tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf') Log probability density function. Args:

tf.contrib.distributions.Normal.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='Normal') Construct Normal

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.name

tf.contrib.rnn.GridLSTMCell.__init__(num_units, use_peepholes=False, share_time_frequency_weights=False, cell_clip=None, initializer=None, num_unit_shards=1, forget_bias=1

tf.contrib.graph_editor.OpMatcher.__call__(op) Evaluate if the op matches or not.

tf.contrib.framework.assert_same_float_dtype(tensors=None, dtype=None) Validate and return float type based on tensors

tf.contrib.distributions.Multinomial.pdf(value, name='pdf') Probability density function. Args:

tf.contrib.learn.extract_dask_labels(labels) Extract data from dask.Series for labels.

tf.contrib.distributions.ExponentialWithSoftplusLam.is_reparameterized

tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None) Most basic RNN: output = new_state = activation(W * input + U