diff --git a/README.md b/README.md
index 156b515..04a1257 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
# [Keras.js](https://transcranial.github.io/keras-js)
-Run trained [Keras](https://github.com/fchollet/keras) models in your browser, GPU-powered using WebGL. Models are serialized directly from the Keras JSON-format configuration file and associated HDF5 weights.
+Run [Keras](https://github.com/fchollet/keras) models (trained using Tensorflow backend) in your browser, with GPU support. Models are serialized directly from the Keras JSON-format configuration file and associated HDF5 weights. GPU support is powered by WebGL through [weblas](https://github.com/waylonflinn/weblas).
### [Interactive Demos](https://transcranial.github.io/keras-js)
@@ -15,25 +15,17 @@ Run trained [Keras](https://github.com/fchollet/keras) models in your browser, G
- Bidirectional LSTM for IMDB sentiment classification
- 
- 
- 
- 
+
+
+
+
-### Why?
-
-- Eliminate need for backend infrastructure or API calls
-
-- Offload computation entirely to client browsers
-
-- Interactive apps
-
### Usage
-See `demos/src/` for source code of real examples.
+See `demos/src/` for source code of real examples written in VueJS.
-1. Works for both `Model` and `Sequential`:
+1. Works for models based on both `Model` and `Sequential` classes:
```py
model = Sequential()
@@ -72,7 +64,7 @@ See `demos/src/` for source code of real examples.
- the weights metadata file: `model_metadata.json`
-4. GPU support is powered by [weblas](https://github.com/waylonflinn/weblas). Include the Keras.js and Weblas libraries:
+4. Include both the Keras.js and Weblas libraries:
```html
@@ -81,7 +73,7 @@ See `demos/src/` for source code of real examples.
5. Create new model
- On instantiation, data is loaded over XHR (same-domain or CORS required), and layers are initialized as directed acyclic graph. Class method `ready()` returns a Promise which resolves when these steps are complete. Then, use `predict()` to run data through the model, which also returns a Promise.
+ On instantiation, data is loaded using XHR (same-domain or CORS required), and layers are initialized as a directed acyclic graph:
```js
const model = new KerasJS.Model({
@@ -89,65 +81,92 @@ See `demos/src/` for source code of real examples.
model: 'url/path/to/model.json',
weights: 'url/path/to/model_weights.buf',
metadata: 'url/path/to/model_metadata.json'
- }
- ,gpu: true
+ },
+ gpu: true
})
+ ```
- model.ready().then(() => {
+ Class method `ready()` returns a Promise which resolves when these steps are complete. Then, use `predict()` to run data through the model, which also returns a Promise:
- // input data object keyed by names of the input layers
- // or `input` for Sequential models
- // values are the flattened Float32Array data
- // (input tensor shapes are specified in the model config)
+ ```js
+ model.ready()
+ .then(() => {
+ // input data object keyed by names of the input layers
+ // or `input` for Sequential models
+ // values are the flattened Float32Array data
+ // (input tensor shapes are specified in the model config)
+ const inputData = {
+ 'input_1': new Float32Array(data)
+ }
+
+ // make predictions
+ // outputData is an object keyed by names of the output layers
+ // or `output` for Sequential models
+ model.predict(inputData)
+ .then(outputData => {
+ // e.g.,
+ // outputData['fc1000']
+ })
+ .catch(err => {
+ // handle error
+ }
+ })
+ .catch(err => {
+ // handle error
+ }
+ ```
+
+ Alternatively, we could also use async/await:
+
+ ```js
+ try {
+ await model.ready()
const inputData = {
'input_1': new Float32Array(data)
}
-
- // make predictions
- // outputData is an object keyed by names of the output layers
- // or `output` for Sequential models
- model.predict(inputData).then(outputData => {
- // e.g.,
- // outputData['fc1000']
- })
- })
+ const outputData = await model.predict(inputData)
+ } catch (err) {
+ // handle error
+ }
```
### Available layers
- - advanced activations: `LeakyReLU`, `PReLU`, `ELU`, `ParametricSoftplus`, `ThresholdedReLU`, `SReLU`
+ - *advanced activations*: LeakyReLU, PReLU, ELU, ParametricSoftplus, ThresholdedReLU, SReLU
- - convolutional: `Convolution1D`, `Convolution2D`, `AtrousConvolution2D`, `SeparableConvolution2D`, `Deconvolution2D`, `Convolution3D`, `UpSampling1D`, `UpSampling2D`, `UpSampling3D`, `ZeroPadding1D`, `ZeroPadding2D`, `ZeroPadding3D`
+ - *convolutional*: Convolution1D, Convolution2D, AtrousConvolution2D, SeparableConvolution2D, Deconvolution2D, Convolution3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D
- - core: `Dense`, `Activation`, `Dropout`, `SpatialDropout2D`, `SpatialDropout3D`, `Flatten`, `Reshape`, `Permute`, `RepeatVector`, `Merge`, `Highway`, `MaxoutDense`
+ - *core*: Dense, Activation, Dropout, SpatialDropout2D, SpatialDropout3D, Flatten, Reshape, Permute, RepeatVector, Merge, Highway, MaxoutDense
- - embeddings: `Embedding`
+ - *embeddings*: Embedding
- - normalization: `BatchNormalization`
+ - *normalization*: BatchNormalization
- - pooling: `MaxPooling1D`, `MaxPooling2D`, `MaxPooling3D`, `AveragePooling1D`, `AveragePooling2D`, `AveragePooling3D`, `GlobalMaxPooling1D`, `GlobalAveragePooling1D`, `GlobalMaxPooling2D`, `GlobalAveragePooling2D`
+ - *pooling*: MaxPooling1D, MaxPooling2D, MaxPooling3D, AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalMaxPooling1D, GlobalAveragePooling1D, GlobalMaxPooling2D, GlobalAveragePooling2D
- - recurrent: `SimpleRNN`, `LSTM`, `GRU`
+ - *recurrent*: SimpleRNN, LSTM, GRU
- - wrappers: `Bidirectional`, `TimeDistributed`
+ - *wrappers*: Bidirectional, TimeDistributed
- **Layers not yet implemented**
+### Layers to be implemented
- Lambda cannot be implemented directly at this point, but will eventually create a mechanism for defining computational logic through JavaScript.
+ Note: Lambda layers cannot be implemented directly at this point, but will eventually create a mechanism for defining computational logic through JavaScript.
- - core: `Lambda`
+ - *core*: Lambda
- - convolutional: `Cropping1D`, `Cropping2D`, `Cropping3D`
+ - *convolutional*: AtrousConvolution1D, Cropping1D, Cropping2D, Cropping3D
- - locally-connected: `LocallyConnected1D`, `LocallyConnected2D`
+ - *locally-connected*: LocallyConnected1D, LocallyConnected2D
- - noise: `GaussianNoise`, `GaussianDropout`
+ - *noise*: GaussianNoise, GaussianDropout
+
+ - *pooling*: GlobalMaxPooling3D, GlobalAveragePooling3D
### Notes
**WebWorkers and their limitations**
-Keras.js can be run in a WebWorker separate from the main thread. Because Keras.js performs a lot of synchronous computations, this can prevent the UI from being affected. However, one of the biggest limitations of WebWorkers is the lack of `