/TensorFlow Python



Defined in tensorflow/contrib/layers/python/layers/optimizers.py.

See the guide: Layers (contrib) > Optimization

Given loss and parameters for optimizer, returns a training op.

Various ways of passing optimizers include:

  • by string specifying the name of the optimizer. See OPTIMIZER_CLS_NAMES for full list. E.g. optimize_loss(..., optimizer='Adam').
  • by function taking learning rate Tensor as argument and returning an Optimizer instance. E.g. optimize_loss(..., optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5)). Alternatively, if learning_rate is None, the function takes no arguments. E.g. optimize_loss(..., learning_rate=None, optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5)).
  • by a subclass of Optimizer having a single-argument constructor (the argument is the learning rate), such as AdamOptimizer or AdagradOptimizer. E.g. optimize_loss(..., optimizer=tf.train.AdagradOptimizer).
  • by an instance of a subclass of Optimizer. E.g., optimize_loss(..., optimizer=tf.train.AdagradOptimizer(0.5)).


  • loss: Scalar Tensor.
  • global_step: Scalar int Tensor, step counter to update on each step unless increment_global_step is False. If not supplied, it will be fetched from the default graph (see tf.train.get_global_step for details). If it has not been created, no step will be incremented with each weight update. learning_rate_decay_fn requires global_step.
  • learning_rate: float or Tensor, magnitude of update per each training step. Can be None.
  • optimizer: string, class or optimizer instance, used as trainer. string should be name of optimizer, like 'SGD', 'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant. class should be sub-class of tf.Optimizer that implements compute_gradients and apply_gradients functions. optimizer instance should be instantiation of tf.Optimizer sub-class and have compute_gradients and apply_gradients functions.
  • gradient_noise_scale: float or None, adds 0-mean normal noise scaled by this value.
  • gradient_multipliers: dict of variables or variable names to floats. If present, gradients for specified variables will be multiplied by given constant.
  • clip_gradients: float, callable or None. If float, is provided, a global clipping is applied to prevent the norm of the gradient to exceed this value. Alternatively, a callable can be provided e.g.: adaptive_clipping. This callable takes a list of (gradients, variables) tuples and returns the same thing with the gradients modified.
  • learning_rate_decay_fn: function, takes learning_rate and global_step Tensors, returns Tensor. Can be used to implement any learning rate decay functions. For example: tf.train.exponential_decay. Ignored if learning_rate is not supplied.
  • update_ops: list of update Operations to execute at each step. If None, uses elements of UPDATE_OPS collection. The order of execution between update_ops and loss is non-deterministic.
  • variables: list of variables to optimize or None to use all trainable variables.
  • name: The name for this operation is used to scope operations and summaries.
  • summaries: List of internal quantities to visualize on tensorboard. If not set, the loss, the learning rate, and the global norm of the gradients will be reported. The complete list of possible values is in OPTIMIZER_SUMMARIES.
  • colocate_gradients_with_ops: If True, try colocating gradients with the corresponding op.
  • increment_global_step: Whether to increment global_step. If your model calls optimize_loss multiple times per training step (e.g. to optimize different parts of the model), use this arg to avoid incrementing global_step more times than necessary.


Training op.


  • ValueError: if:
    • loss is an invalid type or shape.
    • global_step is an invalid type or shape.
    • learning_rate is an invalid type or value.
    • optimizer has the wrong type.
    • clip_gradients is neither float nor callable.
    • learning_rate and learning_rate_decay_fn are supplied, but no global_step is available.
    • gradients is empty.

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