# Keras batch size

## What is batch size, steps, iteration, and epoch in the neural network?

Training a neural network model you usually update a metric of your model using some calculations on the data. When the size of your data is large it might need a lot of time to complete training and may consume a lot of resources.

Iterative calculations on a portion of the data to save time and computational resources. This portion calls the batch of data and the process is called batch data processing. That’s especially important if you are not able to fit the whole dataset in your machine’s memory.

Batch size is the number of items from the data to takes the training model. If you use the batch size of one you update weights after every sample. If you use batch size 32, you calculate the average error and then update weights every 32 items.

For instance, let’s say you have 24000 training samples and you want to set up a batch size equal to 32. The algorithm takes the first 32 samples from the training dataset and trains the network. Next, it takes the second 32 samples and trains the network again. We can keep doing this procedure until we have propagated all samples through the network.

Typically networks train faster with mini-batches. That’s because we update the weights after each batch.

### How to choose the batch size

When you put m examples in a mini-batch, you need to do O(m) computation and use O(m) memory, and you reduce the amount of uncertainty in the gradient by a factor of only O(sqrt(m)).

Using a larger batch decreases the quality of the model, as measured by its ability to generalize.

In contrast, small-batch methods consistently converge to flat minimizers this is due to the inherent noise in the gradient estimation. In terms of computational power, while the single-sample Stochastic Gradient Descent process takes more iterations, you end up getting there for less cost than the full batch mode.

Too small batch size has the risk of making learning too stochastic, faster but will converge to unreliable models, too big and it won’t fit into memory and still take ages. The higher the batch size, the more memory space you’ll need.

### What is epoch

Number epoch equal to the number of times the algorithm sees the entire data set. So, each time the algorithm has seen all samples in the dataset, one epoch has completed.

### What is iteration

Every time you pass a batch of data through the neural network, you completed one iteration. In the case of neural networks, that means the forward pass and backward pass. So, batch size * number of iterations = epoch

### Epoch vs iteration

One epoch includes all the training examples whereas one iteration includes only one batch of training examples.

### Steps vs Epoch in TensorFlow

Important different is that the one-step equal to process one batch of data, while you have to process all batches to make one epoch. Steps parameter indicating the number of steps to run over data.

A training step is one gradient update. In one step batch_size, many examples are processed.

An epoch consists of one full cycle through the training data. This are usually many steps. As an example, if you have 2,000 images and use a batch size of 10 an epoch consists of 2,000 images / (10 images / step) = 200 steps.

### Online Learning

Typically when people say online learning they mean batch_size=1. The idea behind online learning is that you update your model as soon as you see the example.

### How does batch size affect the performance of the model

Computing the gradient of a batch generally involves computing some function over each training example in the batch and summing over the functions. In particular, gradient computation is roughly linear in the batch size. So it’s going to take about 100x longer to compute the gradient of a 10,000-batch than a 100-batch.

The gradient of a single data point is going to be a lot noisier than the gradient of a 100-batch. This means that we won’t necessarily be moving down the error function in the direction of steepest descent.

If we used the entire training set to compute each gradient, our model would get stuck in the first valley because it would register a gradient of 0 at this point. If we use smaller mini-batches, on the other hand, we’ll get more noise in our estimate of the gradient. This noise might be enough to push us out of some of the shallow valleys in the error function.

In general, a batch size of 32 is a good starting point, and you should also try with 64, 128, and 256. Other values may be fine for some data sets, but the given range is generally the best to start experimenting with. Though, under 32, it might get too slow because of significantly lower computational speed, because of not exploiting vectorization to the full extent. If you get an “out of memory” error, you should try reducing the batch size.

Sours: https://androidkt.com/batch-size-step-iteration-epoch-neural-network/

## How big should batch size and number of epochs be when fitting a model in Keras?

Since you have a pretty small dataset (~ 1000 samples), you would probably be safe using a batch size of 32, which is pretty standard. It won't make a huge difference for your problem unless you're training on hundreds of thousands or millions of observations.

In general: Larger batch sizes result in faster progress in training, but don't always converge as fast. Smaller batch sizes train slower, but can converge faster. It's definitely problem dependent.

In general, the models improve with more epochs of training, to a point. They'll start to plateau in accuracy as they converge. Try something like 50 and plot number of epochs (x axis) vs. accuracy (y axis). You'll see where it levels out.

What is the type and/or shape of your data? Are these images, or just tabular data? This is an important detail.

Sours: https://stackoverflow.com/questions/35050753/how-big-should-batch-size-and-number-of-epochs-be-when-fitting-a-model-in-keras

## What is batch size in neural network?

The question has been asked a while ago but I think people are still tumbling across it. For me it helped to know about the mathematical background to understand batching and where the advantages/disadvantages mentioned in itdxer's answer come from. So please take this as a complementary explanation to the accepted answer.

Consider Gradient Descent as an optimization algorithm to minimize your Loss function $J(\theta)$. The updating step in Gradient Descent is given by

$$\theta_{k+1} = \theta_{k} - \alpha \nabla J(\theta)$$

For simplicity let's assume you only have 1 parameter ($n=1$), but you have a total of 1050 training samples ($m = 1050$) as suggested by itdxer.

In Batch Gradient Descent one computes the gradient for a batch of training samples first (represented by the sum in below equation, here the batch comprises all samples $m$ = full-batch) and then updates the parameter:

$$\theta_{k+1} = \theta_{k} - \alpha \sum^m_{j=1} \nabla J_j(\theta)$$

This is what is described in the wikipedia excerpt from the OP. For large number of training samples, the updating step becomes very expensive since the gradient has to be evaluated for each summand.

In Stochastic Gradient Descent one computes the gradient for one training sample and updates the paramter immediately. These two steps are repeated for all training samples.

$$\theta_{k+1} = \theta_{k} - \alpha \nabla J_j(\theta)$$

One updating step is less expensive since the gradient is only evaluated for a single training sample j.

Difference between both approaches

Updating Speed: Batch gradient descent tends to converge more slowly because the gradient has to be computed for all training samples before updating. Within the same number of computation steps, Stochastic Gradient Descent already updated the parameter multiple times. But why should we then even choose Batch Gradient Descent?

Convergence Direction: Faster updating speed comes at the cost of lower "accuracy". Since in Stochastic Gradient Descent we only incorporate a single training sample to estimate the gradient it does not converge as directly as batch gradient descent. One could say, that the amount of information in each updating step is lower in SGD compared to BGD.

The less direct convergence is nicely depicted in itdxer's answer. Full-Batch has the most direct route of convergence, where as mini-batch or stochastic fluctuate a lot more. Also with SDG it can happen theoretically happen, that the solution never fully converges.

Memory Capacity: As pointed out by itdxer feeding training samples as batches requires memory capacity to load the batches. The greater the batch, the more memory capacity is required.

Summary

In my example I used Gradient Descent and no particular loss function, but the concept stays the same since optimization on computers basically always comprises iterative approaches.

So, by batching you have influence over training speed (smaller batch size) vs. gradient estimation accuracy (larger batch size). By choosing the batch size you define how many training samples are combined to estimate the gradient before updating the parameter(s).

$\endgroup$Sours: https://stats.stackexchange.com/questions/153531/what-is-batch-size-in-neural-network

## Model training APIs

### method

Configures the model for training.

Example

Arguments

• optimizer: String (name of optimizer) or optimizer instance. See .
• loss: Loss function. Maybe be a string (name of loss function), or a instance. See . A loss function is any callable with the signature , where are the ground truth values, and are the model's predictions. should have shape (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape ). should have shape . The loss function should return a float tensor. If a custom instance is used and reduction is set to , return value has shape i.e. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses, unless is specified.
• metrics: List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a instance. See . Typically you will use . A function is any callable with the signature . To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as . You can also pass a list to specify a metric or a list of metrics for each output, such as or . When you pass the strings 'accuracy' or 'acc', we convert this to one of , , based on the loss function used and the model output shape. We do a similar conversion for the strings 'crossentropy' and 'ce' as well.
• loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.
• weighted_metrics: List of metrics to be evaluated and weighted by or during training and testing.
• run_eagerly: Bool. Defaults to . If , this 's logic will not be wrapped in a . Recommended to leave this as unless your cannot be run inside a . is not supported when using .
• steps_per_execution: Int. Defaults to 1. The number of batches to run during each call. Running multiple batches inside a single call can greatly improve performance on TPUs or small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if is set to , and methods will only be called every batches (i.e. before/after each execution).
• **kwargs: Arguments supported for backwards compatibility only.

Raises

• ValueError: In case of invalid arguments for , or .

### method

Trains the model for a fixed number of epochs (iterations on a dataset).

Arguments

• x: Input data. It could be:
• A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
• A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
• A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
• A dataset. Should return a tuple of either or .
• A generator or returning or .
• A , which wraps a callable that takes a single argument of type , and returns a . should be used when users prefer to specify the per-replica batching and sharding logic for the . See doc for more information. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below. If using , only type is supported for .
• y: Target data. Like the input data , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with (you cannot have Numpy inputs and tensor targets, or inversely). If is a dataset, generator, or instance, should not be specified (since targets will be obtained from ).
• batch_size: Integer or . Number of samples per gradient update. If unspecified, will default to 32. Do not specify the if your data is in the form of datasets, generators, or instances (since they generate batches).
• epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire and data provided. Note that in conjunction with , is to be understood as "final epoch". The model is not trained for a number of iterations given by , but merely until the epoch of index is reached.
• verbose: 'auto', 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 'auto' defaults to 1 for most cases, but 2 when used with . Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment).
• callbacks: List of instances. List of callbacks to apply during training. See . Note and callbacks are created automatically and need not be passed into . is created or not based on argument to . Callbacks with batch-level calls are currently unsupported with , and users are advised to implement epoch-level calls instead with an appropriate value.
• validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the and data provided, before shuffling. This argument is not supported when is a dataset, generator or instance. is not yet supported with .
• validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using or is not affected by regularization layers like noise and dropout. will override . could be: - A tuple of Numpy arrays or tensors. - A tuple of NumPy arrays. - A . - A Python generator or returning or . is not yet supported with .
• shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when is a generator or an object of tf.data.Dataset. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when is not .
• class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
• sample_weight: Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape , to apply a different weight to every timestep of every sample. This argument is not supported when is a dataset, generator, or instance, instead provide the sample_weights as the third element of .
• initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).
• steps_per_epoch: Integer or . Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the argument. If the training will run indefinitely with an infinitely repeating dataset. This argument is not supported with array inputs. When using : * is not supported.
• validation_steps: Only relevant if is provided and is a dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the dataset is exhausted. In the case of an infinitely repeated dataset, it will run into an infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time.
• validation_batch_size: Integer or . Number of samples per validation batch. If unspecified, will default to . Do not specify the if your data is in the form of datasets, generators, or instances (since they generate batches).
• validation_freq: Only relevant if validation data is provided. Integer or instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. runs validation at the end of the 1st, 2nd, and 10th epochs.
• max_queue_size: Integer. Used for generator or input only. Maximum size for the generator queue. If unspecified, will default to 10.
• workers: Integer. Used for generator or input only. Maximum number of processes to spin up when using process-based threading. If unspecified, will default to 1.
• use_multiprocessing: Boolean. Used for generator or input only. If , use process-based threading. If unspecified, will default to . Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.

Unpacking behavior for iterator-like inputs: A common pattern is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the argument of fit, which will in fact yield not only features (x) but optionally targets (y) and sample weights. Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length one tuple, effectively treating everything as 'x'. When yielding dicts, they should still adhere to the top-level tuple structure. e.g. . Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to . As a result the data processing code will simply raise a ValueError if it encounters a namedtuple. (Along with instructions to remedy the issue.)

Returns

A object. Its attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Raises

• RuntimeError: 1. If the model was never compiled or, 2. If is wrapped in .
• ValueError: In case of mismatch between the provided input data and what the model expects or when the input data is empty.

### method

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the arg.)

Arguments

• x: Input data. It could be:
• A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
• A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
• A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
• A dataset. Should return a tuple of either or .
• A generator or returning or . A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the section of .
• y: Target data. Like the input data , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with (you cannot have Numpy inputs and tensor targets, or inversely). If is a dataset, generator or instance, should not be specified (since targets will be obtained from the iterator/dataset).
• batch_size: Integer or . Number of samples per batch of computation. If unspecified, will default to 32. Do not specify the if your data is in the form of a dataset, generators, or instances (since they generate batches).
• verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
• sample_weight: Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape , to apply a different weight to every timestep of every sample. This argument is not supported when is a dataset, instead pass sample weights as the third element of .
• steps: Integer or . Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of . If x is a dataset and is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs.
• callbacks: List of instances. List of callbacks to apply during evaluation. See callbacks.
• max_queue_size: Integer. Used for generator or input only. Maximum size for the generator queue. If unspecified, will default to 10.
• workers: Integer. Used for generator or input only. Maximum number of processes to spin up when using process-based threading. If unspecified, will default to 1.
• use_multiprocessing: Boolean. Used for generator or input only. If , use process-based threading. If unspecified, will default to . Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.
• return_dict: If , loss and metric results are returned as a dict, with each key being the name of the metric. If , they are returned as a list.
• **kwargs: Unused at this time.

See the discussion of for .

is not yet supported with .

Returns

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute will give you the display labels for the scalar outputs.

Raises

• RuntimeError: If is wrapped in .
• ValueError: in case of invalid arguments.

### method

Generates output predictions for the input samples.

Computation is done in batches. This method is designed for performance in large scale inputs. For small amount of inputs that fit in one batch, directly using is recommended for faster execution, e.g., , or if you have layers such as that behaves differently during inference. Also, note the fact that test loss is not affected by regularization layers like noise and dropout.

Arguments

• x: Input samples. It could be:
• A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
• A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
• A dataset.
• A generator or instance. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the section of .
• batch_size: Integer or . Number of samples per batch. If unspecified, will default to 32. Do not specify the if your data is in the form of dataset, generators, or instances (since they generate batches).
• verbose: Verbosity mode, 0 or 1.
• steps: Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of . If x is a dataset and is None, will run until the input dataset is exhausted.
• callbacks: List of instances. List of callbacks to apply during prediction. See callbacks.
• max_queue_size: Integer. Used for generator or input only. Maximum size for the generator queue. If unspecified, will default to 10.
• workers: Integer. Used for generator or input only. Maximum number of processes to spin up when using process-based threading. If unspecified, will default to 1.
• use_multiprocessing: Boolean. Used for generator or input only. If , use process-based threading. If unspecified, will default to . Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.

See the discussion of for . Note that Model.predict uses the same interpretation rules as and , so inputs must be unambiguous for all three methods.

Returns

Numpy array(s) of predictions.

Raises

• RuntimeError: If is wrapped in .
• ValueError: In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size.

### method

Runs a single gradient update on a single batch of data.

Arguments

• x: Input data. It could be:
• A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
• A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
• A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
• y: Target data. Like the input data , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with (you cannot have Numpy inputs and tensor targets, or inversely).
• sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
• class_weight: Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
• reset_metrics: If , the metrics returned will be only for this batch. If , the metrics will be statefully accumulated across batches.
• return_dict: If , loss and metric results are returned as a dict, with each key being the name of the metric. If , they are returned as a list.

Returns

Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute will give you the display labels for the scalar outputs.

Raises

• RuntimeError: If is wrapped in .
• ValueError: In case of invalid user-provided arguments.

### method

Test the model on a single batch of samples.

Arguments

• x: Input data. It could be:
• A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
• A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
• A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
• y: Target data. Like the input data , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with (you cannot have Numpy inputs and tensor targets, or inversely).
• sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
• reset_metrics: If , the metrics returned will be only for this batch. If , the metrics will be statefully accumulated across batches.
• return_dict: If , loss and metric results are returned as a dict, with each key being the name of the metric. If , they are returned as a list.

Returns

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute will give you the display labels for the scalar outputs.

Raises

• RuntimeError: If is wrapped in .
• ValueError: In case of invalid user-provided arguments.

### method

Returns predictions for a single batch of samples.

Arguments

• x: Input data. It could be:
• A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
• A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).

Returns

Numpy array(s) of predictions.

Raises

• RuntimeError: If is wrapped in .
• ValueError: In case of mismatch between given number of inputs and expectations of the model.

### property

Settable attribute indicating whether the model should run eagerly.

Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls.

By default, we will attempt to compile your model to a static graph to deliver the best execution performance.

Returns

Boolean, whether the model should run eagerly.

Sours: https://keras.io/api/models/model_training_apis

## Machine Learning & Deep Learning Fundamentals

### Batch size in artificial neural networks

In this post, we'll discuss what it means to specify a batch size as it pertains to training an artificial neural network, and we'll also see how to specify the batch size for our model in code using Keras.

In our previous post on how an artificial neural network learns, we saw that when we train our model, we have to specify a batch size. Let's go ahead and discuss the details about this now.

### Introducing batch size

Put simply, the batch size is the number of samples that will be passed through to the network at one time. Note that a batch is also commonly referred to as a mini-batch.

The batch size is the number of samples that are passed to the network at once.

Now, recall that an epoch is one single pass over the entire training set to the network. The batch size and an epoch are not the same thing. Let's illustrate this with an example.

#### Batches in an epoch

Let's say we have images of dogs that we want to train our network on in order to identify different breeds of dogs. Now, let's say we specify our batch size to be . This means that images of dogs will be passed as a group, or as a batch, at one time to the network.

Given that a single epoch is one single pass of all the data through the network, it will take batches to make up full epoch. We have images divided by a batch size of , which equals total batches.

batches in epoch = training set size / batch_size

Ok, we have the idea of batch size down now, but what's the point? Why not just pass each data element one-by-one to our model rather than grouping the data in batches?

#### Why use batches?

Well, for one, generally the larger the batch size, the quicker our model will complete each epoch during training. This is because, depending on our computational resources, our machine may be able to process much more than one single sample at a time.

The trade-off, however, is that even if our machine can handle very large batches, the quality of the model may degrade as we set our batch larger and may ultimately cause the model to be unable to generalize well on data it hasn't seen before.

In general, the batch size is another one of the hyperparameters that we must test and tune based on how our specific model is performing during training. This parameter will also have to be tested in regards to how our machine is performing in terms of its resource utilization when using different batch sizes.

For example, if we were to set our batch size to a relatively high number, say , then our machine may not have enough computational power to process all images in parallel, and this would suggest that we need to lower our batch size.

Additionally, note if using mini-batch gradient descent, which is normally the type of gradient descent algorithm used by most neural network APIs like Keras by default, the gradient update will occur on a per-batch basis. The size of these batches is determined by the batch size.

Alright, we should now have a general idea about what batch size is. Let's see how we specify this parameter in code now using Keras.

### Working with batch size in Keras

We'll be working with the same model we've used in the last several posts. This is just an arbitrary model.

model = Sequential([ Dense(units=16, input_shape=(1,), activation='relu'), Dense(units=32, activation='relu', kernel_regularizer=regularizers.l2(0.01)), Dense(units=2, activation='sigmoid') ])

Let's focus our attention on where we call . We know this is the function we call to train our model, and we saw this in action in our previous post on how an artificial neural network learns.

model.fit( x=scaled_train_samples, y=train_labels, validation_data=valid_set, batch_size=10, epochs=20, shuffle=True, verbose=2 )

This function accepts a parameter called . This is where we specify our for training. In this example, we've just arbitrarily set the value to .

Now, during the training of this model, we'll be passing in samples at a time until we eventually pass in all the training data to complete one single epoch. Then, we'll start the same process over again to complete the next epoch.

That's really all there is to it for specifying the batch size for training a model in Keras!

### Wrapping up

Hopefully now we have a general understanding of what the batch size is and how to specify it in Keras. I'll see you in the next one!

Sours: https://deeplizard.com/learn/video/U4WB9p6ODjM
15. Batch Size and Learning Rate in CNNs
object

Model to train.

x

Vector, matrix, or array of training data (or list if the model has multiple inputs). If all inputs in the model are named, you can also pass a list mapping input names to data. can be (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).

y

Vector, matrix, or array of target (label) data (or list if the model has multiple outputs). If all outputs in the model are named, you can also pass a list mapping output names to data. can be (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).

batch_size

Integer or . Number of samples per gradient update. If unspecified, will default to 32.

epochs

Number of epochs to train the model. Note that in conjunction with , is to be understood as "final epoch". The model is not trained for a number of iterations given by , but merely until the epoch of index is reached.

verbose

Verbosity mode (0 = silent, 1 = progress bar, 2 = one line per epoch).

callbacks

List of callbacks to be called during training.

view_metrics

View realtime plot of training metrics (by epoch). The default () will display the plot when running within RStudio, were specified during model , and . Use the global option to establish a different default.

validation_split

Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the and data provided, before shuffling.

validation_data

Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be a list (x_val, y_val) or a list (x_val, y_val, val_sample_weights). will override .

shuffle

shuffle: Logical (whether to shuffle the training data before each epoch) or string (for "batch"). "batch" is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when is not .

class_weight

Optional named list mapping indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.

sample_weight

Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify in .

initial_epoch

Integer, Epoch at which to start training (useful for resuming a previous training run).

steps_per_epoch

Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined.

validation_steps

Only relevant if is specified. Total number of steps (batches of samples) to validate before stopping.

...

Unused

A object that contains all information collected during training.

Other model functions: , , , , , , , , , , , , , , ,

Sours: https://keras.rstudio.com/reference/fit.html

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