tensorflow::Env::Env()

class tf.contrib.learn.DNNRegressor A regressor for TensorFlow DNN models. Example: education = sparse_column_with_hash_bucket(column_name="education", hash_bucket_size=1000) occupation = sparse_column_with_hash_bucket(column_name="occupation", hash_bucket_size=1000) education_emb = embedding_column(sparse_id_column=education, dimension=16, combiner="sum") occupation_emb = e

tf.contrib.metrics.streaming_concat(values, axis=0, max_size=None, metrics_collections=None, updates_collections=None, name=None) Concatenate values along an axis across batches. The function streaming_concat creates two local variables, array and size, that are used to store concatenated values. Internally, array is used as storage for a dynamic array (if maxsize is None), which ensures that updates can be run in amortized constant time. For estimation of the metric over a stream of data, the

tf.contrib.metrics.set_intersection(a, b, validate_indices=True) Compute set intersection of elements in last dimension of a and b. All but the last dimension of a and b must match. Args: a: Tensor or SparseTensor of the same type as b. If sparse, indices must be sorted in row-major order. b: Tensor or SparseTensor of the same type as a. Must be SparseTensor if a is SparseTensor. If sparse, indices must be sorted in row-major order. validate_indices: Whether to validate the order and range

tf.contrib.layers.separable_convolution2d(*args, **kwargs) Adds a depth-separable 2D convolution with optional batch_norm layer. This op first performs a depthwise convolution that acts separately on channels, creating a variable called depthwise_weights. If num_outputs is not None, it adds a pointwise convolution that mixes channels, creating a variable called pointwise_weights. Then, if batch_norm_params is None, it adds bias to the result, creating a variable called 'biases', otherwise it a

tf.VarLenFeature.__getstate__() Exclude the OrderedDict from pickling

tf.contrib.distributions.Chi2WithAbsDf.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from Gamma: See the documentation for tf.random_gamma for more details. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.TensorArray.split(value, lengths, name=None) Split the values of a Tensor into the TensorArray. Args: value: (N+1)-D. Tensor of type dtype. The Tensor to split. lengths: 1-D. int32 vector with the lengths to use when splitting value along its first dimension. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the split occurs. Use this object all for subsequent operations. Raises: ValueError: if the shape inference fails.

tf.contrib.learn.monitors.ValidationMonitor.__init__(x=None, y=None, input_fn=None, batch_size=None, eval_steps=None, every_n_steps=100, metrics=None, early_stopping_rounds=None, early_stopping_metric='loss', early_stopping_metric_minimize=True, name=None) Initializes a ValidationMonitor. Args: x: See BaseEstimator.evaluate. y: See BaseEstimator.evaluate. input_fn: See BaseEstimator.evaluate. batch_size: See BaseEstimator.evaluate. eval_steps: See BaseEstimator.evaluate. every_n_steps: C

tf.image.draw_bounding_boxes(images, boxes, name=None) Draw bounding boxes on a batch of images. Outputs a copy of images but draws on top of the pixels zero or more bounding boxes specified by the locations in boxes. The coordinates of the each bounding box in boxes are encoded as [y_min, x_min, y_max, x_max]. The bounding box coordinates are floats in [0.0, 1.0] relative to the width and height of the underlying image. For example, if an image is 100 x 200 pixels and the bounding box is [0.1