Algorithms and Methods Used: Convolutional Neural Network (CNN):

The core algorithm used for image classification is a Convolutional Neural Network. CNNs are particularly effective for image recognition tasks because they can automatically learn spatial hierarchies of features from input images. Data Augmentation with ImageDataGenerator:

ImageDataGenerator from TensorFlow is used for data augmentation, which helps in increasing the diversity of the training data without actually collecting new data. The transformations include rescaling, shear, zoom, and horizontal flip. Model Architecture:

Sequential Model: The model is built using the Sequential API from TensorFlow. Convolutional Layers: Four convolutional layers with ReLU activation and varying filter sizes (64 filters each with kernel sizes 5x5 and 3x3). MaxPooling Layers: Four MaxPooling layers with pool size 2x2 to reduce the spatial dimensions of the feature maps. Flatten Layer: To convert the 3D outputs from the convolutional layers into 1D vectors. Dense Layers: One dense layer with 512 units and ReLU activation followed by an output dense layer with softmax activation for multi-class classification (five classes). Model Compilation:

Optimizer: Adam optimizer is used, which is a popular choice for deep learning models due to its adaptive learning rate capabilities. Loss Function: Categorical crossentropy is used as the loss function since it is a multi-class classification problem. Metrics: Accuracy is used as the metric to evaluate the model’s performance. Model Training:

The model is trained for 30 epochs using the fit method with the training and validation data generated by ImageDataGenerator. Model Saving and Loading:

The trained model is saved to disk using the save method and can be reloaded for future use with the load_model function. Prediction:

Images are preprocessed and passed through the trained model to predict the class. The output probabilities are mapped to the corresponding flower types. Code Snippets Highlighting the Use of Algorithms: Convolutional Layers and Pooling Layers:

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model.add(Conv2D(filters=64, kernel_size=(5, 5), padding='same', activation='relu', input_shape=(224, 224, 3))) model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) Compilation of the Model:

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model.compile(optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy', metrics=['accuracy']) Data Augmentation:

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train_datagen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, validation_split=0.2) test_datagen = ImageDataGenerator(rescale=1. / 255, validation_split=0.2) Conclusion: The algorithms used in this flower recognition task primarily involve convolutional neural networks for model development, data augmentation techniques to enhance the training process, and specific functions and methods from TensorFlow and Keras for model training, evaluation, and prediction. This approach demonstrates a practical implementation of CNNs for supervised image classification problems. dataset 🖇️:https://drive.google.com/drive/folders/1S5zhOxlftyRwWtnJBjT3ZR6RYnjRpMLs