In general, you won't have to create your own losses, metrics, or optimizersįrom scratch, because what you need is likely to be already part of the Keras API: compile ( optimizer = "rmsprop", loss = "sparse_categorical_crossentropy", metrics =, ) return model Many built-in optimizers, losses, and metrics are available Model ( inputs = inputs, outputs = outputs ) return model def get_compiled_model (): model = get_uncompiled_model () model. Dense ( 10, activation = "softmax", name = "predictions" )( x ) model = keras. Dense ( 64, activation = "relu", name = "dense_2" )( x ) outputs = layers. Dense ( 64, activation = "relu", name = "dense_1" )( inputs ) x = layers. Input ( shape = ( 784 ,), name = "digits" ) x = layers. Loss, and metrics can be specified via string identifiers as a shortcut:ĭef get_uncompiled_model (): inputs = keras. Note that if you're satisfied with the default settings, in many cases the optimizer, You will find more details about this in the Passing data to multi-input, If your model has multiple outputs, you can specify different losses and metrics forĮach output, and you can modulate the contribution of each output to the total loss of The metrics argument should be a list - your model can have any number of metrics. Falling back to the legacy Keras optimizer, i.e., (). WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. WARNING:absl:At this time, the v2.11+ optimizer (/api/optimizers/rmsprop#rmsprop-class) runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at (). Let's consider the following model (here, we build in with the Functional API, but itĬould be a Sequential model or a subclassed model as well):
Test data generator keras how to#
Order to demonstrate how to use optimizers, losses, and metrics. In the next few paragraphs, we'll use the MNIST dataset as NumPy arrays, in NumPy arrays (if your data is small and fits in memory) or tf.data.Dataset When passing data to the built-in training loops of a model, you should either use Guide to multi-GPU & distributed training. This guide doesn't cover distributed training, which is covered in our Sequential models, models built with the Functional API, and models written from In general, whether you are using built-in loops or writing your own, model training &Įvaluation works strictly in the same way across every kind of Keras model. If you are interested in writing your own training & evaluation loops from If you are interested in leveraging fit() while specifying your When using built-in APIs for training & validation (such as Model.fit(),
This guide covers training, evaluation, and prediction (inference) models Import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers
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