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
