jupyter上运行结果如下
导入数据
(x_train,y_train), (x_test,y_test) = mnist.load_data()
x_train = x_train.reshape(x_train.shape[0],28,28,1).astype('float32')
x_test = x_test.reshape(x_test.shape[0],28,28,1).astype('float32')
x_train /= 255
x_test /= 255
y_train = np_utils.to_categorical(y_train,10)
y_test = np_utils.to_categorical(y_test,10)
训练网络
model = Sequential()
model.add(Conv2D(filters = 64, kernel_size = (3,3), activation = 'relu', input_shape = (28,28,1)))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(filters = 64, kernel_size = (3,3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dense(10,activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = Adadelta(), metrics = ['accuracy'])
model.fit(x_train,y_train,batch_size=100,epochs=20)
训练结果
用自己手写的数字验证结果总是同一个数字
# 将图片转为灰度图并调整为28*28大小
def convert_gray(f, **args):
rgb=io.imread(f)
gray=color.rgb2gray(rgb)
dst=transform.resize(gray,(28,28))
return dst
test_gray_resize = convert_gray('number3.png')
运行结果如上，我手写的数字3，预测结果是6，后面不管我用哪个自己手写的图片验证，预测结果都是6，刚开始做深度学习，出现bug真是愁好几天，大神求解惑
