Keras compile

For example:. For readability purposes, I will focus on loss functions from now on. So if we want to use a common loss function such as MSE or Categorical Cross-entropy, we can easily do so by passing the appropriate name. For example, if we want for some reason to create a loss function that adds the mean square value of all activations in the first layer to the MSE:.

Note that we have created a function without limiting the number of arguments that returned a legitimate loss function, which has access to the arguments of its enclosing function. The previous example was rather a toy example for a not so useful use case. So when would we want to use such loss functions? You want your model to be able to reconstruct its inputs from the encoded latent space.

However, you also want your encoding in the latent space to be approximately normally distributed. For the latter, you will need to design a loss term for instance, Kullback Leibler loss that operates on the latent tensor. To give your loss function access to this intermediate tensor, the trick we have just learned can come in handy. As mentioned before, though examples are for loss functions, creating custom metric functions works in the same way.

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Subscribe to KDnuggets News. Photo Credit: Eyal Zakkay. Previous post. A Layman's Guide to Data Science. Math and Architectures of Deep Learning! Sign Up.You can either instantiate an optimizer before passing it to model.

In the latter case, the default parameters for the optimizer will be used. When writing a custom training loop, you would retrieve gradients via a tf.

keras compile

GradientTape instance, then call optimizer. You can use a learning rate schedule to modulate how the learning rate of your optimizer changes over time:. Check out the learning rate schedule API documentation for a list of available schedules. This is the second part of minimize. It returns an Operation that applies gradients. The method sums gradients from all replicas in the presence of tf. Strategy by default.

An Operation that applies the specified gradients. The iterations will be automatically increased by 1. The weights of an optimizer are its state ie, variables. This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.

For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:. This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.

For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:. Sequential model. Instantiate an optimizer.

Adam Iterate over the batches of a dataset.

Save and load models

GradientTape as tape : Forward pass.This chapter explains about how to compile the model. The compilation is the final step in creating a model. Once the compilation is done, we can move on to training phase. In machine learning, Loss function is used to find error or deviation in the learning process. Keras requires loss function during model compilation process.

In machine learning, Optimization is an important process which optimize the input weights by comparing the prediction and the loss function. Keras provides quite a few optimizer as a module, optimizers and they are as follows:.

In machine learning, Metrics is used to evaluate the performance of your model. It is similar to loss function, but not used in training process.

Keras provides quite a few metrics as a module, metrics and they are as follows. Keras model provides a method, compile to compile the model. The argument and default value of the compile method is as follows. Models are trained by NumPy arrays using fit. The main purpose of this fit function is used to evaluate your model on training. This can be also used for graphing model performance. Before creating a model, we need to choose a problem, need to collect the required data and convert the data to NumPy array.

keras compile

Once data is collected, we can prepare the model and train it by using the collected data. Data collection is one of the most difficult phase of machine learning. Keras provides a special module, datasets to download the online machine learning data for training purposes. It fetches the data from online server, process the data and return the data as training and test set. Let us check the data provided by Keras dataset module.

keras compile

The data available in the module are as follows. It contains 10 digits. It also contains 10, test images. Other dataset can also be fetched using similar API and every API returns similar data as well except the shape of the data. The shape of the data depends on the type of data.

Let us choose a simple multi-layer perceptron MLP as represented below and try to create the model using Keras. We have created the model, loaded the data and also trained the data to the model. We still need to evaluate the model and predict output for unknown input, which we learn in upcoming chapter. Keras - Model Compilation Advertisements. Previous Page. Next Page. Previous Page Print Page. Dashboard Logout.Keras is an API designed for human beings, not machines.

It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform.

keras compile

It's also easy to serve Keras models as via a web API. Keras is a central part of the tighly-connected TensorFlow 2. Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles.

Because of its ease-of-use and focus on user experience, Keras is the deep learning solution of choice for many university courses. It is widely recommended as one of the best ways to learn deep learning. Deep learning for humans. Iterate at the speed of thought. Exascale machine learning. Deploy anywhere. A vast ecosystem. State-of-the-art research. An accessible superpower.


Take it from our users. Aakash Nain Research Engineer " Keras is that sweet spot where you get flexibility for research and consistency for deployment.A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor. Note that there's also a corresponding pop method to remove layers: a Sequential model behaves very much like a list of layers.

Also note that the Sequential constructor accepts a name argument, just like any layer or model in Keras. This is useful to annotate TensorBoard graphs with semantically meaningful names. Generally, all layers in Keras need to know the shape of their inputs in order to be able to create their weights. So when you create a layer like this, initially, it has no weights:. It creates its weights the first time it is called on an input, since the shape of the weights depends on the shape of the inputs:.

Naturally, this also applies to Sequential models. When you instantiate a Sequential model without an input shape, it isn't "built": it has no weights and calling model. The weights are created when the model first sees some input data:.

However, it can be very useful when building a Sequential model incrementally to be able to display the summary of the model so far, including the current output shape. In this case, you should start your model by passing an Input object to your model, so that it knows its input shape from the start:. Note that the Input object is not displayed as part of model.

Models built with a predefined input shape like this always have weights even before seeing any data and always have a defined output shape. In general, it's a recommended best practice to always specify the input shape of a Sequential model in advance if you know what it is.

When building a new Sequential architecture, it's useful to incrementally stack layers with add and frequently print model summaries. For instance, this enables you to monitor how a stack of Conv2D and MaxPooling2D layers is downsampling image feature maps:.

This means that every layer has an input and output attribute. These attributes can be used to do neat things, like quickly creating a model that extracts the outputs of all intermediate layers in a Sequential model:.

Transfer learning consists of freezing the bottom layers in a model and only training the top layers. If you aren't familiar with it, make sure to read our guide to transfer learning.Model progress can be saved during—and after—training. This means a model can resume where it left off and avoid long training times.

Saving also means you can share your model and others can recreate your work. When publishing research models and techniques, most machine learning practitioners share:.

Sharing this data helps others understand how the model works and try it themselves with new data. This guide uses tf. To speed up these runs, use the first examples:. You can use a trained model without having to retrain it, or pick-up training where you left off—in case the training process was interrupted.

The tf. ModelCheckpoint callback allows to continually save the model both during and at the end of training. Create a tf. ModelCheckpoint callback that saves weights only during training:. This creates a single collection of TensorFlow checkpoint files that are updated at the end of each epoch:. Create a new, untrained model. When restoring a model from weights-only, you must have a model with the same architecture as the original model.

Since it's the same model architecture, you can share weights despite that it's a different instance of the model. Now rebuild a fresh, untrained model, and evaluate it on the test set. The callback provides several options to provide unique names for checkpoints and adjust the checkpointing frequency. The above code stores the weights to a collection of checkpoint -formatted files that contain only the trained weights in a binary format.

Checkpoints contain:. If you are only training a model on a single machine, you'll have one shard with the suffix:. You saw how to load the weights into a model.The purpose of loss functions is to compute the quantity that a model should seek to minimize during training.

Note that all losses are available both via a class handle and via a function handle. The class handles enable you to pass configuration arguments to the constructor e. Loss functions are typically created by instantiating a loss class e. All losses are also provided as function handles e. Note that this is an important difference between loss functions like tf. MeanSquaredError : the function version does not perform reduction, but by default the class instance does.

When using fitthis difference is irrelevant since reduction is handled by the framework. Note that sample weighting is automatically supported for any such loss. When writing the call method of a custom layer or a subclassed model, you may want to compute scalar quantities that you want to minimize during training e.

Keras - Model Compilation

Here's an example of a layer that adds a sparsity regularization loss based on the L2 norm of the inputs:. These losses are cleared by the top-level layer at the start of each forward pass -- they don't accumulate.

So layer. You would typically use these losses by summing them before computing your gradients when writing a training loop. When writing a custom training loop, you should retrieve these terms by hand from model. Losses The purpose of loss functions is to compute the quantity that a model should seek to minimize during training.

Available losses Note that all losses are available both via a class handle and via a function handle. Sequential model. SparseCategoricalCrossentropy model.

Adam Iterate over the batches of a dataset. GradientTape as tape : Forward pass. Probabilistic losses. Regression losses. Hinge losses for "maximum-margin" classification.

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