import numpy as np
import pandas as pd
from keras.utils import np_utils
from keras.datasets import mnist
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

(X_train_image,y_train_label),(X_test_image,y_test_label) = mnist.load_data()

print('train data=',len(X_train_image))
print('test data=',len(X_test_image))

print('x_train_image:',X_train_image.shape)
print('y_train_label',y_train_label.shape)

def plot_image(image):
fig=plt.gcf()
fig.set_size_inches(2,2)
plt.imshow(image,cmap='binary')
plt.show()

plot_image(X_train_image[0])

x_Train = X_train_image.reshape(60000,784).astype('float32')
x_Test = X_test_image.reshape(10000,784).astype('float32')

print('x_train:',x_Train.shape)
print('x_test',x_Test.shape)

x_Train_normalize = x_Train/255
x_Test_normalize = x_Test/255

print(y_train_label[:5])

y_trainOneHot = np_utils.to_categorical(y_train_label)
y_testOneHot = np_utils.to_categorical(y_test_label)

print(y_trainOneHot[:5])

model = Sequential()

model.add(Dense(units=1000,input_dim=784,kernel_initializer='normal',activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(units=1000,kernel_initializer='normal',activation='relu'))
model.add(Dropout(0.5))

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

print(model.summary())

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

train_history = model.fit(x=x_Train_normalize,y=y_trainOneHot,validation_split=0.2,epochs=10,batch_size=200,verbose=2)

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()

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_Test_normalize,y_testOneHot)
print('accuracy=',scores[1])

prediction = model.predict_classes(x_Test)

print(prediction)
plot_images_labels_prediction(X_test_image,y_test_label,prediction,idx=340)

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

df = pd.DataFrame({'label':y_test_label,'predict':prediction})
print(df[(df.label==5)&(df.predict==3)])

plot_images_labels_prediction(X_test_image,y_test_label,prediction,idx=340,num=1)