Crax Rat — !link!

# Data augmentation train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)

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

x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x) activation='relu')(x) predictions = Dense(1

validation_generator = validation_datagen.flow_from_directory(validation_dir, target_size=(224, 224), batch_size=32, class_mode='categorical') crax rat

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

validation_datagen = ImageDataGenerator(rescale=1./255)

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