Merge branch 'keras-team:master' into master

.github/workflows/csat.yaml

@@ -0,0 +1,21 @@
+name: 'CSAT survey'
+on:
+  issues:
+    types:
+      - closed
+
+permissions:
+  contents: read
+  issues: write
+  pull-requests: write
+
+jobs:
+  welcome:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/github-script@v6
+        with:
+          script: |
+            const script = require('./\.github/workflows/scripts/csat.js')
+            script({github, context})

.github/workflows/scripts/constant.js

@@ -0,0 +1,14 @@
+let CONSTANT_VALUES = {
+    MODULE: {
+        CSAT: {
+            YES:'Yes',
+            NO:'No',
+            BASE_URL: 'https://docs.google.com/forms/d/e/1FAIpQLSdHag0RVFS7UXzZkKcsFCKOcX8raCupKK9RHSlYxp5U8lSJbQ/viewform?',
+            SATISFACTION_PARAM: 'entry.492125872=',
+            ISSUEID_PARAM: '&entry.243948740=',
+            MSG: 'Are you satisfied with the resolution of your issue?',
+            CSAT_LABELS: ['type:bug', 'type:build/install', 'type:support', 'type:others', 'type:docs-bug', 'type:performance', 'type:feature']
+        }
+    }
+  };
+  module.exports = CONSTANT_VALUES;

.github/workflows/scripts/csat.js

@@ -0,0 +1,34 @@
+
+const CONSTANT_VALUES = require('./constant');
+ /** 
+  * Invoked from staleCSAT.js and CSAT.yaml file to 
+  * post survey link in closed issue.
+  * @param {!object} Object -  gitHub and context contains the information about the
+  *  current and context and github APIs using their built-in library functions.
+ */
+module.exports = async ({ github, context }) => {
+    const issue = context.payload.issue.html_url;
+    let base_url = CONSTANT_VALUES.MODULE.CSAT.BASE_URL;
+     //Loop over all ths label present in issue and check if specific label is present for survey link.
+    for (const label of context.payload.issue.labels) {
+            if (CONSTANT_VALUES.MODULE.CSAT.CSAT_LABELS.includes(label.name)) {
+                console.log(`label-${label.name}, posting CSAT survey for issue =${issue}`);
+                const yesCsat = `<a href="${base_url + CONSTANT_VALUES.MODULE.CSAT.SATISFACTION_PARAM +
+                    CONSTANT_VALUES.MODULE.CSAT.YES +
+                    CONSTANT_VALUES.MODULE.CSAT.ISSUEID_PARAM + issue}"> ${CONSTANT_VALUES.MODULE.CSAT.YES}</a>`;
+
+                const noCsat = `<a href="${base_url + CONSTANT_VALUES.MODULE.CSAT.SATISFACTION_PARAM +
+                    CONSTANT_VALUES.MODULE.CSAT.NO +
+                    CONSTANT_VALUES.MODULE.CSAT.ISSUEID_PARAM + issue}"> ${CONSTANT_VALUES.MODULE.CSAT.NO}</a>`;
+                const comment = CONSTANT_VALUES.MODULE.CSAT.MSG + '\n' + yesCsat + '\n' +
+                    noCsat + '\n';
+                let issueNumber = context.issue.number ?? context.payload.issue.number;
+                await github.rest.issues.createComment({
+                    issue_number: issueNumber,
+                    owner: context.repo.owner,
+                    repo: context.repo.repo,
+                    body: comment
+                });
+            }
+        }
+    };

.github/workflows/scripts/stale_csat.js

@@ -0,0 +1,52 @@
+
+const csat = require('./csat.js');
+const CONSTANT = require("./constant.js");
+/**
+ *When stale bot closes the issue this function will
+ *invoke and post CSAT link on the issue.
+ *This function will fetch all the issues closed within 10 minutes and
+ *post the survey link if survey link is not posted already.
+ * @param {!object} Object -  gitHub and context contains the information about
+ *the current and context and github APIs using their built-in library
+ *functions.
+ */
+module.exports = async ({ github, context }) => {
+  let date = new Date();
+  let totalMilliSeconds = date.getTime();
+  let minutes = 10;
+  let millisecondsToSubtract = minutes * 60 * 1000;
+  let closeTime = totalMilliSeconds-millisecondsToSubtract;
+  let newDate = new Date(closeTime);
+  let ISOCloseTime = newDate.toISOString();
+  // Fetch all the issue closed with in 10 mins. 
+  let closeTimeIssues  = await github.rest.issues.listForRepo({
+    owner: context.repo.owner,
+    repo: context.repo.repo,
+    state:"closed", 
+    labels:"stale",
+    since:ISOCloseTime
+  });
+ let ISSUESLIST = closeTimeIssues.data;
+ console.log(`Fetching all the closed within ${minutes} minutes.`);
+ console.log(ISSUESLIST);
+ for(let i=0;i<ISSUESLIST.length;i++){ 
+  if(ISSUESLIST[i].node_id && ISSUESLIST[i].node_id.indexOf("PR") !=-1)
+     continue;
+  // Fetch last comments for the issues.   
+  let comments = await github.rest.issues.listComments({
+    owner: context.repo.owner,
+    repo: context.repo.repo,
+    issue_number: ISSUESLIST[i].number
+  });
+  let noOfComments = comments.data.length;
+  let lastComment = comments.data[noOfComments-1];
+  let strCom = JSON.stringify(lastComment);
+  if(strCom.indexOf(CONSTANT.MODULE.CSAT.MSG) == -1){
+       context.payload.issue = {};
+       context.payload.issue.number = ISSUESLIST[i].number;
+       context.payload.issue.labels = ISSUESLIST[i].labels;
+       context.payload.issue.html_url = ISSUESLIST[i].html_url;
+       csat({github, context});
+  }
+ }
+};

.github/workflows/stale-issues-pr.yml

@@ -48,3 +48,9 @@ jobs:
           close-issue-message: >
             This issue was closed because it has been inactive for more than 1 year.
           repo-token: ${{ secrets.GITHUB_TOKEN }}
+      - uses: actions/checkout@v3
+      - uses: actions/github-script@v6
+        with:
+          script: |
+            const script = require('./\.github/workflows/scripts/stale_csat.js')
+            script({github, context})

examples/graph/gnn_citations.py

@@ -242,7 +242,7 @@ def create_ffn(hidden_units, dropout_rate, name=None):
 ### Prepare the data for the baseline model
 """
 
-feature_names = set(papers.columns) - {"paper_id", "subject"}
+feature_names = list(set(papers.columns) - {"paper_id", "subject"})
 num_features = len(feature_names)
 num_classes = len(class_idx)
 

examples/graph/ipynb/gnn_citations.ipynb

@@ -461,7 +461,7 @@
    },
    "outputs": [],
    "source": [
-    "feature_names = set(papers.columns) - {\"paper_id\", \"subject\"}\n",
+    "feature_names = list(set(papers.columns) - {\"paper_id\", \"subject\"})\n",
     "num_features = len(feature_names)\n",
     "num_classes = len(class_idx)\n",
     "\n",

examples/graph/md/gnn_citations.md

@@ -400,7 +400,7 @@ def create_ffn(hidden_units, dropout_rate, name=None):
 
 
 ```python
-feature_names = set(papers.columns) - {"paper_id", "subject"}
+feature_names = list(set(papers.columns) - {"paper_id", "subject"})
 num_features = len(feature_names)
 num_classes = len(class_idx)
 

examples/nlp/addition_rnn.py

@@ -2,10 +2,11 @@
 Title: Sequence to sequence learning for performing number addition
 Author: [Smerity](https://twitter.com/Smerity) and others
 Date created: 2015/08/17
-Last modified: 2020/04/17
+Last modified: 2024/02/13
 Description: A model that learns to add strings of numbers, e.g. "535+61" -> "596".
 Accelerator: GPU
 """
+
 """
 ## Introduction
 
@@ -208,9 +209,14 @@ def decode(self, x, calc_argmax=True):
 ## Train the model
 """
 
+# Training parameters.
 epochs = 30
 batch_size = 32
 
+# Formatting characters for results display.
+green_color = "\033[92m"
+red_color = "\033[91m"
+end_char = "\033[0m"
 
 # Train the model each generation and show predictions against the validation
 # dataset.
@@ -229,23 +235,17 @@ def decode(self, x, calc_argmax=True):
     for i in range(10):
         ind = np.random.randint(0, len(x_val))
         rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]
-        preds = np.argmax(model.predict(rowx), axis=-1)
+        preds = np.argmax(model.predict(rowx, verbose=0), axis=-1)
         q = ctable.decode(rowx[0])
         correct = ctable.decode(rowy[0])
         guess = ctable.decode(preds[0], calc_argmax=False)
         print("Q", q[::-1] if REVERSE else q, end=" ")
         print("T", correct, end=" ")
         if correct == guess:
-            print("☑ " + guess)
+            print(f"{green_color}☑ {guess}{end_char}")
         else:
-            print("☒ " + guess)
+            print(f"{red_color}☒ {guess}{end_char}")
 
 """
 You'll get to 99+% validation accuracy after ~30 epochs.
-
-Example available on HuggingFace.
-
-| Trained Model | Demo |
-| :--: | :--: |
-| [![Generic badge](https://img.shields.io/badge/🤗%20Model-Addition%20LSTM-black.svg)](https://huggingface.co/keras-io/addition-lstm) | [![Generic badge](https://img.shields.io/badge/🤗%20Spaces-Addition%20LSTM-black.svg)](https://huggingface.co/spaces/keras-io/addition-lstm) |
 """

examples/nlp/ipynb/addition_rnn.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "**Author:** [Smerity](https://twitter.com/Smerity) and others<br>\n",
     "**Date created:** 2015/08/17<br>\n",
-    "**Last modified:** 2020/04/17<br>\n",
+    "**Last modified:** 2024/02/13<br>\n",
     "**Description:** A model that learns to add strings of numbers, e.g. \"535+61\" -> \"596\"."
    ]
   },
@@ -290,9 +290,14 @@
    },
    "outputs": [],
    "source": [
+    "# Training parameters.\n",
     "epochs = 30\n",
     "batch_size = 32\n",
     "\n",
+    "# Formatting characters for results display.\n",
+    "green_color = \"\\033[92m\"\n",
+    "red_color = \"\\033[91m\"\n",
+    "end_char = \"\\033[0m\"\n",
     "\n",
     "# Train the model each generation and show predictions against the validation\n",
     "# dataset.\n",
@@ -311,16 +316,16 @@
     "    for i in range(10):\n",
     "        ind = np.random.randint(0, len(x_val))\n",
     "        rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]\n",
-    "        preds = np.argmax(model.predict(rowx), axis=-1)\n",
+    "        preds = np.argmax(model.predict(rowx, verbose=0), axis=-1)\n",
     "        q = ctable.decode(rowx[0])\n",
     "        correct = ctable.decode(rowy[0])\n",
     "        guess = ctable.decode(preds[0], calc_argmax=False)\n",
     "        print(\"Q\", q[::-1] if REVERSE else q, end=\" \")\n",
     "        print(\"T\", correct, end=\" \")\n",
     "        if correct == guess:\n",
-    "            print(\"\u2611 \" + guess)\n",
+    "            print(f\"{green_color}\u2611 {guess}{end_char}\")\n",
     "        else:\n",
-    "            print(\"\u2612 \" + guess)"
+    "            print(f\"{red_color}\u2612 {guess}{end_char}\")"
    ]
   },
   {
@@ -329,13 +334,7 @@
     "colab_type": "text"
    },
    "source": [
-    "You'll get to 99+% validation accuracy after ~30 epochs.\n",
-    "\n",
-    "Example available on HuggingFace.\n",
-    "\n",
-    "| Trained Model | Demo |\n",
-    "| :--: | :--: |\n",
-    "| [![Generic badge](https://img.shields.io/badge/\ud83e\udd17%20Model-Addition%20LSTM-black.svg)](https://huggingface.co/keras-io/addition-lstm) | [![Generic badge](https://img.shields.io/badge/\ud83e\udd17%20Spaces-Addition%20LSTM-black.svg)](https://huggingface.co/spaces/keras-io/addition-lstm) |"
+    "You'll get to 99+% validation accuracy after ~30 epochs."
    ]
   }
  ],