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Prathmesh PatilVerified signatureThis code is a Python script that demonstrates how to create a deep learning model for binary classification using the VGG16 architecture pre-trained on the ImageNet dataset. Here's a breakdown of what each part of the code does: Importing Libraries: The necessary libraries are imported, including NumPy, Keras, OpenCV (cv2), and TensorFlow. Load Pre-trained VGG16 Model: The VGG16 model is loaded with pre-trained weights from the ImageNet dataset. The include_top=False argument indicates that the fully connected layers (top layers) of the VGG16 model will not be included, allowing for customization with additional layers. Freeze Base Model Layers: All layers of the pre-trained VGG16 model are set to non-trainable (frozen) to prevent their weights from being updated during training. Add Custom Layers for Classification: Additional layers are added on top of the pre-trained VGG16 base model to adapt it for binary classification (in this case, face authentication). These layers include a …9341581 -
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