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Integrate skin disease classification task
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| import * as tf from '@tensorflow/tfjs' | ||
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| import type { Model, Task, TaskProvider } from '../index.js' | ||
| import { data, models } from '../index.js' | ||
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| export const skinMnist: TaskProvider = { | ||
| getTask (): Task { | ||
| return { | ||
| id: 'skin_mnist', | ||
| displayInformation: { | ||
| taskTitle: 'Skin Disease Classification', | ||
| summary: { | ||
| preview: 'Can you determine the skin disease from the dermatoscopic images?', | ||
| overview: 'HAM10000 "Human Against Machine with 10000 training images" dataset is a large collection of multi-source dermatoscopic images of pigmented lesions from Kaggle' | ||
| }, | ||
| dataFormatInformation: '', | ||
| dataExampleText: 'Below you find an example', | ||
| dataExampleImage: 'http://walidbn.com/ISIC_0024306.jpg' | ||
| }, | ||
| trainingInformation: { | ||
| modelID: 'skin-mnist-model', | ||
| epochs: 50, | ||
| roundDuration: 2, | ||
| validationSplit: 0.2, | ||
| batchSize: 5, | ||
| preprocessingFunctions: [data.ImagePreprocessing.Resize, data.ImagePreprocessing.Normalize], | ||
| dataType: 'image', | ||
| IMAGE_H: 28, | ||
| IMAGE_W: 28, | ||
| LABEL_LIST: ['nv', 'vasc', 'mel', 'bkl', 'df', 'akiec', 'bcc'], | ||
| scheme: 'federated', | ||
| noiseScale: undefined, | ||
| clippingRadius: undefined | ||
| } | ||
| } | ||
| }, | ||
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| // async getModel (): Promise<Model> { | ||
| // const imageHeight = 28 | ||
| // const imageWidth = 28 | ||
| // const imageChannels = 3 | ||
| // const numOutputClasses = 7 | ||
| // const model = tf.sequential() | ||
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| // // In the first layer of our convolutional neural network we have | ||
| // // to specify the input shape. Then we specify some parameters for | ||
| // // the convolution operation that takes place in this layer. | ||
| // model.add(tf.layers.conv2d({ | ||
| // inputShape: [imageHeight, imageWidth, imageChannels], | ||
| // kernelSize: 5, | ||
| // filters: 8, | ||
| // strides: 1, | ||
| // activation: 'relu', | ||
| // kernelInitializer: 'varianceScaling' | ||
| // })) | ||
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| // // The MaxPooling layer acts as a sort of downsampling using max values | ||
| // // in a region instead of averaging. | ||
| // model.add(tf.layers.maxPooling2d({ poolSize: [2, 2], strides: [2, 2] })) | ||
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| // // Repeat the conv2d + maxPooling block. | ||
| // // Note that we have more filters in the convolution. | ||
| // 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] })) | ||
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| // // Now we flatten the output from the 2D filters into a 1D vector to prepare | ||
| // // it for input into our last layer. This is common practice when feeding | ||
| // // higher dimensional data to a final classification output layer. | ||
| // model.add(tf.layers.flatten()) | ||
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| // // Our last layer is a dense layer which has 2 output units, one for each | ||
| // // output class. | ||
| // model.add(tf.layers.dense({ | ||
| // units: numOutputClasses, | ||
| // kernelInitializer: 'varianceScaling', | ||
| // activation: 'softmax' | ||
| // })) | ||
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| // model.compile({ | ||
| // optimizer: 'adam', | ||
| // loss: 'categoricalCrossentropy', | ||
| // metrics: ['accuracy'] | ||
| // }) | ||
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| // return Promise.resolve(new models.TFJS(model)) | ||
| // } | ||
| async getModel(): Promise<Model> { | ||
| const imageHeight = 28 | ||
| const imageWidth = 28 | ||
| const imageChannels = 3 | ||
| const numOutputClasses = 7 | ||
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| const model = tf.sequential() | ||
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| model.add( | ||
| tf.layers.conv2d({ | ||
| inputShape: [imageHeight, imageWidth, imageChannels], | ||
| filters: 256, | ||
| kernelSize: 3, | ||
| activation: 'relu' | ||
| }) | ||
| ) | ||
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| model.add(tf.layers.maxPooling2d({ poolSize: [2, 2] })) | ||
| model.add(tf.layers.dropout({ rate: 0.3 })) | ||
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| model.add( | ||
| tf.layers.conv2d({ | ||
| filters: 128, | ||
| kernelSize: 3, | ||
| activation: 'relu' | ||
| }) | ||
| ) | ||
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| model.add(tf.layers.maxPooling2d({ poolSize: [2, 2] })) | ||
| model.add(tf.layers.dropout({ rate: 0.3 })) | ||
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| model.add( | ||
| tf.layers.conv2d({ | ||
| filters: 64, | ||
| kernelSize: 3, | ||
| activation: 'relu' | ||
| }) | ||
| ) | ||
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| model.add(tf.layers.maxPooling2d({ poolSize: [2, 2] })) | ||
| model.add(tf.layers.dropout({ rate: 0.3 })) | ||
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| model.add(tf.layers.flatten()) | ||
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| model.add(tf.layers.dense({ units: 32 })) | ||
| model.add(tf.layers.dense({ units: numOutputClasses, activation: 'softmax' })) | ||
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| model.compile({ | ||
| optimizer: tf.train.adam(0.00001), | ||
| loss: 'categoricalCrossentropy', | ||
| metrics: ['accuracy'] | ||
| }) | ||
| return Promise.resolve(new models.TFJS(model)) | ||
| } | ||
| } |
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