class tf.FixedLenFeature Configuration for parsing a fixed-length input feature. To treat sparse input as dense, provide a default_value; otherwise, the parse functions will fail on any examples missing this feature. Fields: shape: Shape of input data. dtype: Data type of input. default_value: Value to be used if an example is missing this feature. It must be compatible with dtype.

tf.contrib.distributions.Gamma.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.OpError.error_code The integer error code that describes the error.

tf.contrib.learn.TensorFlowEstimator.fit(x, y, steps=None, monitors=None, logdir=None) Neural network model from provided model_fn and training data. Note: called first time constructs the graph and initializers variables. Consecutives times it will continue training the same model. This logic follows partial_fit() interface in scikit-learn. To restart learning, create new estimator. Args: x: matrix or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of features.

tf.contrib.distributions.Multinomial.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1

tf.decode_csv(records, record_defaults, field_delim=None, name=None) Convert CSV records to tensors. Each column maps to one tensor. RFC 4180 format is expected for the CSV records. (https://tools.ietf.org/html/rfc4180) Note that we allow leading and trailing spaces with int or float field. Args: records: A Tensor of type string. Each string is a record/row in the csv and all records should have the same format. record_defaults: A list of Tensor objects with types from: float32, int32, int64

tf.sparse_retain(sp_input, to_retain) Retains specified non-empty values within a SparseTensor. For example, if sp_input has shape [4, 5] and 4 non-empty string values: [0, 1]: a [0, 3]: b [2, 0]: c [3, 1]: d and to_retain = [True, False, False, True], then the output will be a SparseTensor of shape [4, 5] with 2 non-empty values: [0, 1]: a [3, 1]: d Args: sp_input: The input SparseTensor with N non-empty elements. to_retain: A bool vector of length N with M true values. Returns: A Spars

tf.contrib.distributions.Dirichlet.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.learn.LinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None) Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training. This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you wa

tf.contrib.rnn.LayerNormBasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, activation=tanh, layer_norm=True, norm_gain=1.0, norm_shift=0.0, dropout_keep_prob=1.0, dropout_prob_seed=None) Initializes the basic LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). input_size: Deprecated and unused. activation: Activation function of the inner states. layer_norm: If True, layer normalization wil