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@TimKoornstra
TimKoornstra authored Oct 14, 2023
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Expand Up @@ -5,7 +5,7 @@ VGSLify is a powerful toolkit designed to simplify the process of defining, trai
## Table of Contents

- [Installation](#installation)
- [How It Works](#how-it-works)
- [How VGSL Works](#how-vgsl-works)
- [Quick Start](#quick-start)
- [Supported Layers and Their Specifications](#supported-layers-and-their-specifications)
- [Future Work](#future-work)
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Below is a concise table providing a summary of each supported layer:

| **Layer** | **Spec** | **Example** | **Description** |
|-------------------------------|-----------------------------------------------------------|-----------------------------|--------------------------------------------------------------------------------------------------------------|
| Input | `batch,height,width,depth` | `None,64,None,1` | Input layer with variable batch size & width, and 1 channel depth |
| Output | `O(2\|1\|0)(l\|s)` | `O1s10` | Dense layer with a 1D sequence, 10 output classes, and softmax activation |
| Conv2D | `C(s\|t\|r\|l\|m),<x>,<y>[,<s_x>,<s_y>],<d>` | `Cr3,3,64` | Conv2D layer with ReLU activation, 3x3 filter size, 1x1 stride, and 64 filters |
| Dense (Fully Connected, FC) | `F(s\|t\|r\|l\|m)<d>` | `Fs64` | Dense layer with softmax activation and 64 units |
| LSTM | `L(f\|r)[s]<n>[,D<rate>][,Rd<rate>]` | `Lf64sD25Rd10` | LSTM cell (forward-only) with 64 units, return sequences, 0.25 dropout, and 0.10 recurrent dropout |
| GRU | `G(f\|r)[s]<n>[,D<rate>][,Rd<rate>]` | `Gr64s,D20,Rd15` | GRU cell (reverse-only) with 64 units, return sequences, 0.20 dropout, and 0.15 recurrent dropout |
| Bidirectional | `B(g\|l)<n>[,D<rate>][,Rd<rate>]` | `Bl256,D15,Rd10` | Bidirectional layer wrapping an LSTM RNN with 256 units, 0.15 dropout, and 0.10 recurrent dropout |
| BatchNormalization | `Bn` | `Bn` | BatchNormalization layer |
| MaxPooling2D | `Mp<x>,<y>,<s_x>,<s_y>` | `Mp2,2,1,1` | MaxPooling2D layer with 2x2 pool size and 1x1 strides |
| AvgPooling2D | `Ap<x>,<y>,<s_x>,<s_y>` | `Ap2,2,2,2` | AveragePooling2D layer with 2x2 pool size and 2x2 strides |
| Dropout | `D<rate>` | `D25` | Dropout layer with a dropout rate of 0.25 |
| Reshape | `Rc` | `Rc` | Reshape layer returns a new (collapsed) tf.Tensor based on the previous layer outputs |
| **Layer** | **Spec** | **Example** | **Description** |
|-------------------------------------------|----------------------------------------------|------------------|----------------------------------------------------------------------------------------------------|
| [Input](#input) | `batch,height,width,depth` | `None,64,None,1` | Input layer with variable batch size & width, and 1 channel depth |
| [Output](#output) | `O(2\|1\|0)(l\|s)` | `O1s10` | Dense layer with a 1D sequence, 10 output classes, and softmax activation |
| [Conv2D](#conv2d) | `C(s\|t\|r\|l\|m),<x>,<y>[,<s_x>,<s_y>],<d>` | `Cr3,3,64` | Conv2D layer with ReLU activation, 3x3 filter size, 1x1 stride, and 64 filters |
| [Dense (Fully Connected, FC)](#dense) | `F(s\|t\|r\|l\|m)<d>` | `Fs64` | Dense layer with softmax activation and 64 units |
| [LSTM](#lstm) | `L(f\|r)[s]<n>[,D<rate>][,Rd<rate>]` | `Lf64sD25Rd10` | LSTM cell (forward-only) with 64 units, return sequences, 0.25 dropout, and 0.10 recurrent dropout |
| [GRU](#gru) | `G(f\|r)[s]<n>[,D<rate>][,Rd<rate>]` | `Gr64s,D20,Rd15` | GRU cell (reverse-only) with 64 units, return sequences, 0.20 dropout, and 0.15 recurrent dropout |
| [Bidirectional](#bidirectional) | `B(g\|l)<n>[,D<rate>][,Rd<rate>]` | `Bl256,D15,Rd10` | Bidirectional layer wrapping an LSTM RNN with 256 units, 0.15 dropout, and 0.10 recurrent dropout |
| [BatchNormalization](#batchnormalization) | `Bn` | `Bn` | BatchNormalization layer |
| [MaxPooling2D](#maxpooling2d) | `Mp<x>,<y>,<s_x>,<s_y>` | `Mp2,2,1,1` | MaxPooling2D layer with 2x2 pool size and 1x1 strides |
| [AvgPooling2D](#avgpooling2d) | `Ap<x>,<y>,<s_x>,<s_y>` | `Ap2,2,2,2` | AveragePooling2D layer with 2x2 pool size and 2x2 strides |
| [Dropout](#dropout) | `D<rate>` | `D25` | Dropout layer with a dropout rate of 0.25 |
| [Reshape](#reshape) | `Rc` | `Rc` | Reshape layer returns a new (collapsed) tf.Tensor based on the previous layer outputs |

*Note*: In the specs, the `|` symbol indicates options. For example, in `O(2 | 1 | 0)(l | s)`, it means the output layer could be `O2l`, `O1s`, etc. Arguments between the `[` and `]` symbol indicate that this is optional. The `[s]` in RNN layers activates `return_sequences`.

Expand Down Expand Up @@ -204,7 +204,7 @@ For more detailed information about each layer and its associated VGSL spec, see
- `Cr3,3,64` creates a Conv2D layer with a ReLU activation function, a 3x3 filter, 1x1 stride, and 64 filters.
- `Cr3,3,1,3,128` creates a Conv2D layer with a ReLU activation function, a 3x3 filter, 1x3 strides, and 128 filters.

#### Dense (Fully-connected layer)
#### Dense

- **Spec**: `F(s|t|r|e|l|m)<d>`
- **Description**: Fully-connected layer with `s|t|r|e|l|m` non-linearity and `d` units.
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