text": "Production Mode will remove model validation, NaN checks, and other correctness checks in favor of performance.",
+        "updated": 1613062862838
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/reinforcement-learning.json

@@ -0,0 +1,19 @@
+{
+    "type": "Reinforcement Learning",
+    "definition": {
+        "text": "Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.",
+        "updated": 1613197154946
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).",
+            "updated": 1613197179841
+        },
+        {
+            "style": "Text",
+            "text": "The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.",
+            "updated": 1613197190452
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/relu-layer.json

@@ -0,0 +1,18 @@
+{
+    "type": "Relu Layer",
+    "definition": {
+        "text": "Rectified Linear Unit activation function.",
+        "updated": 1613216977133
+    },
+    "paragraphs": [
+        {
+            "style": "List",
+            "text": "Input shape: Arbitrary. Use the config field inputShape (Array of integers, does not include the sample axis) when using this layer as the first layer in a model.",
+            "updated": 1613216997299
+        },
+        {
+            "style": "List",
+            "text": "Output shape: Same shape as the input."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/repeat-vector-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Repeat Vector Layer",
+    "definition": {
+        "text": "Repeats the input n times in a new dimension.",
+        "updated": 1613214045190
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/reshape-layer.json

@@ -0,0 +1,18 @@
+{
+    "type": "Reshape Layer",
+    "definition": {
+        "text": "Reshapes an input to a certain shape.",
+        "updated": 1613214077391
+    },
+    "paragraphs": [
+        {
+            "style": "List",
+            "text": "Input shape: Arbitrary, although all dimensions in the input shape must be fixed. Use the configuration inputShape when using this layer as the first layer in a model.",
+            "updated": 1613214162552
+        },
+        {
+            "style": "List",
+            "text": "Output shape: [batchSize, targetShape[0], targetShape[1], ..., targetShape[targetShape.length - 1]]."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/secuential-layer.json

@@ -0,0 +1,13 @@
+{
+    "type": "Secuential Layer",
+    "definition": {
+        "text": "A set of neurons in an Artificial Neural Network with a Secuential Model, that process a set of input features, or the output of those neurons.",
+        "updated": 1613208793464
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/secuential-model.json

@@ -0,0 +1,13 @@
+{
+    "type": "Secuential Model",
+    "definition": {
+        "text": "The most common type of model is the Sequential model, which is a linear stack of layers. ",
+        "updated": 1613201804709
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/self-learning.json

@@ -0,0 +1,13 @@
+{
+    "type": "Self Learning",
+    "definition": {
+        "text": "Self-learning as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). It is a learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.",
+        "updated": 1613199403151
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/separable-conv-2d-layer.json

@@ -0,0 +1,22 @@
+{
+    "type": "Separable Conv 2D Layer",
+    "definition": {
+        "text": "Depthwise separable 2D convolution. Separable convolution consists of first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. The depthMultiplier argument controls how many output channels are generated per input channel in the depthwise step.",
+        "updated": 1613218940003
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.",
+            "updated": 1613218949720
+        },
+        {
+            "style": "List",
+            "text": "Input shape: 4D tensor with shape: [batch, channels, rows, cols] if data_format='channelsFirst' or 4D tensor with shape: [batch, rows, cols, channels] if data_format='channelsLast'."
+        },
+        {
+            "style": "List",
+            "text": "Output shape: 4D tensor with shape: [batch, filters, newRows, newCols] if data_format='channelsFirst' or 4D tensor with shape: [batch, newRows, newCols, filters] if data_format='channelsLast'. rows and cols values might have changed due to padding."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/softmax-layer.json

@@ -0,0 +1,18 @@
+{
+    "type": "Softmax Layer",
+    "definition": {
+        "text": "Softmax activation layer. A function that provides probabilities for each possible class in a multi-class classification model. The probabilities add up to exactly 1.0. For example, softmax might determine that the probability of a particular image being a dog at 0.9, a cat at 0.08, and a horse at 0.02.",
+        "updated": 1613217104730
+    },
+    "paragraphs": [
+        {
+            "style": "List",
+            "text": "Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model.",
+            "updated": 1613217039070
+        },
+        {
+            "style": "List",
+            "text": "Output shape: Same shape as the input."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/spatial-dropout-1d-layer.json

@@ -0,0 +1,18 @@
+{
+    "type": "Spatial Dropout 1D Layer",
+    "definition": {
+        "text": "Spatial 1D version of Dropout.",
+        "updated": 1613214223035
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "This Layer type performs the same function as the Dropout layer, but it drops entire 1D feature maps instead of individual elements. For example, if an input example consists of 3 timesteps and the feature map for each timestep has a size of 4, a spatialDropout1d layer may zero out the feature maps of the 1st timesteps and 2nd timesteps completely while sparing all feature elements of the 3rd timestep.",
+            "updated": 1613214250740
+        },
+        {
+            "style": "Text",
+            "text": "If adjacent frames (timesteps) are strongly correlated (as is normally the case in early convolution layers), regular dropout will not regularize the activation and will otherwise just result in merely an effective learning rate decrease. In this case, spatialDropout1d will help promote independence among feature maps and should be used instead."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/supervised-learning.json

@@ -0,0 +1,14 @@
+{
+    "type": "Supervised Learning",
+    "definition": {
+        "text": "Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).",
+        "updated": 1613197014944
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a \"reasonable\" way (see inductive bias).",
+            "updated": 1613197051912
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/tensorflow-library.json

@@ -0,0 +1,13 @@
+{
+    "type": "TensorFlow Library",
+    "definition": {
+        "text": "In Node.js, TensorFlow.js supports binding directly to the TensorFlow API or running with the slower vanilla CPU implementations.",
+        "updated": 1613062988909
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/thresholded-relu-layer.json

@@ -0,0 +1,26 @@
+{
+    "type": "Thresholded Relu Layer",
+    "definition": {
+        "text": "Thresholded Rectified Linear Unit.",
+        "updated": 1613217166999
+    },
+    "paragraphs": [
+        {
+            "style": "Title",
+            "text": "It Follows ",
+            "updated": 1613217193348
+        },
+        {
+            "style": "Javascript",
+            "text": "f(x) = x for x > theta, f(x) = 0 otherwise."
+        },
+        {
+            "style": "List",
+            "text": "Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model."
+        },
+        {
+            "style": "List",
+            "text": "Output shape: Same shape as the input."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/trainable.json

@@ -0,0 +1,19 @@
+{
+    "type": "Trainable",
+    "definition": {
+        "text": "Whether the weights of this layer are updatable by training. Defaults to true.",
+        "updated": 1613243947387
+    },
+    "paragraphs": [
+        {
+            "style": "Subtitle",
+            "text": "Possible Values",
+            "updated": 1613243963146
+        },
+        {
+            "style": "Javascript",
+            "text": "true\nfalse",
+            "updated": 1613243984862
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/unsupervised-learning.json

@@ -0,0 +1,14 @@
+{
+    "type": "Unsupervised Learning",
+    "definition": {
+        "text": "Unsupervised learning (UL) is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, the machine is forced to build a compact internal representation of its world. ",
+        "updated": 1613197508073
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "In contrast to Supervised Learning (SL) where data is tagged by a human, eg. as \"car\" or \"fish\" etc, UL exhibits self-organization that captures patterns as neuronal predelections or probability densities. The other levels in the supervision spectrum are Reinforcement Learning where the machine is given only a numerical performance score as its guidance, and Semi-supervised learning where a smaller portion of the data is tagged. Two broad methods in UL are Neural Networks and Probabilistic Methods.",
+            "updated": 1613197533736
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/up-sampling-2d-layer.json

@@ -0,0 +1,18 @@
+{
+    "type": "Up Sampling 2D Layer",
+    "definition": {
+        "text": "Upsampling layer for 2D inputs. Repeats the rows and columns of the data by size[0] and size[1] respectively.",
+        "updated": 1613219000480
+    },
+    "paragraphs": [
+        {
+            "style": "List",
+            "text": "Input shape: 4D tensor with shape: - If dataFormat is \"channelsLast\": [batch, rows, cols, channels] - If dataFormat is \"channelsFirst\": [batch, channels, rows, cols]",
+            "updated": 1613219012908
+        },
+        {
+            "style": "List",
+            "text": "Output shape: 4D tensor with shape: - If dataFormat is \"channelsLast\": [batch, upsampledRows, upsampledCols, channels] - If dataFormat is \"channelsFirst\": [batch, channels, upsampledRows, upsampledCols]"
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/wasm-backend.json

@@ -0,0 +1,13 @@
+{
+    "type": "WASM Backend",
+    "definition": {
+        "text": "TensorFlow.js provides a WebAssembly backend (wasm), which offers CPU acceleration and can be used as an alternative to the vanilla JavaScript CPU (cpu) and WebGL accelerated (webgl) backends.",
+        "updated": 1613058813729
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/webgl-backend.json

@@ -0,0 +1,13 @@
+{
+    "type": "WebGL Backend",
+    "definition": {
+        "text": "The WebGL Backend,'webgl', is currently the most powerful Backend for the browser. This backend is up to 100x faster than the vanilla CPU backend. Tensors are stored as WebGL textures and mathematical operations are implemented in WebGL shaders.",
+        "updated": 1613058352372
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/weights.json

@@ -0,0 +1,13 @@
+{
+    "type": "Weights",
+    "definition": {
+        "text": "Initial weight values of the layer.",
+        "updated": 1613244009359
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Left click and select the pencil button to enter edit mode."
+        }
+    ]
+}

Projects/TensorFlow/UI/Function-Libraries/ActionSwitch.js

@@ -0,0 +1,30 @@
+function newTensorFlowActionSwitch() {
+
+    let thisObject = {
+        executeAction: executeAction,
+        initialize: initialize,
+        finalize: finalize
+    }
+
+
+    return thisObject
+
+    function finalize() {
+
+    }
+
+    function initialize() {
+        /* Nothing to initialize since a Function Library does not hold any state. */
+    }
+
+    async function executeAction(action) {
+        switch (action.name) {
+            /*
+            case 'Create Wallet Account': {
+                UI.projects.ethereum.functionLibraries.accounts.createWalletAccount(action.node)
+                break
+            }
+            */
+        }
+    }
+}