tf.keras.layers.BatchNormalization( axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer="zeros", gamma_initializer="ones", moving_mean_initializer="zeros", moving_variance_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, synchronized=False, **kwargs )
Layer that normalizes its inputs.
Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1.
Importantly, batch normalization works differently during training and during inference.
During training (i.e. when using
fit() or when calling the layer/model
with the argument
training=True), the layer normalizes its output using
the mean and standard deviation of the current batch of inputs. That is to
say, for each channel being normalized, the layer returns
gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, where:
epsilonis small constant (configurable as part of the constructor arguments)
gammais a learned scaling factor (initialized as 1), which can be disabled by passing
scale=Falseto the constructor.
betais a learned offset factor (initialized as 0), which can be disabled by passing
center=Falseto the constructor.
During inference (i.e. when using
predict() or when
calling the layer/model with the argument
training=False (which is the
default), the layer normalizes its output using a moving average of the
mean and standard deviation of the batches it has seen during training. That
is to say, it returns
gamma * (batch - self.moving_mean) / sqrt(self.moving_var+epsilon) + beta.
self.moving_var are non-trainable variables that
are updated each time the layer in called in training mode, as such:
moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum)
moving_var = moving_var * momentum + var(batch) * (1 - momentum)
As such, the layer will only normalize its inputs during inference after having been trained on data that has similar statistics as the inference data.
synchronized=True is set and if this layer is used within a
tf.distribute strategy, there will be an
to aggregate batch statistics across all replicas at every
training step. Setting
synchronized has no impact when the model is
trained without specifying any distribution strategy.
strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(16)) model.add(tf.keras.layers.BatchNormalization(synchronized=True))
betato normalized tensor. If False,
gamma. If False,
gammais not used. When the next layer is linear (also e.g.
nn.relu), this can be disabled since the scaling will be done by the next layer.
training=True: The layer will normalize its inputs using the mean and variance of the current batch of inputs.
training=False: The layer will normalize its inputs using the mean and variance of its moving statistics, learned during training.
Arbitrary. Use the keyword argument
input_shape (tuple of
integers, does not include the samples axis) when using this layer as the
first layer in a model.
Same shape as input.
layer.trainable = False on a
The meaning of setting
layer.trainable = False is to freeze the layer,
i.e. its internal state will not change during training:
its trainable weights will not be updated
train_on_batch(), and its state updates will not be run.
Usually, this does not necessarily mean that the layer is run in inference
mode (which is normally controlled by the
training argument that can
be passed when calling a layer). "Frozen state" and "inference mode"
are two separate concepts.
However, in the case of the
BatchNormalization layer, setting
trainable = False on the layer means that the layer will be
subsequently run in inference mode (meaning that it will use
the moving mean and the moving variance to normalize the current batch,
rather than using the mean and variance of the current batch).
This behavior has been introduced in TensorFlow 2.0, in order
layer.trainable = False to produce the most commonly
expected behavior in the convnet fine-tuning use case.
trainable on an model containing other layers will
recursively set the
trainable value of all inner layers.
- If the value of the
attribute is changed after calling
compile() on a model,
the new value doesn't take effect for this model
compile() is called again.