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Of course, it wouldn't be a brutal melee game without violent executions to pull off against enemy players. We played the beta and we're ready to explain why executing in For Honor is so important and how to do it. It's quite simple: When you reduce an enemy player's health to zero in For Honor , their character will fall to his or her knees in defeat. Pressing one of those buttons will make your character execute the enemy in a flashy show of dominance. That's all well and good, but there's actually a strategic reason to do this.

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In For Honor , numbers are a huge deal. Being outnumbered when trying to capture an objective is usually going to result in your death. When you kill an enemy player without an execution, one of their teammates can resurrect them and give them a numbers advantage on an objective. Executions, however, disable resurrection and increase the executed player's respawn timer.

Dynamic control flow

A major benefit of eager execution is that all the functionality of the host language is available while your model is executing. So, for example, it is easy to write fizzbuzz :. Many machine learning models are represented by composing layers. When using TensorFlow with eager execution you can either write your own layers or use a layer provided in the tf. While you can use any Python object to represent a layer, TensorFlow has tf.

Layer as a convenient base class. Inherit from it to implement your own layer:. Use tf. Dense layer instead of MySimpleLayer above as it has a superset of its functionality it can also add a bias. When composing layers into models you can use tf. Sequential to represent models which are a linear stack of layers. It is easy to use for basic models:. Alternatively, organize models in classes by inheriting from tf.

Eager Execution

This is a container for layers that is a layer itself, allowing tf. Model objects to contain other tf. Model objects. It's not required to set an input shape for the tf. Model class since the parameters are set the first time input is passed to the layer. To share layer variables, share their objects. Automatic differentiation is useful for implementing machine learning algorithms such as backpropagation for training neural networks. During eager execution, use tf. GradientTape to trace operations for computing gradients later. GradientTape is an opt-in feature to provide maximal performance when not tracing.

Since different operations can occur during each call, all forward-pass operations get recorded to a "tape". To compute the gradient, play the tape backwards and then discard. A particular tf.

4 Ways to Be More Effective at Execution

GradientTape can only compute one gradient; subsequent calls throw a runtime error. The following example creates a multi-layer model that classifies the standard MNIST handwritten digits. It demonstrates the optimizer and layer APIs to build trainable graphs in an eager execution environment. While keras models have a builtin training loop using the fit method , sometimes you need more customization.

Here's an example, of a training loop implemented with eager:. Variable objects store mutable tf. Tensor values accessed during training to make automatic differentiation easier. The parameters of a model can be encapsulated in classes as variables. Better encapsulate model parameters by using tf.

Variable with tf. For example, the automatic differentiation example above can be rewritten:. With graph execution, program state such as the variables is stored in global collections and their lifetime is managed by the tf. Session object. In contrast, during eager execution the lifetime of state objects is determined by the lifetime of their corresponding Python object.

During eager execution, variables persist until the last reference to the object is removed, and is then deleted. Checkpoint can save and restore tf. Variable s to and from checkpoints:. To save and load models, tf. Checkpoint stores the internal state of objects, without requiring hidden variables. To record the state of a model , an optimizer , and a global step, pass them to a tf.

Checkpoint :. Update a metric by passing the new data to the callable, and retrieve the result using the tfe. TensorBoard is a visualization tool for understanding, debugging and optimizing the model training process.

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  4. It uses summary events that are written while executing the program. Summary operations, such as tf. For example, to record summaries once every global steps:. GradientTape can also be used in dynamic models. This example for a backtracking line search algorithm looks like normal NumPy code, except there are gradients and is differentiable, despite the complex control flow:. GradientTape is a powerful interface for computing gradients, but there is another Autograd -style API available for automatic differentiation. These functions are useful if writing math code with only tensors and gradient functions, and without tf.

    In the following example, tfe. To calculate the derivative of square at 3 , grad 3. Custom gradients are an easy way to override gradients in eager and graph execution. Within the forward function, define the gradient with respect to the inputs, outputs, or intermediate results. For example, here's an easy way to clip the norm of the gradients in the backward pass:.

    Custom gradients are commonly used to provide a numerically stable gradient for a sequence of operations:. Here, the log1pexp function can be analytically simplified with a custom gradient. The implementation below reuses the value for tf. Computation is automatically offloaded to GPUs during eager execution.

    If you want control over where a computation runs you can enclose it in a tf. Tensor object can be copied to a different device to execute its operations:. For compute-heavy models, such as ResNet50 training on a GPU, eager execution performance is comparable to graph execution. But this gap grows larger for models with less computation and there is work to be done for optimizing hot code paths for models with lots of small operations. While eager execution makes development and debugging more interactive, TensorFlow graph execution has advantages for distributed training, performance optimizations, and production deployment.

    However, writing graph code can feel different than writing regular Python code and more difficult to debug. For building and training graph-constructed models, the Python program first builds a graph representing the computation, then invokes Session. This provides:.

    Deploying code written for eager execution is more difficult: either generate a graph from the model, or run the Python runtime and code directly on the server. The same code written for eager execution will also build a graph during graph execution. Do this by simply running the same code in a new Python session where eager execution is not enabled.

    Most TensorFlow operations work during eager execution, but there are some things to keep in mind:. It's best to write code for both eager execution and graph execution. This gives you eager's interactive experimentation and debuggability with the distributed performance benefits of graph execution. Write, debug, and iterate in eager execution, then import the model graph for production deployment. Checkpoint to save and restore model variables, this allows movement between eager and graph execution environments.

    Selectively enable eager execution in a TensorFlow graph environment using tfe. This is used when tf. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies.