code initializes image data generators for augmenting and preprocessing images, then creates data generators for training and test sets.
It builds a convolutional neural network (CNN) with convolutional, pooling, flatten, and dense layers for binary image classification.
The model is compiled with the Adam optimizer and binary cross-entropy loss.
It trains the model on the dataset for 25 epochs and validates it with test data. Finally, the trained model is saved as cat_dog_classifier.h5.
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This project builds a Convolutional Neural Network (CNN) using TensorFlow and Keras to classify images of cats and dogs. It preprocesses the image data by rescaling pixel values and applying random transformations. The CNN model includes convolutional, pooling, and dense layers for feature extraction and classification.
prempraneethkota/Cats_and_dogs_classifier
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This project builds a Convolutional Neural Network (CNN) using TensorFlow and Keras to classify images of cats and dogs. It preprocesses the image data by rescaling pixel values and applying random transformations. The CNN model includes convolutional, pooling, and dense layers for feature extraction and classification.
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