image.py

import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D

# Load the pre-trained ResNet50 model without the top layer
base_model = ResNet50(weights='imagenet', include_top=False)

# Add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)

# Add a fully connected layer with a specified number of classes
num_classes = 3  # Pool, Roof Condition, Property Maintenance
predictions = Dense(num_classes, activation='softmax')(x)

# Create the final model
model = Model(inputs=base_model.input, outputs=predictions)

# Fine-tune only the top layers (i.e., freeze all ResNet layers)
for layer in base_model.layers:
    layer.trainable = False

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy',
              metrics=['accuracy'])

# Train the model on your dataset
model.fit(train_dataset, validation_data=val_dataset, epochs=10)

# Evaluate the model on the test dataset
model.evaluate(test_dataset)