（4运行例子）自己动手，编写神经网络程序，解决Mnist问题，并网络化部署

# https://pypi.python.org/pypi/pydot
#!apt-get -qq install -y graphviz && pip install -q pydot
#import pydot

from __future__ import print_function
import keras
from keras.datasets import fashion_mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.utils import plot_model
import matplotlib.pyplot as plt

batch_size = 128
num_classes = 10
epochs = 12
#epochs = 2

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

if K.image_data_format() == 'channels_first' :
    x_train = x_train.reshape(x_train.shape[ 0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[ 0], 1, img_rows, img_cols)
    input_shape = ( 1, img_rows, img_cols)
else :
    x_train = x_train.reshape(x_train.shape[ 0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[ 0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype( 'float32')
x_test = x_test.astype( 'float32')
x_train /= 255
x_test /= 255
print( 'x_train shape:', x_train.shape)
print(x_train.shape[ 0], 'train samples')
print(x_test.shape[ 0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D( 32, kernel_size =( 3, 3),
                 activation = 'relu',
                 input_shape =input_shape))
model.add(Conv2D( 64, ( 3, 3), activation = 'relu'))
model.add(MaxPooling2D(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(num_classes, activation = 'softmax'))

model. compile(loss =keras.losses.categorical_crossentropy,
              optimizer =keras.optimizers.Adadelta(),
              metrics =[ 'accuracy'])

#log = model.fit(X_train, Y_train,   
#          batch_size=batch_size, nb_epoch=num_epochs,  
#          verbose=1, validation_split=0.1)  

log = model.fit(x_train, y_train,
          batch_size =batch_size,
          epochs =epochs,
          verbose = 1,
          validation_data =(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose = 0)
print( 'Test loss:', score[ 0])
print( 'Test accuracy:', score[ 1])

plt.figure( 'acc')  
plt.subplot( 2, 1, 1)  
plt.plot(log.history[ 'acc'], 'r--',label = 'Training Accuracy')  
plt.plot(log.history[ 'val_acc'], 'r-',label = 'Validation Accuracy')  
plt.legend(loc = 'best')  
plt.xlabel( 'Epochs')  
plt.axis([ 0, epochs, 0. 9, 1])  
plt.figure( 'loss')  
plt.subplot( 2, 1, 2)  
plt.plot(log.history[ 'loss'], 'b--',label = 'Training Loss')  
plt.plot(log.history[ 'val_loss'], 'b-',label = 'Validation Loss')  
plt.legend(loc = 'best')  
plt.xlabel( 'Epochs')  
plt.axis([ 0, epochs, 0, 1])  
plt.show()