Batch Normalization (“batch norm”) explained
Summary
TLDRThis video explains batch normalization (batch norm) in the context of training artificial neural networks. It starts with a discussion on regular normalization techniques and their importance in preventing issues like imbalanced gradients and the exploding gradient problem. The video then introduces batch normalization as a method to stabilize and accelerate training by normalizing layer outputs. The presenter demonstrates how to implement batch norm using Keras, highlighting its benefits, such as optimizing weights and speeding up the training process. The video also provides a code example for integrating batch norm into neural network models.
Takeaways
- 🎯 Batch normalization (Batch Norm) helps improve neural network training by stabilizing data distribution across layers.
- 📊 Normalization or standardization during pre-processing ensures that input data is on the same scale, which avoids issues caused by wide data ranges.
- 🚗 Without normalization, large disparities in data points can cause instability in neural networks, leading to issues like the exploding gradient problem.
- 📈 Standardization involves subtracting the mean from data points and dividing by standard deviation, resulting in a mean of 0 and standard deviation of 1.
- ⚖️ Even with normalized input data, imbalances can occur during training if weights become disproportionately large, affecting neuron outputs.
- 🔄 Batch Norm normalizes the output of the activation function for specific layers, preventing large weights from cascading and causing instability.
- ⚙️ In Batch Norm, normalized output is multiplied by an arbitrary parameter and adjusted by another, both of which are trainable and optimized during training.
- ⏱️ Batch Norm increases training speed by ensuring stable and balanced data distribution across the network's layers.
- 🧮 Batch Norm operates on a per-batch basis, normalizing data for each batch based on the batch size specified during training.
- 💻 Implementing Batch Norm in Keras is straightforward by adding a batch normalization layer between hidden and output layers, and it can improve model performance.
Q & A
What is the primary purpose of normalization or standardization in neural network training?
-The primary purpose of normalization or standardization is to put all data points on the same scale, which helps increase training speed and avoids issues such as instability caused by large numerical data points.
What is the difference between normalization and standardization?
-Normalization scales numerical data to a range from 0 to 1, while standardization subtracts the mean and divides by the standard deviation, resulting in data with a mean of 0 and a standard deviation of 1. Both techniques aim to make the data more uniform for better training results.
Why is it important to normalize data before training a neural network?
-Normalizing data is important because non-normalized data can cause instability in the network due to large input values cascading through layers. This may result in problems such as exploding gradients and slower training speeds.
How does batch normalization help during the training of a neural network?
-Batch normalization helps by normalizing the output from the activation function for selected layers in the network. This prevents large weight values from dominating the training process, stabilizes the network, and increases the training speed.
What problem does batch normalization address that regular data normalization does not?
-Batch normalization addresses the issue of imbalanced weights during training. Even with normalized input data, some weights can grow much larger than others, causing instability in the network. Batch normalization normalizes the output of each layer, mitigating this problem.
How does batch normalization adjust the data in each layer?
-Batch normalization normalizes the output from the activation function by applying a mean and standard deviation, then multiplies the normalized output by an arbitrary parameter and adds another arbitrary parameter to adjust the data further. These parameters are trainable and optimized during training.
What are the main benefits of using batch normalization in neural networks?
-The main benefits of using batch normalization are faster training speeds and increased stability, as it prevents the problem of outlier weights becoming too large and influencing the network disproportionately.
When is batch normalization applied in the context of a neural network?
-Batch normalization is applied after the activation function in layers that you choose to normalize. It can be added to any hidden or output layers where you want to control the output distribution.
How does batch normalization affect the training process?
-Batch normalization normalizes the layer outputs on a per-batch basis, which ensures that each batch of data is on a more uniform scale. This improves gradient flow and prevents issues such as vanishing or exploding gradients, making the training process more efficient.
What parameters can be adjusted when implementing batch normalization in Keras?
-In Keras, parameters like `axis`, `beta_initializer`, and `gamma_initializer` can be adjusted when implementing batch normalization. These control how the normalization is applied and how the arbitrary parameters are initialized.
Outlines
🔍 Understanding Data Normalization in Neural Networks
The video introduces the concept of batch normalization, known as batch norm, in the context of neural network training. Before diving into batch norm, it explains regular data normalization techniques like scaling data between 0 and 1 (normalization) or standardizing it by subtracting the mean and dividing by standard deviation. This helps neural networks operate on a common scale, avoiding instability and the exploding gradient problem. Data that varies too widely, like miles driven versus age, can cause training issues. Normalizing the data ensures faster, more stable training by reducing wide variations in input values.
📉 The Problem with Large Weights in Neural Networks
Even after input data normalization, issues can arise during training, specifically with large weights. As neural networks update their weights through stochastic gradient descent, some weights can grow disproportionately large, leading to imbalanced neuron outputs. This instability can cascade through the network, creating problems in training. Batch normalization addresses this by normalizing the output of a layer’s activation function and applying adjustable parameters that set a new standard deviation and mean. This not only prevents extreme weight imbalances but also optimizes the training process, improving both speed and performance.
⚙️ Batch Normalization in Practice and Code
The video then explains how batch norm can be applied during training at individual layers, not just to the input data. Normalization now happens both before the data enters the input layer and during the training process within each layer. Batch norm operates on a per-batch basis, determined by the batch size set during model training. The presenter then shows how to implement batch normalization in Keras. By inserting a `BatchNormalization` layer in the code after a hidden layer, the model normalizes its output. Key parameters like the axis and optional initializers (beta and gamma) are explained.
💻 Coding Batch Normalization in Keras
The video dives deeper into the technical aspects of implementing batch normalization in Keras. The presenter demonstrates adding a batch normalization layer between a hidden layer and the output layer in a neural network model. Key parameters include the axis for normalization (usually the features axis), with optional parameters like beta and gamma initializers for fine-tuning. These parameters default to zero and one, respectively, but can be customized. The video wraps up by emphasizing how batch norm optimizes training, stabilizes weights, and improves the model’s overall performance.
Mindmap
Keywords
💡Batch Normalization
💡Normalization
💡Standardization
💡Stochastic Gradient Descent (SGD)
💡Exploding Gradient Problem
💡Activation Function
💡Mean and Standard Deviation
💡Arbitrary Parameters (Beta and Gamma)
💡Training Speed
💡Batch Size
Highlights
Batch normalization (batch norm) helps stabilize neural network training by addressing imbalanced data within the layers.
Normalization and standardization are preprocessing techniques used to transform data to a common scale before training neural networks.
Normalization typically scales data between 0 and 1, while standardization subtracts the mean and divides by the standard deviation, forcing the data to have a mean of 0 and standard deviation of 1.
Non-normalized data can cause instability in neural networks, leading to exploding gradient problems due to large differences in input scales.
Using batch normalization ensures the network does not suffer from imbalanced gradients and speeds up the training process.
Batch normalization normalizes the output of the activation function for a layer, ensuring balanced input into the next layer.
The process of batch norm includes multiplying the normalized output by an arbitrary parameter and adding another arbitrary parameter, which are trainable during the model’s optimization.
Batch normalization optimizes four parameters: the mean, the standard deviation, and two additional arbitrary trainable parameters.
Batch normalization helps avoid large weights dominating the training process, leading to more stable and faster convergence.
While normalization in preprocessing adjusts data before being passed to the input layer, batch norm adjusts data after activation within the network layers.
Batch normalization occurs on a per-batch basis, dependent on the batch size set during training.
Keras allows easy implementation of batch normalization by adding a BatchNormalization layer after the desired activation layer.
Keras provides options to set initializers for the two arbitrary parameters (beta and gamma), which can be customized, though defaults are set to 0 and 1.
Batch normalization can greatly enhance training efficiency and mitigate issues such as exploding or vanishing gradients.
Batch norm is a crucial addition to neural networks, especially for deep networks, to maintain balanced gradient flow and improve model performance.
Transcripts
[Music]
in this video we'll be discussing batch
normalization otherwise known as batch
norm and how it applies to training and
artificial neural network will then see
how to implement batch norm and code
with Kerris before getting to the
details about batch normalization let's
quickly first discuss regular
normalization techniques generally
speaking when training a neural network
we want to normalize or standardize our
data in some way ahead of time as part
of the pre-processing step this is a
step where we prepare our data to get it
ready for training normalization and
standardization both have the same
objective of transforming the data to
put all the data points on the same
scale a typical normalization process
consists of scaling the numerical data
down to be on a scale from zero to one
in a typical standardization process
consists of subtracting the mean of the
data set from each data point and then
dividing the difference by the data sets
standard deviation this forces the
standardized data to take on a mean of
zero and a standard deviation of one in
practice this standardization process is
often just referred to as normalization
as well in general though this all boils
down to putting our data on some type of
known or standard scale so why do we do
this well if we didn't normalize our
data in some way you can imagine that we
may have some numerical data points in
our data set that might be very high and
others that might be very low for
example say we have data on the number
of miles individuals have driven a car
over the last five years then we may
have someone who's driven a hundred
thousand miles total and we may have
someone else who's only driven a
thousand miles total this data has a
relatively wide range and isn't
necessarily on the same scale
additionally each one of the features
for each of our samples could vary
widely as well if we have one feature
which corresponds to an individual's age
and then another feature corresponding
to the number of miles that that
individual has driven a car over the
last five years then again we see that
these two pieces of data age and miles
driven will not be on the same scale
the larger data points in these
non-normalized datasets can cause
instability in neural networks because
the relatively large inputs can cascade
down through the layers in the network
which may cause imbalance gradients
which may therefore cause the famous
exploding gradient problem we may cover
this particular problem in another video
but for now understand that this
imbalanced non-normalized data may cause
problems with our network that make it
drastically harder to Train
additionally non-normalized data can
significantly decrease our training
speed when we normalize our inputs
however we put all of our data on the
same scale and attempts to increase
training speed as well as avoid the
problem we just discussed because we
won't have this relatively wide range
between data points any longer once
we've normalized the data okay so this
is good but there's another problem that
can arise even with normalized data so
from our previous video on how a neural
network learns we know how the weights
in our model become updated over each
epoch during training via the process of
stochastic gradient descent or SGD so
what if during training one of the
weights ends up becoming drastically
larger than the other weights
well this large weight will then cause
the output from its corresponding neuron
to be extremely large and this imbalance
will again continue to cascade through
the neural network causing instability
this is where batch normalization comes
into play batch norm is applied to
layers that you choose to apply it to
within your network when applying batch
norm to a layer the first thing the
batch norm does is normalize the output
from the activation function recall from
our video on activation functions that
the output from a layer is passed to an
activation function which transforms the
output in some way depending on the
function itself before being passed to
the next layer as input after
normalizing the output from the
activation function bash norm then
multiplies this normalized output by
some arbitrary parameter and then adds
another arbitrary parameter to this
resulting product this calculation with
the two arbitrary parameters sets a new
standard deviation and mean for the data
these four parameters consisting of the
mean the standard deviation
and the two arbitrarily set parameters
are all trainable meaning that they too
will become optimized during the
training process this process makes it
so that the weights within the network
don't become imbalance with extremely
high or low values since the
normalization is included in the
gradient process this addition of batch
norm to our model can greatly increase
the speed in which training occurs and
reduce the ability of outlying large
weights that will over influence the
training process so when we spoke
earlier about normalizing our input data
in the pre-processing step before
training occurs we understand that this
normalization happens to the data before
being passed to the input layer now with
batch norm we can normalize the output
data from the activation functions for
individual layers with our model as well
so we have normalized data coming in and
we also have normalized data within the
model itself now everything we just
mentioned about the batch normalization
process occurs on a per batch basis
hence the name batch norm these batches
are determined by the batch size you set
when you train your model so if you're
not yet familiar with training batches
or batch size check out my video that
covers this topic so now that we have an
understanding of batch norm let's look
at how we can add batch norm to a model
and code using Kerris so I'm here in my
Jupiter notebook and I've just copied
the code for a model that we've built in
a previous video so we have a model with
two hidden layers with 16 and 32 nodes
respectively both using rel you as their
activation functions and then an output
layer with to output categories using
the softmax activation function the only
difference here is this line between the
last hidden layer and the output layer
this is how you specify batch
normalization and caris following the
layer for which you want the activation
output normalized you specify a batch
normalization layer which is what we
have here to do this you first need to
import batch normalization from Charis
as shown in this cell now the only
parameter that I'm specifying here is
the axis parameter and that's just to
specify the axis for the data that
should be normalized which is typically
the features axis there are several
other parameters
you can optionally specify including two
called beta initializer and gamma
initializer these are the initializers
for the arbitrarily set parameters that
we mentioned when we were describing how
batch norm works these are set by
default to zero and one by Kerris but
you can optionally change these and set
them here along with several other
optionally specified parameters as well
and that's really all there is to it for
implementing batch norm and caris so I
hope in addition to this implementation
that you also now understand what batch
norm is how it works and why it makes
sense to apply it to a neural network
and I hope you found this video helpful
if you did please like the video
subscribe suggest and comment and thanks
for watching
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