1 files changed, 143 insertions, 0 deletions
diff --git a/app/client/modules/samplernn/samplernn.loss.js b/app/client/modules/samplernn/samplernn.loss.js
new file mode 100644
index 0000000..3900c31
--- /dev/null
+++ b/app/client/modules/samplernn/samplernn.loss.js
@@ -0,0 +1,143 @@
+import { h, Component } from 'preact'
+import { bindActionCreators } from 'redux'
+import { connect } from 'react-redux'
+
+import { lerp, norm, randint, randrange } from '../../util'
+
+import * as samplernnActions from './samplernn.actions'
+
+import Dataset from '../../dataset/dataset.component'
+
+import Group from '../../common/group.component'
+import Slider from '../../common/slider.component'
+import Select from '../../common/select.component'
+import Button from '../../common/button.component'
+import { FileList } from '../../common/fileList.component'
+import TextInput from '../../common/textInput.component'
+
+class SampleRNNLoss extends Component {
+  constructor(props){
+    super()
+    props.actions.load_loss()
+  }
+  render(){
+    this.refs = {}
+    return (
+      <div className='app lossGraph'>
+        <div className='heading'>
+          <h3>SampleRNN Loss</h3>
+          <canvas ref={(ref) => this.refs['canvas'] = ref} />
+        </div>
+      </div>
+    )
+  }
+  componentDidUpdate(){
+    const { lossReport } = this.props.samplernn
+    if (! lossReport) return
+    const canvas = this.refs.canvas
+    canvas.width = window.innerWidth
+    canvas.height = window.innerHeight
+    canvas.style.width = canvas.width + 'px'
+    canvas.style.height = canvas.height + 'px'
+
+    const ctx = canvas.getContext('2d')
+    const w = canvas.width = canvas.width * devicePixelRatio
+    const h = canvas.height = canvas.height * devicePixelRatio
+
+    const keys = Object.keys(lossReport).sort().filter(k => !!lossReport[k].length)
+    let scaleMax = 0
+    let scaleMin = Infinity
+    let epochsMax = 0
+    keys.forEach(key => {
+      const loss = lossReport[key]
+      epochsMax = Math.max(loss.length, epochsMax)
+      loss.forEach((a) => {
+        const v = parseFloat(a.training_loss)
+        if (! v) return
+        scaleMax = Math.max(v, scaleMax)
+        scaleMin = Math.min(v, scaleMin)
+      })
+    })
+    // scaleMax *= 10
+    console.log(scaleMax, scaleMin, epochsMax)
+
+    scaleMax = 3
+    scaleMin = 0
+    const margin = 0
+    const wmin = 0
+    const wmax = w
+    const hmin = 0
+    const hmax = h
+    const epochsScaleFactor = 1 // 3/2
+
+    let X, Y
+    for (var ii = 0; ii < epochsMax; ii++) {
+      X = lerp((ii)/(epochsMax/(epochsScaleFactor))*(epochsScaleFactor), wmin, wmax)
+      ctx.strokeStyle = 'rgba(0,0,0,0.3)'
+      ctx.beginPath(0, 0)
+      ctx.moveTo(X, 0)
+      ctx.lineTo(X, h)
+      ctx.lineWidth = 1
+      // ctx.stroke()
+      if ( (ii % 5) === 0 ) {
+        ctx.lineWidth = 2
+        ctx.stroke()
+      }
+    }
+    for (var ii = scaleMin; ii < scaleMax; ii += 1) {
+      Y = lerp(ii/scaleMax, wmin, wmax)
+      // ctx.strokeStyle = 'rgba(255,255,255,1.0)'
+      ctx.beginPath(0, 0)
+      ctx.moveTo(0, (h-Y))
+      ctx.lineTo(w, (h-Y))
+      ctx.lineWidth = 1
+      // ctx.stroke()        
+      // if ( (ii % 1) < 0.1) {
+        // ctx.strokeStyle = 'rgba(255,255,255,1.0)'
+        ctx.lineWidth = 2
+        ctx.stroke()        
+        ctx.stroke()        
+        ctx.stroke()        
+      // }
+    }
+    ctx.lineWidth = 1
+
+    keys.forEach(key => {
+      const loss = lossReport[key]
+      const vf = parseFloat(loss[loss.length-1].training_loss) || 0
+      const vg = parseFloat(loss[0].training_loss) || 5
+      console.log(vf)
+      const vv = 1 - norm(vf, scaleMin, scaleMax/2)
+      ctx.lineWidth = (1-norm(vf, scaleMin, scaleMax)) * 5
+      ctx.strokeStyle = 'rgba(' + [randrange(30,190), randrange(30,150), randrange(60,120)].join(',') + ',' + 0.8+ ')'
+      let begun = false
+      loss.forEach((a, i) => {
+        const v = parseFloat(a.training_loss)
+        if (! v) return
+        const x = lerp((i-2)/(epochsMax/(epochsScaleFactor))*(epochsScaleFactor), wmin, wmax)
+        const y = lerp(norm(v, scaleMin, scaleMax), hmax, hmin)
+        if (i === 0) {
+          return
+        }
+        if (! begun) {
+          begun = true
+          ctx.beginPath(x,y)
+        } else {
+          ctx.lineTo(x,y)          
+          // ctx.stroke()
+        }
+      })
+      ctx.stroke()
+    })
+  }
+}
+
+const mapStateToProps = state => ({
+  samplernn: state.module.samplernn,
+})
+
+const mapDispatchToProps = (dispatch, ownProps) => ({
+  actions: bindActionCreators(samplernnActions, dispatch),
+})
+
+export default connect(mapStateToProps, mapDispatchToProps)(SampleRNNLoss)