Crax Rat May 2026

# Building the model base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Training history = model.fit(train_generator, steps_per_epoch=train_generator.samples // 32, validation_data=validation_generator, validation_steps=validation_generator.samples // 32, epochs=10) crax rat

model = Model(inputs=base_model.input, outputs=predictions) 3)) # Training history = model.fit(train_generator

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) steps_per_epoch=train_generator.samples // 32

# Assuming you've collected and preprocessed your data train_dir = 'path/to/train' validation_dir = 'path/to/validation'

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

train_generator = train_datagen.flow_from_directory(train_dir, target_size=(224, 224), batch_size=32, class_mode='categorical')