我在Keras中用TensorFlow实现了一个3D CNN
,在这之前它运行得非常好。现在为了加快多个GPU的训练,我想尝试使用MXNet
和Keras
。我原以为除了“channels_last
”到“channels\u first”的问题之外,我不需要修改很多代码,但是程序在Conv3D操作中崩溃了。你知道吗
那个keras.json文件文件是,因此应将其设置为正常运行:
{
"backend": "mxnet",
"image_data_format": "channels_first",
"epsilon": 1e-07,
"floatx": "float32"
}
这是显示错误的一小部分:
from keras.models import *
from keras.layers import *
from keras.optimizers import *
def SimpleInceptionBlock(input, num_kernels, kernel_init='he_normal', padding='same', bn_axis=1):
tower1 = Conv3D(num_kernels, 1, padding=padding, kernel_initializer=kernel_init)(input)
tower1 = BatchNormalization()(tower1)
tower1 = ELU()(tower1)
tower2 = MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding=padding)(input)
tower2 = Conv3D(num_kernels, 1, padding=padding, kernel_initializer=kernel_init)(tower2)
tower2 = BatchNormalization()(tower2)
tower2 = ELU()(tower2)
output = concatenate([tower1, tower2], axis=bn_axis)
return output
def TestNet(input_size=(1,64,64,64), num_class=7):
bn_axis = 1
img_input = Input(shape=input_size)
filter1 = SimpleInceptionBlock(img_input, 16)
# this runs fine, filter1.shape = (None, 32, 64, 64, 64)
filter2 = SimpleInceptionBlock(filter1, 16)
output = Conv3D(num_class, (1, 1, 1), activation='softmax', kernel_initializer = kernel_init, padding='same', kernel_regularizer=l2(1e-4))(filter2)
model = Model(input=img_input, output=output)
return model
model = TestNet()
第一次调用“SimpleInceptionBlock
”运行正常,filter1.shape = (None, 32, 64, 64)
与预期一致,但第二次调用产生错误消息:
Error in operator concat0: [15:40:58] C:\Jenkins\workspace\mxnet-tag\mxnet\src\operator\nn\concat.cc:66: Check failed: shape_assign(&(*in_shape)[i], dshape) Incompatible input shape: expected [0,0,64,64,64], got [0,16,64,65,65]
目前没有回答
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