（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

​1、联通ColaB

2、运行最基础mnist例子，并且打印图表结果 

# 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 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) = 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() 

3、两句修改成fasion模式 

# 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() 

4、VGG16&Mnist

5、VGG16迁移学习

来自为知笔记(Wiz)