feat: add AlphaDropout layer with hot-pink node color

Backend:
- Add customalphadropout to model_generation._build_layer()
- Validates rate in (0, 1) exclusive — strict bounds for AlphaDropout
- Falls back to rate=0.5 on empty input
- 9 unit tests: valid rates, edge cases, invalid inputs, full model pipeline

Frontend:
- AlphaDropoutNode component (hot-pink: rgb(220,53,153) / header rgb(190,24,93))
- Color token: node-alphadropout in tailwind.config.js
- Registered in Canvas.jsx nodeTypes and defaultParams
- Added to Sidebar.jsx layer palette
- NodePropertiesPanel.jsx config with SELU usage hint
- Helpers.jsx validation — rate must be in (0,1) exclusive
- 8 frontend tests covering render, configured, not-configured states

All 171 backend tests pass. All 50 frontend tests pass.

Author: Sickwoman

tensormap-backend/app/services/model_generation.py

@@ -120,5 +120,16 @@ def _build_layer(node: dict, input_tensor):
             raise ValueError(f"Dropout rate must be in [0, 1), got {rate!r}.")
         return tf.keras.layers.Dropout(rate=rate, name=name)(input_tensor)
 
+    elif node_type == "customalphadropout":
+        raw = params.get("rate", "") if params.get("rate") is not None else ""
+        raw = str(raw).strip()
+        try:
+            rate = float(raw) if raw != "" else 0.5
+        except (ValueError, TypeError) as err:
+            raise ValueError(f"AlphaDropout rate must be a number in (0, 1), got {raw!r}.") from err
+        if not 0.0 < rate < 1.0:
+            raise ValueError(f"AlphaDropout rate must be strictly between 0 and 1, got {rate!r}.")
+        return tf.keras.layers.AlphaDropout(rate=rate, name=name)(input_tensor)
+
     else:
         raise ValueError(f"Unknown node type: {node_type}")

tensormap-backend/tests/test_alphadropout_layer.py

@@ -0,0 +1,91 @@
+"""Unit tests for AlphaDropout layer in model_generation._build_layer()."""
+
+import pytest
+import tensorflow as tf
+
+from app.services.model_generation import _build_layer
+
+
+def _make_node(rate: str) -> dict:
+    """Helper to create an AlphaDropout node dict."""
+    return {
+        "id": "alphadropout-test",
+        "type": "customalphadropout",
+        "data": {"params": {"rate": rate}},
+    }
+
+
+def test_build_alphadropout_basic():
+    """AlphaDropout with valid rate builds correctly."""
+    input_tensor = tf.keras.Input(shape=(10,))
+    node = _make_node("0.5")
+    output = _build_layer(node, input_tensor)
+    assert output.shape[-1] == 10
+
+
+def test_build_alphadropout_rate_0_1():
+    """AlphaDropout with low rate 0.1."""
+    input_tensor = tf.keras.Input(shape=(20,))
+    node = _make_node("0.1")
+    output = _build_layer(node, input_tensor)
+    assert output.shape == (None, 20)
+
+
+def test_build_alphadropout_rate_0_9():
+    """AlphaDropout with high rate 0.9."""
+    input_tensor = tf.keras.Input(shape=(15,))
+    node = _make_node("0.9")
+    output = _build_layer(node, input_tensor)
+    assert output.shape == (None, 15)
+
+
+def test_build_alphadropout_default_rate():
+    """AlphaDropout with empty rate falls back to default 0.5."""
+    input_tensor = tf.keras.Input(shape=(8,))
+    node = _make_node("")
+    output = _build_layer(node, input_tensor)
+    assert output.shape[-1] == 8
+
+
+def test_build_alphadropout_invalid_rate_zero():
+    """AlphaDropout with rate=0 raises ValueError."""
+    input_tensor = tf.keras.Input(shape=(10,))
+    node = _make_node("0")
+    with pytest.raises((ValueError, Exception)):
+        _build_layer(node, input_tensor)
+
+
+def test_build_alphadropout_invalid_rate_negative():
+    """AlphaDropout with negative rate raises ValueError."""
+    input_tensor = tf.keras.Input(shape=(10,))
+    node = _make_node("-0.5")
+    with pytest.raises((ValueError, Exception)):
+        _build_layer(node, input_tensor)
+
+
+def test_build_alphadropout_invalid_rate_over_one():
+    """AlphaDropout with rate >= 1 raises ValueError."""
+    input_tensor = tf.keras.Input(shape=(10,))
+    node = _make_node("1.5")
+    with pytest.raises((ValueError, Exception)):
+        _build_layer(node, input_tensor)
+
+
+def test_build_alphadropout_invalid_rate_nan():
+    """AlphaDropout with non-numeric rate raises ValueError."""
+    input_tensor = tf.keras.Input(shape=(10,))
+    node = _make_node("abc")
+    with pytest.raises((ValueError, Exception)):
+        _build_layer(node, input_tensor)
+
+
+def test_build_alphadropout_in_model():
+    """AlphaDropout can be used in a full model pipeline."""
+    inputs = tf.keras.Input(shape=(16,))
+    node = _make_node("0.3")
+    dropped = _build_layer(node, inputs)
+    outputs = tf.keras.layers.Dense(1)(dropped)
+    model = tf.keras.Model(inputs=inputs, outputs=outputs)
+    model.compile(optimizer="adam", loss="mse")
+    assert model.layers[1].__class__.__name__ == "AlphaDropout"
+    assert model.layers[1].rate == pytest.approx(0.3)

tensormap-frontend/src/components/DragAndDropCanvas/Canvas.jsx

@@ -30,6 +30,7 @@ import DenseNode from "./CustomNodes/DenseNode/DenseNode";
 import FlattenNode from "./CustomNodes/FlattenNode/FlattenNode";
 import ConvNode from "./CustomNodes/ConvNode/ConvNode";
 import DropoutNode from "./CustomNodes/DropoutNode/DropoutNode";
+import AlphaDropoutNode from "./CustomNodes/AlphaDropoutNode/AlphaDropoutNode";
 import MaxPoolingNode from "./CustomNodes/MaxPoolingNode/MaxPoolingNode";
 import Sidebar from "./Sidebar";
 import NodePropertiesPanel from "./NodePropertiesPanel";
@@ -54,6 +55,7 @@ const nodeTypes = {
   customflatten: FlattenNode,
   customconv: ConvNode,
   customdropout: DropoutNode,
+  customalphadropout: AlphaDropoutNode,
   custommaxpool: MaxPoolingNode,
   customglobalavgpool: GlobalAvgPoolNode,
 };
@@ -537,6 +539,7 @@ function Canvas() {
           kernelY: "",
         },
         customdropout: { rate: "" },
+        customalphadropout: { rate: "" },
         custommaxpool: { pool_size: "", stride: "", padding: "valid" },
         customglobalavgpool: {},
       };

tensormap-frontend/src/components/DragAndDropCanvas/CustomNodes/AlphaDropoutNode/AlphaDropoutNode.jsx

@@ -0,0 +1,32 @@
+import PropTypes from "prop-types";
+import { Handle, Position } from "reactflow";
+
+function AlphaDropoutNode({ data, id }) {
+  const rate = data.params?.rate ?? "";
+  const configured =
+    String(rate).trim() !== "" && !isNaN(Number(rate)) && Number(rate) > 0 && Number(rate) < 1;
+
+  return (
+    <div className="w-56 rounded-lg border bg-white shadow-sm">
+      <Handle type="target" position={Position.Left} isConnectable id={`${id}_in`} />
+      <div className="rounded-t-lg bg-node-alphadropout px-3 py-1.5 text-xs font-bold text-white">
+        AlphaDropout
+      </div>
+      <div className="px-3 py-2 text-xs text-muted-foreground">
+        {configured ? `Rate: ${rate}` : "Not configured"}
+      </div>
+      <Handle type="source" position={Position.Right} isConnectable id={`${id}_out`} />
+    </div>
+  );
+}
+
+AlphaDropoutNode.propTypes = {
+  data: PropTypes.shape({
+    params: PropTypes.shape({
+      rate: PropTypes.oneOfType([PropTypes.string, PropTypes.number]),
+    }).isRequired,
+  }).isRequired,
+  id: PropTypes.string.isRequired,
+};
+
+export default AlphaDropoutNode;

tensormap-frontend/src/components/DragAndDropCanvas/CustomNodes/AlphaDropoutNode/AlphaDropoutNode.test.jsx

@@ -0,0 +1,55 @@
+import { describe, it, expect } from "vitest";
+import { render, screen } from "@testing-library/react";
+import { ReactFlowProvider } from "reactflow";
+import AlphaDropoutNode from "./AlphaDropoutNode";
+
+const renderNode = (params = {}) =>
+  render(
+    <ReactFlowProvider>
+      <AlphaDropoutNode id="ad-test" data={{ params: { rate: "0.5", ...params } }} />
+    </ReactFlowProvider>,
+  );
+
+describe("AlphaDropoutNode", () => {
+  it("renders the AlphaDropout label", () => {
+    renderNode();
+    expect(screen.getByText("AlphaDropout")).toBeDefined();
+  });
+
+  it("displays rate when set", () => {
+    renderNode({ rate: "0.3" });
+    expect(screen.getByText("Rate: 0.3")).toBeDefined();
+  });
+
+  it("shows Not configured when rate is empty", () => {
+    renderNode({ rate: "" });
+    expect(screen.getByText("Not configured")).toBeDefined();
+  });
+
+  it("shows Not configured when rate is invalid", () => {
+    renderNode({ rate: "abc" });
+    expect(screen.getByText("Not configured")).toBeDefined();
+  });
+
+  it("shows Not configured when rate is 0", () => {
+    renderNode({ rate: "0" });
+    expect(screen.getByText("Not configured")).toBeDefined();
+  });
+
+  it("shows Not configured when rate is > 1", () => {
+    renderNode({ rate: "1.5" });
+    expect(screen.getByText("Not configured")).toBeDefined();
+  });
+
+  it("renders a target handle on the left side", () => {
+    const { container } = renderNode();
+    const left = container.querySelector("[data-handlepos='left']");
+    expect(left).not.toBeNull();
+  });
+
+  it("renders a source handle on the right side", () => {
+    const { container } = renderNode();
+    const right = container.querySelector("[data-handlepos='right']");
+    expect(right).not.toBeNull();
+  });
+});

tensormap-frontend/src/components/DragAndDropCanvas/Helpers.jsx

@@ -40,8 +40,13 @@ export const canSaveModel = (modelName, modelData) => {
       if (node.data.params.rate === "" || isNaN(rate) || rate < 0 || rate >= 1) {
         return false;
       }
+    } else if (node.type === "customalphadropout") {
+      const rate = parseFloat(node.data.params.rate);
+      if (node.data.params.rate === "" || isNaN(rate) || rate <= 0 || rate >= 1) {
+        return false;
+      }
     }
-    // customflatten and customdropout have no required params to validate
+    // customflatten has no required params to validate; customdropout rate is validated above
   }
   return isGraphConnected(modelData);
 };

tensormap-frontend/src/components/DragAndDropCanvas/NodePropertiesPanel.jsx

@@ -282,6 +282,33 @@ function NodePropertiesPanel({
     );
   }
 
+  if (type === "customalphadropout") {
+    return (
+      <Card className="h-fit">
+        <CardHeader>
+          <CardTitle className="text-sm">AlphaDropout</CardTitle>
+        </CardHeader>
+        <CardContent className="space-y-3">
+          <div className="space-y-1">
+            <Label>Rate (exclusive: 0 &lt; rate &lt; 1)</Label>
+            <Input
+              type="number"
+              min="0.001"
+              max="0.999"
+              step="0.01"
+              value={params.rate ?? ""}
+              onChange={(e) => doUpdate("rate", e.target.value)}
+              placeholder="0.5"
+            />
+            <p className="text-xs text-muted-foreground">
+              Keeps mean and variance of inputs. Best used with SELU activations.
+            </p>
+          </div>
+        </CardContent>
+      </Card>
+    );
+  }
+
   if (type === "customdropout") {
     return (
       <Card className="h-fit">
@@ -290,14 +317,14 @@ function NodePropertiesPanel({
         </CardHeader>
         <CardContent className="space-y-3">
           <div className="space-y-1">
-            <Label>Rate (0–1)</Label>
+            <Label>Rate (0 ≤ rate &lt; 1)</Label>
             <Input
               type="number"
               min="0"
-              max="1"
-              step="0.1"
-              value={params.rate}
-              onChange={(e) => updateParam("rate", e.target.value)}
+              max="0.999"
+              step="0.01"
+              value={params.rate ?? ""}
+              onChange={(e) => doUpdate("rate", e.target.value)}
             />
           </div>
         </CardContent>

tensormap-frontend/src/components/DragAndDropCanvas/Sidebar.jsx

@@ -45,6 +45,13 @@ function Sidebar() {
         >
           Dropout
         </div>
+        <div
+          className="cursor-grab rounded-md border border-l-4 border-l-node-alphadropout bg-white px-3 py-2 text-xs font-medium"
+          onDragStart={(e) => onDragStart(e, "customalphadropout")}
+          draggable
+        >
+          AlphaDropout
+        </div>
         <div
           className="cursor-grab rounded-md border border-l-4 border-l-node-conv bg-white px-3 py-2 text-xs font-medium"
           onDragStart={(e) => onDragStart(e, "custommaxpool")}

tensormap-frontend/tailwind.config.js

@@ -44,6 +44,7 @@ export default {
         "node-flatten": { DEFAULT: "rgb(247, 173, 20)", header: "rgb(170, 121, 24)" },
         "node-conv": { DEFAULT: "rgb(255, 128, 43)", header: "rgb(255, 128, 43)" },
         "node-dropout": { DEFAULT: "rgb(220, 80, 80)", header: "rgb(180, 50, 50)" },
+        "node-alphadropout": { DEFAULT: "rgb(220, 53, 153)", header: "rgb(190, 24, 93)" },
         "node-maxpool": { DEFAULT: "rgb(34, 182, 176)", header: "rgb(20, 140, 135)" },
       },
       borderRadius: {