tf.contrib.learn.LinearClassifier.weights_

tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32) Returns an initializer performing "Xavier" initialization for weights. This function implements the weight initialization from: Xavier Glorot and Yoshua Bengio (2010): Understanding the difficulty of training deep feedforward neural networks. International conference on artificial intelligence and statistics. This initializer is designed to keep the scale of the gradients roughly the same in all layers. In uniform

tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.learn.monitors.ExportMonitor.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

tf.contrib.training.NextQueuedSequenceBatch.save_state(state_name, value, name=None) Returns an op to save the current batch of state state_name. Args: state_name: string, matches a key provided in initial_states. value: A Tensor. Its type must match that of initial_states[state_name].dtype. If we had at input: initial_states[state_name].get_shape() == [d1, d2, ...] then the shape of value must match: tf.shape(value) == [batch_size, d1, d2, ...] name: string (optional). The name scope for

tf.contrib.graph_editor.remove_control_inputs(op, cops) Remove the control inputs cops from co. Warning: this function is directly manipulating the internals of the tf.Graph. Args: op: a tf.Operation from which to remove the control inputs. cops: an object convertible to a list of tf.Operation. Raises: TypeError: if op is not a tf.Operation ValueError: if any cop in cops is not a control input of op.

tf.contrib.graph_editor.Transformer.__call__(sgv, dst_graph, dst_scope, src_scope='', reuse_dst_scope=False) Execute the transformation. Args: sgv: the source subgraph-view. dst_graph: the destination graph. dst_scope: the destination scope. src_scope: the source scope, which specify the path from which the relative path of the transformed nodes are computed. For instance, if src_scope is a/ and dst_scoped is b/, then the node a/x/y will have a relative path of x/y and will be transformed

tf.where(input, name=None) Returns locations of true values in a boolean tensor. This operation returns the coordinates of true elements in input. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in input. Indices are output in row-major order. Fo

tf.contrib.learn.monitors.CheckpointSaver.__init__(checkpoint_dir, save_secs=None, save_steps=None, saver=None, checkpoint_basename='model.ckpt', scaffold=None) Initialize CheckpointSaver monitor. Args: checkpoint_dir: str, base directory for the checkpoint files. save_secs: int, save every N secs. save_steps: int, save every N steps. saver: Saver object, used for saving. checkpoint_basename: str, base name for the checkpoint files. scaffold: Scaffold, use to get saver object. Raises: