tf.contrib.learn.TensorFlowEstimator.__init__()

tf.contrib.learn.TensorFlowEstimator.__init__(model_fn, n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, class_weight=None, continue_training=False, config=None, verbose=1)

Initializes a TensorFlowEstimator instance.

Args:

• model_fn: Model function, that takes input x, y tensors and outputs prediction and loss tensors.
• n_classes: Number of classes in the target.
• batch_size: Mini batch size.
• steps: Number of steps to run over data.
• optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".

• learning_rate: If this is constant float value, no decay function is used. Instead, a customized decay function can be passed that accepts global_step as parameter and returns a Tensor. e.g. exponential decay function:

  def exp_decay(global_step):
      return tf.train.exponential_decay(
          learning_rate=0.1, global_step,
          decay_steps=2, decay_rate=0.001)
  

• clip_gradients: Clip norm of the gradients to this value to stop gradient explosion.

• class_weight: None or list of n_classes floats. Weight associated with classes for loss computation. If not given, all classes are supposed to have weight one.

• continue_training: when continue_training is True, once initialized model will be continuely trained on every call of fit.

• config: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc.

• verbose: Controls the verbosity, possible values:

  • 0: the algorithm and debug information is muted.
  • 1: trainer prints the progress.
  • 2: log device placement is printed.