--- a
+++ b/ECG/ConvModel.swift
@@ -0,0 +1,74 @@
+//
+//  ConvModel.swift
+//  ECG
+//
+//  Created by Dave Fernandes on 2019-03-04.
+//  Copyright © 2019 MintLeaf Software Inc. All rights reserved.
+//
+
+// Model is from: https://arxiv.org/pdf/1805.00794.pdf
+
+import TensorFlow
+
+public struct ConvUnit<Scalar: TensorFlowFloatingPoint> : Layer {
+    var conv1: Conv1D<Scalar>
+    var conv2: Conv1D<Scalar>
+    var pool: MaxPool1D<Scalar>
+    
+    public init(kernelSize: Int, channels: Int) {
+        conv1 = Conv1D<Scalar>(filterShape: (kernelSize, channels, channels), padding: .same, activation: relu)
+        conv2 = Conv1D<Scalar>(filterShape: (kernelSize, channels, channels), padding: .same)
+        pool = MaxPool1D<Scalar>(poolSize: kernelSize, stride: 2, padding: .valid)
+    }
+    
+    @differentiable
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+        var tmp = input.sequenced(through: conv1, conv2)
+        tmp = pool(relu(tmp + input))
+        return tmp
+    }
+}
+
+public struct ConvModel : Layer {
+    var conv1: Conv1D<Float>
+    var convUnit = [ConvUnit<Float>]()
+    var dense1: Dense<Float>
+    var dense2: Dense<Float>
+    
+    @noDerivative let convUnitCount = 5
+    
+    public init() {
+        conv1 = Conv1D<Float>(filterShape: (5, 1, 32), stride: 1, padding: .same)
+        for _ in 0..<convUnitCount {
+            convUnit.append(ConvUnit<Float>(kernelSize: 5, channels: 32))
+        }
+        dense1 = Dense<Float>(inputSize: 64, outputSize: 32, activation: relu)
+        dense2 = Dense<Float>(inputSize: 32, outputSize: 5)
+    }
+    
+    @differentiable
+    public func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
+        var tmp = conv1(input.expandingShape(at: 2))
+        
+        for i in 0..<convUnitCount {
+            let unit = convUnit[i]
+            tmp = unit(tmp)
+        }
+        
+        tmp = tmp.reshaped(to: [-1, 64])
+        tmp = tmp.sequenced(through: dense1, dense2)
+        return tmp
+    }
+    
+    public func predictedClasses(for input: Tensor<Float>) -> Tensor<Int32> {
+        return model.inferring(from: input).argmax(squeezingAxis: 1)
+    }
+}
+
+typealias ECGModel = ConvModel
+
+@differentiable(wrt: model)
+func loss(model: ECGModel, examples: Example) -> Tensor<Float> {
+    let logits = model(examples.series)
+    return softmaxCrossEntropy(logits: logits, labels: examples.labels)
+}