The model uses the Tiny VGG architecture. The model consists of four convolutional layers with max-pooling and dropout layers, followed by a fully connected layer and output layer with softmax activation. The m
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Environment Latest Container Image Output 0 B Time # Log Message 4.9s1/kaggle/input/fashionmnist/t10k-labels-idx1-ubyte 4.9s2/kaggle/input/fashionmnist/t10k-images-idx3-ubyte 4.9s3/kaggle/input/fashionmnist/fashion-mnist_test.csv 4.9s4/kaggle/input/fashionmnist/fashion-mnist_train.csv ...
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python tiny-vgg.py After training, you will get two saved models in Keras format:trained_tiny_vgg.h5andtrained_vgg_best.h5. The first file is the final model after training, andtrained_vgg_best.h5is the model having the best validation performance. You can use either one for CNN Explainer...
Model Training: Training CNNs using Keras on the Tiny ImageNet dataset from scratch, including techniques like Batch Normalization, L2 Regularization, and He Normalization. Pre-Trained Models: Using Keras to employ models like VGG16, ResNet50, and InceptionV3 that were pre-trained on ImageNet IL...