|TensorFlow 1 version||View source on GitHub|
Layer that normalizes its inputs.
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, **kwargs )
Used in the notebooks
|Used in the guide||Used in the tutorials|
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.
Integer, the axis that should be normalized (typically the features
axis). For instance, after a
||Momentum for the moving average.|
||Small float added to variance to avoid dividing by zero.|
If True, add offset of
If True, multiply by
||Initializer for the beta weight.|
||Initializer for the gamma weight.|
||Initializer for the moving mean.|
||Initializer for the moving variance.|
||Optional regularizer for the beta weight.|
||Optional regularizer for the gamma weight.|
||Optional constraint for the beta weight.|
||Optional constraint for the gamma weight.|
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in training mode or in inference mode.
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.
trainableon an model containing other layers will recursively set the
trainablevalue of all inner layers.
- If the value of the
trainableattribute is changed after calling
compile()on a model, the new value doesn't take effect for this model until
compile()is called again.