Data Scientist at Warwick Investment Group. Passionate about science, predictive modeling, and data visualizations.
async function createModel() {
const model = tf.sequential()
model.add(tf.layers.conv2d({
inputShape: [28, 28, 1],
kernelSize: 5,
filters: 8,
strides: 1,
activation: 'relu',
kernelInitializer: 'VarianceScaling'
}))
model.add(tf.layers.maxPooling2d({
poolSize: [2, 2],
strides: [2, 2]
}))
model.add(tf.layers.conv2d({
kernelSize: 5,
filters: 16,
strides: 1,
activation: 'relu',
kernelInitializer: 'VarianceScaling'
}))
model.add(tf.layers.maxPooling2d({
poolSize: [2, 2],
strides: [2, 2]
}))
model.add(tf.layers.flatten())
model.add(tf.layers.dense({
units: 10,
kernelInitializer: 'VarianceScaling',
activation: 'softmax',
// useBias: false
}))
const optimizer = tf.train.sgd(LEARNING_RATE)
await model.compile({
optimizer: optimizer,
loss: 'categoricalCrossentropy',
metrics: ['accuracy']
})
return model
}
function readTopology({ model }) {
const input = model.inputs[0]
let inputNodes = 1
let size = IMG_SIZE * IMG_SIZE
const layers = [{
nodes: inputNodes,
outSize: size,
outShape: input.shape.slice(1),
name: 'input',
}]
for (let tfLayer of model.layers) {
const name = tfLayer.name
const nodes = tfLayer.filters || tfLayer.units || (name.match(/flatten/) ? 1 : inputNodes)
const layer = {
nodes,
inSize: size
}
// don't know why but the output of conv2d layers seem to always loose 4px size
if (name.match(/^conv2d/)) {
size = Math.pow(Math.sqrt(size) - 4, 2)
}
// max sample half (not supporting other configurations)
if (name.match(/^max_pooling2d/)) {
size = Math.pow(Math.sqrt(size) / 2, 2)
}
if (name.match(/^flatten/)) {
size = inputNodes * size
}
if (name.match(/^dense/)) {
size = 1
}
layer.outSize = size
layer.outShape = tfLayer.output.shape.slice(1)
layer.name = name
if (tfLayer.useBias) {
layer.bias = tfLayer.bias.val.dataSync()
}
if (tfLayer.kernel) {
const kernelVal = tfLayer.kernel.val
// expect weights per node
if (nodes > 1 && kernelVal.shape[kernelVal.shape.length - 1] !== nodes) {
console.warn('unexpected kernel val shape, ignoring weights')
return
}
const weightsPerNode = nodes > 1
? tf.split(kernelVal, nodes, kernelVal.shape.length - 1).map(val => val.squeeze())
: [tfLayer.kernel.val]
layer.weights = weightsPerNode
.map(perNode => {
if (inputNodes > 1) {
// expect weights per input node
if (inputNodes > 1 && perNode.shape[perNode.shape.length - 1] !== inputNodes) {
console.warn('unexpected kernel val shape, ignoring weights')
return
}
return tf.split(perNode, inputNodes, perNode.shape.length - 1).map(val => val.squeeze())
}
return [perNode]
})
.map(vals => vals.map(val => val.dataSync()))
// layer.weightExtent = layer.weights.map(n => n.map(i => d3.extent(i)))
// layer.weightSums = layer.weights.map(n => n.map(i => d3.sum(i)))
}
layers.push(layer)
inputNodes = nodes
}
return layers
}
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
