Author: Awais Farooq
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Model plotting utilities
plot_model function Converts a Keras model to dot format and save to a file. Example Arguments Returns A Jupyter notebook Image object if Jupyter is installed. This enables in-line display of the model plots in notebooks. model_to_dot function Convert a Keras model to dot format. Arguments
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Random operations
categorical function Draws samples from a categorical distribution. This function takes as input logits, a 2-D input tensor with shape (batch_size, num_classes). Each row of the input represents a categorical distribution, with each column index containing the log-probability for a given class. The function will output a 2-D tensor with shape (batch_size, num_samples), where each row contains…
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SeedGenerator class
SeedGenerator class Generates variable seeds upon each call to a RNG-using function. In Keras, all RNG-using methods (such as keras.random.normal()) are stateless, meaning that if you pass an integer seed to them (such as seed=42), they will return the same values at each call. In order to get different values at each call, you must use a SeedGenerator instead as…
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Distribution utilities
set_distribution function Set the distribution as the global distribution setting. Arguments distribution function Retrieve the current distribution from global context. list_devices function Return all the available devices based on the device type. Note: in a distributed setting, global devices are returned. Arguments Return: List of devices that are available for distribute computation. initialize function Initialize the distribution system for…
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ModelParallel API
ModelParallel class Distribution that shards model variables. Compare to DataParallel which replicates the variables across all devices, ModelParallel allows you to shard variables in addition to the input data. To construct a ModelParallel distribution, you need to provide a DeviceMesh and a LayoutMap. Example You can quickly update the device mesh shape to change the sharding factor of the variables. E.g. To figure out a…
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ModelParallel API
ModelParallel class Distribution that shards model variables. Compare to DataParallel which replicates the variables across all devices, ModelParallel allows you to shard variables in addition to the input data. To construct a ModelParallel distribution, you need to provide a DeviceMesh and a LayoutMap. Example You can quickly update the device mesh shape to change the sharding factor of the variables. E.g. To figure out a…
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DataParallel API
DataParallel class Distribution for data parallelism. You can choose to create this instance by either specifying the device_mesh or devices arguments (but not both). The device_mesh argument is expected to be a DeviceMesh instance, and is expected to be 1D only. In case that the mesh has multiple axes, then the first axis will be treated as the data parallel dimension (and a warning…
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LayoutMap API
LayoutMap class A dict-like object that maps string to TensorLayout instances. LayoutMap uses a string as key and a TensorLayout as value. There is a behavior difference between a normal Python dict and this class. The string key will be treated as a regex when retrieving the value. See the docstring of get for more details. See below for a usage example. You…
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Mixed precision policy API
DTypePolicy class A dtype policy for a Keras layer. A dtype policy determines a layer’s computation and variable dtypes. Each layer has a policy. Policies can be passed to the dtype argument of layer constructors, or a global policy can be set with keras.config.set_dtype_policy. Arguments Typically you only need to interact with dtype policies when using mixed precision, which…
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InceptionResNetV2
InceptionResNetV2 function Instantiates the Inception-ResNet v2 architecture. Reference This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet. For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning. Note: each Keras Application expects a specific kind…