mnist 卷积神经网络

# from keras.models import Sequential
# from keras.layers.core import Dense,Activation,Flatten

#creating the Sequential model
# model=Sequential()
# #layer 1 --adding a flatten layer
# model.add(Flatten(input_shape=(32,32,3)))
# #layer 2 --adding a fully connected layer
# model.add(Dense(100))
# #layer 3 --add a Relu activation layer
# model.add(Activation("relu"))
# #layer 4 --adding a fully connected layer
# model.add(Dense(60))
# #layer 5 --adding a Relu activation layer
# model.add(Activation("relu"))

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense,Dropout,Activation
from keras.utils import np_utils

#retrieving the training and test data
(x_train,y_train),(x_test,y_test)=mnist.load_data()
print("x_train_shape",x_train.shape)
print("y_train_shape",y_train.shape)
print("x_test_shape",x_test.shape)
print("y_test_shape",y_test.shape)

#visualize the data
import matplotlib.pyplot as plt

#display a training image by its index in the MNIST set
def disply_digit(index):
    label=y_train[index].argmax(axis=0)
    image=x_train[label]
    plt.title("training data,index:%d,label:%d" %(index,label))
    plt.imshow(image,cmap="gray_r")
    plt.show()

#display the first (index 0)training image
disply_digit(0)

x_train=x_train.reshape(60000,784)
x_test=x_test.reshape(10000,784)
x_train=x_train.astype("float32")
x_test=x_test.astype("float32")
x_train/=255
x_test/=255
print("train the marix shape",x_train.shape)
print("test the matrix shape",x_test.shape)

#one hot encoding of labels
from keras.utils.np_utils import to_categorical
print(y_train.shape)
y_train=to_categorical(y_train,10)
y_test=to_categorical(y_test,10)

#def the neural network
def build_model():
    model=Sequential()
    model.add(Dense(512,input_shape=(784,)))
    model.add(Activation("relu"))
    model.add(Dropout(0.2))
    model.add(Dense(512))
    model.add(Activation("relu"))
    model.add(Dropout(0.2))
    model.add(Dense(10))
    model.add(Activation("softmax"))
    return model
#build the model
model=build_model()
model.compile(optimizer="rmsprop",loss="categorica_crossentropy",metrics=["accuracy"])

#traning
model.fit(x_train,y_train,batch_size=128,nb_epoch=4,verbose=1,validation_data=(x_test,y_test))

#comparing the labels predicted by our model with the actual labels
score=model.evaluate(x_test,y_test,batch_size=2,verbose=1,sample_weight=None)
print("test score",score[0])
print("test accuarcy",score[1])