from keras.datasets import mnist
from keras.utils import np_utils
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
np.random.seed(10)

from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPool2D

(x_Train,y_Train),(x_Test,y_Test) = mnist.load_data()

x_Train4D = x_Train.reshape(x_Train.shape[0],28,28,1).astype('float32')
x_Test4D = x_Test.reshape(x_Test.shape[0],28,28,1).astype('float32')

x_Train4D_normalize = x_Train4D/255
x_Test4D_normalize = x_Test4D/255

y_TrainOneHot = np_utils.to_categorical(y_Train)
y_TestOneHot = np_utils.to_categorical(y_Test)

model = Sequential()

model.add(Conv2D(filters=16,kernel_size=(5,5),padding='same',input_shape=(28,28,1), activation='relu'))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Conv2D(filters=36,kernel_size=(5,5),padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.5))

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

print(model.summary())

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

train_history = model.fit(x=x_Train4D_normalize,y=y_TrainOneHot,validation_split=0.2,epochs=10,batch_size=300,verbose=2)

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')

scores = model.evaluate(x_Test4D,y_TestOneHot)
print(scores[1])

prediction = model.predict_classes(x_Test4D_normalize)
print(prediction[:10])

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(labels[idx])
if len(prediction)>0:
title+=",predict="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.show()

plot_images_labels_prediction(x_Test,y_Test,prediction,idx=0)

print(pd.crosstab(y_Test,prediction,rownames=['label'],colnames=['predict']))