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1. Using AlexNet network to recognize weather images - prediction

The following is a complete implementation scheme for using AlexNet network to recognize weather images and make predictions, optimized and explained in detail based on the provided reference code:

1. Core implementation steps: Environment configuration # Ensure environment consistency (Python 3.8+TensorFlow 2.3) import tensorflow as tfprint (tf. __version __) # Expected output 2.3.0 Data preparation from tensorflow. keras. reprocessing. image import ImageDataGenerator # Path configuration (directory structure needs to be created in advance) train-dir="./CV_data/Multi class weather image dataset/train_image/" # Training set (including subfolders) pred ict_ir="./CV_data/Multi class weather image dataset/predict_image/" # The image to be predicted is saved as "./weather_madel/" # Model saving path # Image preprocessing parameters Imgzize=(224, 224) # AlexNet standard input size batchsize=32 # Adjust based on GPU memory # Data augmentation generator (used during training) train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True)# Prediction Data Generator (Normalized Only) predict_datagen=ImageDataGenerator (rescale=1/255) # Generate Training Data Stream train_generator = train_datagen.flow_from_directory( train_dir, target_size=img_size, batch_size=batch_size, class_mode="categorical") # Construction of AlexNet model for multi classification tasks from tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropoutdef build_alexnet(input_shape=(224,224,3), num_classes=5):

model = Sequential([ # The first convolutional block Conv2D(96, (11,11), strides=4, activation="relu", input_shape=input_shape), MaxPooling2D((3,3), strides=2), #Second Convolutional Block Conv2D(256, (5,5), activation="relu", padding="same"), MaxPooling2D((3,3), strides=2), #The third to fifth convolution blocks Conv2D(384, (3,3), activation="relu", padding="same"), Conv2D(384, (3,3), activation="relu", padding="same"), Conv2D(256, (3,3), activation="relu", padding="same"), MaxPooling2D((3,3), strides=2), #Fully connected layer Flatten(), Dense(4096, activation="relu"), Dropout(0.5), Dense(4096, activation="relu"), Dropout(0.5), Dense(num_classes, activation="softmax") ]) return model# Instantiate model (assuming 5 weather categories) model = build_alexnet(num_classes=len(train_generator.class_indices))model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) Model Training and Saving # Train the model (sufficient training data needs to be prepared in advance) history=model. fit (train_generator, epochs=30, # adjust validation_data=train_generator based on validation set performance) # Actually, an independent validation set should be used # Save the model model. save (f "{save_dir}/weather_alexnet. h5") for prediction implementation import numpy as npfrom pathlib import Pathimport matplotlib.pyplot as pltdef predict_weather(image_dir, model_path): # Load model=tf. keras. models. load_madel (model_math) # Create prediction generator predict_generator = predict_datagen.flow_from_directory( image_dir, target_size=img_size, batch_size=1, class_mode=None, shuffle=False) # Maintain file order # Execute prediction predictions = model.predict(predict_generator)

flow rate monitor

1. Using AlexNet network to recognize weather images - prediction

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