from keras.datasets import cifar10
import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation,Flatten
from keras.layers import Conv2D,MaxPool2D,ZeroPadding2D
import numpy as np
np.random.seed(10)

(x_img_train,y_label_train),(x_img_test,y_label_test) = cifar10.load_data()

label_dict = {0:"airplane",1:"automobile",2:"bird",3:"cat",4:"deer",5:"dog",6:"frog",7:"horse",8:"ship",9:"truck"}

def plot_images_labels_prediction(images,labels,prediction,idx,num=10):
fig = plt.gcf()
fig.set_size_inches(12,14)
if num>25: num=25
for i in range(0,num):
ax=plt.subplot(5,5,1+i)
ax.imshow(images[idx],cmap='binary')
title="label="+str(label_dict[labels[i][0]])
if len(prediction)>0:
title+=",predict="+str(label_dict[prediction[i]])
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.show()

print('train_data:images:',x_img_train.shape,'labels:',y_label_train.shape)
print('test_data:images:',x_img_test.shape,'labels:',y_label_test.shape)

x_img_train_normalize = x_img_train.astype('float32')/255.0
x_img_test_normalize = x_img_test.astype('float32')/255.0

y_label_train_OneHot = np_utils.to_categorical(y_label_train)
y_label_test_OneHot = np_utils.to_categorical(y_label_test)

model = Sequential()

model.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=(32,32,3),activation='relu',padding='same'))
model.add(Dropout(rate=0.25))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu',padding='same'))
model.add(Dropout(rate=0.25))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dropout(rate=0.25))

model.add(Dense(1024,activation='relu'))
model.add(Dropout(rate=0.25))

model.add(Dense(10,activation='softmax'))

print(model.summary())


model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
train_history = model.fit(x_img_train_normalize,y_label_train_OneHot,validation_split=0.2,epochs=10,batch_size=128,verbose=1)

def show_train_history(train_history,train,validation):
plt.plot(train_history.history[train])
plt.plot(train_history.history[validation])
plt.title('Train History')
plt.ylabel(train)
plt.xlabel('Epoch')
plt.legend(['train','validation'],loc='upper left')
plt.show()

show_train_history(train_history,'acc','val_acc')
show_train_history(train_history,'loss','val_loss')