​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