seed = 7
np.random.seed(seed)
#设置了seed后,会让后面每次产生的随机数相同。
def Conv2d_BN(x, nb_filter,kernel_size, strides=(1,1), padding='same',name=None):
if name is not None:
bn_name = name + '_bn'
conv_name = name + '_conv'
else:
bn_name = None
conv_name = None
x = Conv2D(nb_filter,kernel_size,padding=padding,strides=strides,activation='relu',name=conv_name)(x)
x = BatchNormalization(axis=3,name=bn_name)(x)
return x
def Conv_Block(inpt,nb_filter,kernel_size,strides=(1,1), with_conv_shortcut=False):
x = Conv2d_BN(inpt,nb_filter=nb_filter[0],kernel_size=(1,1),strides=strides,padding='same')
x = Conv2d_BN(x, nb_filter=nb_filter[1], kernel_size=(3,3), padding='same')
x = Conv2d_BN(x, nb_filter=nb_filter[2], kernel_size=(1,1), padding='same')
if with_conv_shortcut:
shortcut = Conv2d_BN(inpt,nb_filter=nb_filter[2],strides=strides,kernel_size=kernel_size)
x = add([x,shortcut])
return x
else:
x = add([x,inpt])
return x
inpt = Input(shape=(224,224,3))
#预期的输入将是一批224*224*3维度的向量
x = ZeroPadding2D((3,3))(inpt)
x = Conv2d_BN(x,nb_filter=64,kernel_size=(95,95),strides=(2,2),padding='valid') #找64种特征,特征基本像素的大小为7*7
x = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same')(x)
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3),strides=(1,1),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3))
x = AveragePooling2D(pool_size=(5,5))(x)
# x = Dropout(0.9)(x)
x = Flatten()(x)
x = Dense(25,activation='softmax')(x)
model = Model(inputs=inpt,outputs=x)
sgd = SGD(decay=0.0001,momentum=0.9)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
model.summary()
神经网络如上所示,只运行到上面的这个步骤,我的12G显存就占了11G多。
然后下一步
print('Training ------------')
# training the model,加上shuffle=True,要不然可能会overfit。只要validation随着acc上升,说明模型就没问题。
# model_load('my_model_resnet.h5')
model.fit(X_train, y_train, validation_split = 0.2,shuffle = True,epochs=50, batch_size=64)
NotFoundError: No algorithm worked!
[[node model/conv2d/Relu (defined at <ipython-input-17-25b86b2dbd84>:5) ]] [Op:__inference_train_function_12948]
Function call stack:
train_function
在ubuntu系统里面。我用的是3060显卡,已经安装了显卡驱动,CUDA驱动,cuDNN驱动等。
为什么会出现这个情况,还没training显存就占满了。求助,麻烦各位大佬帮忙。