如何将 CNN 从 keras 转换为 mxnet？
How to convert a CNN from keras to mxnet?
我有以下问题：我在 Keras 中有一个脚本，它非常有用。我现在想将此脚本转换为 MXNet。 Keras 中的 CNN 看起来像这样：
model=Sequential()
model.add(Convolution2D(128, (3, 3), padding='same', activation='relu', name='block1_conv1', input_shape=(80,120,3)))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Convolution2D(256, (3, 3), padding='same', activation='relu', name='block2_conv1'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(2, activation = 'softmax', name='final_fully_connected'))
我认为转换到 MXNet 不会那么困难，我查看了相应的文档并尽我所能传递了参数。
model=gluon.nn.Sequential()
with model.name_scope():
model.add(gluon.nn.Conv2D(channels=128, kernel_size=(3, 3), activation='relu'))
model.add(gluon.nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
model.add(gluon.nn.Conv2D(channels=256, kernel_size=(3, 3), activation='relu'))
model.add(gluon.nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# The Flatten layer collapses all axis, except the first one, into one axis.
model.add(gluon.nn.Flatten())
model.add(gluon.nn.Dense(2, activation='relu'))
但是如果我现在尝试训练模型，我会收到以下错误：
"MXNetError: [17:01:34] C:\ci\libmxnet_1533399150922\work\src\operator\nn\pooling.cc:145: Check failed: param.kernel[1] <= dshape[3] + 2 * param.pad[1] kernel size (2) exceeds input (1 padded to 1)"
我认为它与内核和 MaxPooling2D 层的维度有关，但我不明白这个错误，因为我认为我实际上正在构建与 Keras 中相同的网络。
为了完整性：我的输入变量 X 的维度为 (80, 120, 3)。
非常感谢一些 Keras/MXNet 专家的帮助。
好的，对于那些可能有类似问题的人，这是我自己想出的解决方案：问题是 Keras 和 MXNet 将卷积层应用于不同的维度。 Keras 使用最后一个维度，而 MXNet 使用第一个维度。一个简单的解决方案是更改维度的顺序以使结果相同。就我而言，尺寸为 (3, 80, 120) 的输入参数 X 会给我相同的结果。
我定义模型的函数：
# DEFINE THE MODEL
def create_model(load_file=None):
num_outputs = 2 # The number of outputs of the network
channels = [128, 256] # The number of different filters (each with other entries) in the convolution.
kernel_size = (3, 3) # Specifies the dimensions of the convolution window (i.e., filter).
padding = (kernel_size[0]//2,
kernel_size[1]//2) # To be able to process the border regions of the input layer with the kernel (e.g., a kernel of 3x3 needs an additional neighboring cell), these are surrounded by zeros.
pool_size = (2, 2) # Specifies the size of the pooling window (i.e. region) from which the maximum value is determined.
strides = (2, 2) # Determines by how many steps the pooling window moves. A pooling window of 2x2 and a step size of 2x2 means that the regions won't overlap.
net = gluon.nn.Sequential(prefix='cnn_')
with net.name_scope():
net.add(gluon.nn.Conv2D(channels=channels[0], kernel_size=kernel_size, padding=padding, activation='relu'))
net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
net.add(gluon.nn.Conv2D(channels=channels[1], kernel_size=kernel_size, padding=padding, activation='relu'))
net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
# The Flatten layer collapses all axis, except the first one, into one axis.
net.add(gluon.nn.Flatten())
# In the keras template the authors used activation='softmax'. In Gluon this activation function does not exist. Therefore, we first break down the output to the desired number of outputs and apply the softmax function after the output of the network.
net.add(gluon.nn.Dense(num_outputs))
# Initialize the model parameters
net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
# net.collect_params().initialize(mx.init.Uniform(scale=1.0), ctx=ctx)
# Optional: Load model parameters from a previous run
if load_file:
net.load_parameters(load_file, ctx=ctx)
return net
之后，每当我预测类时，我都会使用mxnet的softmax函数：
y_pred = nd.softmax(net(data[0]))
这是您的模型的准确翻译（据我所知），使用 gluon mxnet api。
class YourNet(HybridBlock):
def __init__(self,kernel_size = (3,3),dilation =(1,1),**kwargs):
super(YourNet,self).__init__(**kwargs)
# Use this scheme for padding='same' for **ODD** kernels
px = dilation[0] * (kernel_size[0] - 1)//2
py = dilation[1] * (kernel_size[1] - 1)//2
pad = (px,py)
# Here you DECLARE but not use!! the layers
with self.name_scope():
self.conv1 = gluon.nn.Conv2D(channels=128,kernel_size=kernel_size,padding=pad,dilation=dilation,prefix='_block1_conv1')
self.conv2 = gluon.nn.Conv2D(channels=256,kernel_size=kernel_size,padding=pad,dilation=dilation,prefix='_block2_conv2')
self.last_layer = gluon.nn.Dense(units=2,prefix='_final_fully_connected')
# You need only one pooling operation, since it doesn't have trainable
# parameters
self.pool = gluon.nn.MaxPool2D(pool_size=(2,2),strides=(2,2))
def hybrid_forward(self, F, input):
"""
In this function you specify how you want to use the layers you defined
previously. F stands for functional, it has some additional
function definitions. There are multiple ways to achieve the same result
(using layers instead of F.SomeFunction).
"""
out = self.conv1(input) # pass input through first layer
out = F.relu(out) # do the activation of the output
out = self.pool(out) # Do max pooling after the activation
out = self.conv2(out) # Now pass through second convolution
out = F.relu(out) # another activation
out = self.pool(out) # Again maxpool 2D
out = F.flatten(out) # Flatten the output. Similar with gluon.nn.Flatten()
out = self.last_layer(out) # Apply last layer (dense)
# Caution with the softmax on the channel applied
out = F.softmax(out,axis=-1) # Do the softmax, with the last layer
# Once you are done, return the output.
return out
用法：
net = YourNet()
net.initialize()
net.hybridize() # ~ x3 speed performance (in gpus), using hybrid block.
# Some random input
xx = nd.random.uniform(shape=[batch_size,3,80,120]) # Channels FIRST - performance improvement.
out = net(xx)
# Try also net.summary(xx), without hybridizing first
补充一下之前的帖子，还有另外一条路，你可以尝试简单的在Keras中使用MXNet后端。查看 keras-mxnet 包：https://github.com/awslabs/keras-apache-mxnet
pip install keras-mxnet
并将您的 ~/.keras/keras.json 修改为：
{
"floatx": "float32",
"epsilon": 1e-07,
"backend": "mxnet",
"image_data_format": "channels_first"
}
我有以下问题：我在 Keras 中有一个脚本，它非常有用。我现在想将此脚本转换为 MXNet。 Keras 中的 CNN 看起来像这样：
model=Sequential()
model.add(Convolution2D(128, (3, 3), padding='same', activation='relu', name='block1_conv1', input_shape=(80,120,3)))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Convolution2D(256, (3, 3), padding='same', activation='relu', name='block2_conv1'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(2, activation = 'softmax', name='final_fully_connected'))
我认为转换到 MXNet 不会那么困难，我查看了相应的文档并尽我所能传递了参数。
model=gluon.nn.Sequential()
with model.name_scope():
model.add(gluon.nn.Conv2D(channels=128, kernel_size=(3, 3), activation='relu'))
model.add(gluon.nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
model.add(gluon.nn.Conv2D(channels=256, kernel_size=(3, 3), activation='relu'))
model.add(gluon.nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# The Flatten layer collapses all axis, except the first one, into one axis.
model.add(gluon.nn.Flatten())
model.add(gluon.nn.Dense(2, activation='relu'))
但是如果我现在尝试训练模型，我会收到以下错误：
"MXNetError: [17:01:34] C:\ci\libmxnet_1533399150922\work\src\operator\nn\pooling.cc:145: Check failed: param.kernel[1] <= dshape[3] + 2 * param.pad[1] kernel size (2) exceeds input (1 padded to 1)"
我认为它与内核和 MaxPooling2D 层的维度有关，但我不明白这个错误，因为我认为我实际上正在构建与 Keras 中相同的网络。
为了完整性：我的输入变量 X 的维度为 (80, 120, 3)。
非常感谢一些 Keras/MXNet 专家的帮助。
好的，对于那些可能有类似问题的人，这是我自己想出的解决方案：问题是 Keras 和 MXNet 将卷积层应用于不同的维度。 Keras 使用最后一个维度，而 MXNet 使用第一个维度。一个简单的解决方案是更改维度的顺序以使结果相同。就我而言，尺寸为 (3, 80, 120) 的输入参数 X 会给我相同的结果。
我定义模型的函数：
# DEFINE THE MODEL
def create_model(load_file=None):
num_outputs = 2 # The number of outputs of the network
channels = [128, 256] # The number of different filters (each with other entries) in the convolution.
kernel_size = (3, 3) # Specifies the dimensions of the convolution window (i.e., filter).
padding = (kernel_size[0]//2,
kernel_size[1]//2) # To be able to process the border regions of the input layer with the kernel (e.g., a kernel of 3x3 needs an additional neighboring cell), these are surrounded by zeros.
pool_size = (2, 2) # Specifies the size of the pooling window (i.e. region) from which the maximum value is determined.
strides = (2, 2) # Determines by how many steps the pooling window moves. A pooling window of 2x2 and a step size of 2x2 means that the regions won't overlap.
net = gluon.nn.Sequential(prefix='cnn_')
with net.name_scope():
net.add(gluon.nn.Conv2D(channels=channels[0], kernel_size=kernel_size, padding=padding, activation='relu'))
net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
net.add(gluon.nn.Conv2D(channels=channels[1], kernel_size=kernel_size, padding=padding, activation='relu'))
net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
# The Flatten layer collapses all axis, except the first one, into one axis.
net.add(gluon.nn.Flatten())
# In the keras template the authors used activation='softmax'. In Gluon this activation function does not exist. Therefore, we first break down the output to the desired number of outputs and apply the softmax function after the output of the network.
net.add(gluon.nn.Dense(num_outputs))
# Initialize the model parameters
net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
# net.collect_params().initialize(mx.init.Uniform(scale=1.0), ctx=ctx)
# Optional: Load model parameters from a previous run
if load_file:
net.load_parameters(load_file, ctx=ctx)
return net
之后，每当我预测类时，我都会使用mxnet的softmax函数：
y_pred = nd.softmax(net(data[0]))
这是您的模型的准确翻译（据我所知），使用 gluon mxnet api。
class YourNet(HybridBlock):
def __init__(self,kernel_size = (3,3),dilation =(1,1),**kwargs):
super(YourNet,self).__init__(**kwargs)
# Use this scheme for padding='same' for **ODD** kernels
px = dilation[0] * (kernel_size[0] - 1)//2
py = dilation[1] * (kernel_size[1] - 1)//2
pad = (px,py)
# Here you DECLARE but not use!! the layers
with self.name_scope():
self.conv1 = gluon.nn.Conv2D(channels=128,kernel_size=kernel_size,padding=pad,dilation=dilation,prefix='_block1_conv1')
self.conv2 = gluon.nn.Conv2D(channels=256,kernel_size=kernel_size,padding=pad,dilation=dilation,prefix='_block2_conv2')
self.last_layer = gluon.nn.Dense(units=2,prefix='_final_fully_connected')
# You need only one pooling operation, since it doesn't have trainable
# parameters
self.pool = gluon.nn.MaxPool2D(pool_size=(2,2),strides=(2,2))
def hybrid_forward(self, F, input):
"""
In this function you specify how you want to use the layers you defined
previously. F stands for functional, it has some additional
function definitions. There are multiple ways to achieve the same result
(using layers instead of F.SomeFunction).
"""
out = self.conv1(input) # pass input through first layer
out = F.relu(out) # do the activation of the output
out = self.pool(out) # Do max pooling after the activation
out = self.conv2(out) # Now pass through second convolution
out = F.relu(out) # another activation
out = self.pool(out) # Again maxpool 2D
out = F.flatten(out) # Flatten the output. Similar with gluon.nn.Flatten()
out = self.last_layer(out) # Apply last layer (dense)
# Caution with the softmax on the channel applied
out = F.softmax(out,axis=-1) # Do the softmax, with the last layer
# Once you are done, return the output.
return out
用法：
net = YourNet()
net.initialize()
net.hybridize() # ~ x3 speed performance (in gpus), using hybrid block.
# Some random input
xx = nd.random.uniform(shape=[batch_size,3,80,120]) # Channels FIRST - performance improvement.
out = net(xx)
# Try also net.summary(xx), without hybridizing first
补充一下之前的帖子，还有另外一条路，你可以尝试简单的在Keras中使用MXNet后端。查看 keras-mxnet 包：https://github.com/awslabs/keras-apache-mxnet
pip install keras-mxnet
并将您的 ~/.keras/keras.json 修改为：
{
"floatx": "float32",
"epsilon": 1e-07,
"backend": "mxnet",
"image_data_format": "channels_first"
}