tf.contrib.graph_editor.Transformer.__call__(sgv, dst_graph, dst_scope, src_scope='', reuse_dst_scope=False) Execute the transformation

tf.contrib.layers.one_hot_encoding(*args, **kwargs) Transform numeric labels into onehot_labels using tf.one_hot.

tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See

tf.contrib.learn.monitors.CheckpointSaver.set_estimator(estimator) A setter called automatically by the target estimator.

class tf.contrib.learn.ModeKeys Standard names for model modes. The following standard keys are

tf.contrib.learn.LinearClassifier.get_variable_value(name)

tf.contrib.learn.monitors.NanLoss.post_step(step, session)

tf.SparseTensor.from_value(cls, sparse_tensor_value)

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n') Generate n

tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=True) Create a RNN cell composed sequentially of a number of RNNCells