Input Layer 2D
This function block is used to define the input layer for a 2D Convolutional Neural Network (CNN) in machine learning applications. It allows you to specify the dimensions of the input data based on the connected dataset.
📥 Inputs
Folder
This input accepts a folder containing image data which will be used for training the model.
📤 Outputs
This function block outputs the configured model.
🕹️ Controls
Size:
A text input field where you can specify the size (width and height) of the input image. The default value is set to 128.
🎨 Features
Customizable Input Size
Users can define the size of the input images, allowing for flexibility in model training.
Integration with Keras
The block integrates seamlessly with Keras, enabling the creation of CNN architectures suitable for various applications.
📝 Usage Instructions
Connect Input Folder: Connect the input to a block that specifies a folder containing 2D image data.
Set Input Size: Adjust the size in the
Size:
text input to define the desired dimensions of the input images.Evaluate the Model: Execute the block to configure and output the 2D input layer model.
📊 Evaluation
When executed, this function block verifies the connected folder and creates a corresponding input layer based on the specified size, which can then be used in a CNN setup.
💡 Tips and Tricks
🛠️ Troubleshooting
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