# === Generators ===
# train_path = "C:\\Users\\micromikko\\AnacondaProjects\\data\\case2_data\\train"
# validation_path = "C:\\Users\\micromikko\\AnacondaProjects\\data\\case2_data\\validation"
# test_path = "C:\\Users\\micromikko\\AnacondaProjects\\data\\case2_data\\test"

# train_datagen = ImageDataGenerator(rescale=1./255, horizontal_flip=True, vertical_flip=True)
# validation_datagen = ImageDataGenerator(rescale=1./255)
# test_datagen = ImageDataGenerator(rescale=1./255)

# train_generator = train_datagen.flow_from_directory(train_path, target_size=(150, 150), batch_size=30, class_mode="binary")
# validation_generator = validation_datagen.flow_from_directory(validation_path, target_size=(150, 150), batch_size=30, class_mode="binary")
# test_generator = test_datagen.flow_from_directory(test_path, target_size=(150, 150), batch_size=30, class_mode="binary")

# === Creating the model ===
# model = keras.models.Sequential()

# model.add(layers.Conv2D(16, (3, 3), activation="relu", input_shape=(150, 150, 3)))
# model.add(layers.Conv2D(16, (3, 3), activation="relu"))
# model.add(layers.MaxPooling2D((2, 2)))
# model.add(layers.Dropout(0.25))

# model.add(layers.Conv2D(32, (3, 3), activation="relu"))
# model.add(layers.Conv2D(32, (3, 3), activation="relu"))
# model.add(layers.MaxPooling2D((2, 2)))
# model.add(layers.Dropout(0.25))

# model.add(layers.Flatten())

# model.add(layers.Dense(32, activation="relu"))
# model.add(layers.Dense(64, activation="relu"))
# model.add(layers.Dropout(0.5))

# model.add(layers.Dense(1, activation="sigmoid"))

# === Model summary ===
# model.summary()

# === Compiling model ===
# model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy"])

# === Defining the checkpointing system ===
# filepath = "C:\\Users\\micromikko\\AnacondaProjects\\models\\case2_test_best\\case2-{epoch:02d}-{val_loss:.2f}.h5"
# checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')

# === Training the model ===
# start = time.time()
# model_history = model.fit_generator(train_generator, steps_per_epoch=32, epochs=3, validation_data=validation_generator, validation_steps=20, verbose=1, callbacks=[checkpoint])
# end = time.time()
# total = (end - start) / 60

# print("elapsed time:", total)