问题描述
我正在将TensorFlow代码迁移到Tensorflow 2.1.0.
I'm migrating a TensorFlow code to Tensorflow 2.1.0.
这是原始代码:
conv = tf.layers.conv2d(inputs, out_channels, kernel_size=3, padding='SAME')
conv = tf.contrib.layers.batch_norm(conv, updates_collections=None, decay=0.99, scale=True, center=True)
conv = tf.nn.relu(conv)
conv = tf.contrib.layers.max_pool2d(conv, 2)
这就是我所做的:
conv1 = Conv2D(out_channels, (3, 3), activation='relu', padding='same', data_format='channels_last', name=name)(inputs)
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', data_format="channels_last")(conv1)
#conv = tf.contrib.layers.batch_norm(conv, updates_collections=None, decay=0.99, scale=True, center=True)
pool1 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last")(conv1)
我的问题是我不知道如何处理 tf.contrib.layers.batch_norm
.
My problem is that I don't know what to do with tf.contrib.layers.batch_norm
.
如何将 tf.contrib.layers.batch_norm
迁移到Tensorflow 2.x?
How can I migrate tf.contrib.layers.batch_norm
to Tensorflow 2.x?
更新:
使用评论建议,我认为我已正确迁移:
UPDATE:
Using the comment suggestion, I think I have migrated correctly:
conv1 = BatchNormalization(momentum=0.99, scale=True, center=True)(conv1)
但是我不确定 decay
是否像 momentum
一样,而且我不知道如何在中设置
方法. updates_collections
BatchNormalization
But I'm not sure if decay
is like momentum
and I don't know how to set updates_collections
in the BatchNormalization
method.
推荐答案
在使用我将要进行微调的训练模型时,我遇到了这个问题.像OP那样用 tf.keras.layers.BatchNormalization
替换 tf.contrib.layers.batch_norm
确实给了我一个错误,其修复方法如下所述.
I encountered this problem when working with a trained model that I was going to fine tune. Just replacing tf.contrib.layers.batch_norm
with tf.keras.layers.BatchNormalization
like OP did gave me an error whose fix is described below.
旧代码如下:
tf.contrib.layers.batch_norm(
tensor,
scale=True,
center=True,
is_training=self.use_batch_statistics,
trainable=True,
data_format=self._data_format,
updates_collections=None,
)
,更新后的工作代码如下:
and the updated working code looks like this:
tf.keras.layers.BatchNormalization(
name="BatchNorm",
scale=True,
center=True,
trainable=True,
)(tensor)
我不确定我删除的所有关键字参数是否都会出现问题,但是一切似乎都可以正常工作.请注意 name ="BatchNorm"
参数.图层使用不同的命名架构,因此我不得不使用 inspect_checkpoint.py
工具查看模型,并找到恰好是 BatchNorm
的图层名称.
I'm unsure if all the keyword arguments I removed are going to be a problem but everything seems to work. Note the name="BatchNorm"
argument. The layers use a different naming schema so I had to use the inspect_checkpoint.py
tool to look at the model and find the layer names which happened to be BatchNorm
.
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