From a6112f000d5ba6d21ea33085412921ea4757735b Mon Sep 17 00:00:00 2001 From: Tim Koornstra Date: Thu, 28 Sep 2023 15:49:10 +0200 Subject: [PATCH] Update README.md --- README.md | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 7ad62c5a..e75cda5d 100644 --- a/README.md +++ b/README.md @@ -22,11 +22,11 @@ Ensure you have the following prerequisites installed or set up: - Ubuntu or a similar Linux-based operating system. The provided commands are tailored for such systems. -### Steps - > [!IMPORTANT] > The requirements listed in `requirements.txt` require a Python version > 3.8. It should be possible to run in Python <= 3.8, but one would have to downgrade some packages (such as NumPy and Tensorflow). +### Steps + 1. **Install Python 3** ```bash @@ -177,8 +177,7 @@ In this example, the string defines a neural network with input layers, convolut | AvgPooling2D | `Ap,,,` | `Ap2,2,2,2` | AveragePooling2D layer with 2x2 pool size and 1x1 strides | | Dropout | `D` | `Do25` | Dropout layer with `dropout` = 0.25 | | Reshape | `Rc` | `Rc` | Reshape layer returns a new (collapsed) tf.Tensor with a different shape based on the previous layer outputs | -| ResidualBlock | **TODO** | **TODO** | **TODO** | -| CTCLayer | **TODO** | **TODO** | **TODO** | +| ResidualBlock | `RB[d],,` | `RB3,3,64` | Residual Block with optional downsample. Has a kernel size of , and a depth of . If [d] is provided, the block will downsample the input | ### Layer Details #### Input @@ -255,11 +254,10 @@ In this example, the string defines a neural network with input layers, convolut - **Description**: Reshapes the output tensor from the previous layer, making it compatible with RNN layers. - **Example**: `Rc` applies a specific transformation: `layers.Reshape((-1, prev_layer_y * prev_layer_x))`. ---- - -#### Custom blocks: -- **ResidualBlock**: Documentation in progress. -- **CTCLayer**: Documentation in progress. +#### ResidualBlock +- **Spec**: `RB[d],,` +- **Description**: A Residual Block with a kernel size of , and a depth of . If [d] is provided, the block will downsample the input. Residual blocks are used to allow for deeper networks by adding skip connections, which helps in preventing the vanishing gradient problem. +- **Example**: `RB3,3,64` creates a Residual Block with a 3x3 kernel size and a depth of 64 filters. ## API Usage Guide