import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import cifar10, cifar100
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Input
from tensorflow.keras.applications import VGG19, Xception, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet121, MobileNetV2, NASNetMobile, EfficientNetB0
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import accuracy_score
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import warnings
warnings.filterwarnings('ignore')

(x_train, y_train), (x_test, y_test) = cifar100.load_data()
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

model_list = [VGG19, Xception, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet121, MobileNetV2, NASNetMobile, EfficientNetB0]

for j in range(len(model_list)):
    try:
        pretrained = model_list[j](weights='imagenet', include_top=False, input_shape=(32, 32, 3))

        result_list = []
        test_acc_list = []

        for i in range(4):
            if 0 <= i < 2:
                input1 = pretrained
            elif 2 <= i:
                pretrained.trainable = False
                input1 = pretrained

            if i == 0 or i == 2:
                flat = Flatten()(input1.output)
            
            elif i == 1 or i == 3:
                flat = GlobalAveragePooling2D()(input1.output)
            
            dense2 = Dense(100)(flat)
            dense3 = Dense(100)(dense2) 
            output = Dense(100, activation='softmax')(dense3)
            
            model = Model(inputs=input1.input, outputs=output)
            model.compile(loss='categorical_crossentropy', optimizer='adam', metrics='acc')
            model.fit(x_train, y_train, epochs=10, batch_size=1024)
            result = model.evaluate(x_test, y_test)
            acc = accuracy_score(np.argmax(y_test, axis=1), np.argmax(model.predict(x_test), axis=1))
            
            print(f'{model_list[j].__name__}\\n{i+1} result : {result}\\nacc : {acc}')
            result_list.append(result[0])
            test_acc_list.append(acc)

        print(f'{model_list[j].__name__}\\n{result_list}\\n{test_acc_list}')
    except:
        print(f'{model_list[j].__name__} encounter an unexpected error')
        continue