tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See

tf.contrib.crf.crf_log_likelihood(inputs, tag_indices, sequence_lengths, transition_params=None) Computes the log-likehood of

tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf') Log cumulative distribution function.

tf.nn.rnn_cell.BasicRNNCell.state_size

tf.contrib.distributions.Binomial.name Name prepended to all ops created by this Distribution.

tf.ones_like(tensor, dtype=None, name=None, optimize=True) Creates a tensor with all elements set to 1. Given

tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None, feature_engineering_fn=None) Constructs

tf.contrib.framework.model_variable(*args, **kwargs) Gets an existing model variable with these parameters or creates a new one

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample') Generate samples

tf.contrib.losses.get_regularization_losses(scope=None) Gets the regularization losses. Args: