tf.image.decode_png(contents, channels=None, dtype=None, name=None) Decode a PNG-encoded image to a uint8 or uint16 tensor. The attr channels indicates the desired number of color channels for the decoded image. Accepted values are: 0: Use the number of channels in the PNG-encoded image. 1: output a grayscale image. 3: output an RGB image. 4: output an RGBA image. If needed, the PNG-encoded image is transformed to match the requested number of color channels. Args: contents: A Tensor of typ

Configuration information for a Session . Member Details Env* tensorflow::SessionOptions::env The environment to use. string tensorflow::SessionOptions::target The TensorFlow runtime to connect to. If 'target' is empty or unspecified, the local TensorFlow runtime implementation will be used. Otherwise, the TensorFlow engine defined by 'target' will be used to perform all computations. "target" can be either a single entry or a comma separated list of entries. Each entry is a resolvable address

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.segment_min(data, segment_ids, name=None) Computes the minimum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \min_j(data_j)\) where min is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. segment_ids: A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose rank is eq

tf.lbeta(x, name='lbeta') Computes ln(|Beta(x)|), reducing along the last dimension. Given one-dimensional z = [z_0,...,z_{K-1}], we define Beta(z) = \prod_j Gamma(z_j) / Gamma(\sum_j z_j) And for n + 1 dimensional x with shape [N1, ..., Nn, K], we define lbeta(x)[i1, ..., in] = Log(|Beta(x[i1, ..., in, :])|). In other words, the last dimension is treated as the z vector. Note that if z = [u, v], then Beta(z) = int_0^1 t^{u-1} (1 - t)^{v-1} dt, which defines the traditional bivariate beta func

tf.image.flip_up_down(image) Flip an image horizontally (upside down). Outputs the contents of image flipped along the first dimension, which is height. See also reverse(). Args: image: A 3-D tensor of shape [height, width, channels]. Returns: A 3-D tensor of the same type and shape as image. Raises: ValueError: if the shape of image not supported.

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.contrib.distributions.QuantizedDistribution.__init__(base_dist_cls, lower_cutoff=None, upper_cutoff=None, name='QuantizedDistribution', **base_dist_args) Construct a Quantized Distribution. Some properties are inherited from the distribution defining X. In particular, validate_args and allow_nan_stats are determined for this QuantizedDistribution by reading the additional kwargs passed as base_dist_args. Args: base_dist_cls: the base distribution class to transform. Must be a subclass of D

tf.contrib.distributions.Gamma.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.distributions.MultivariateNormalFull.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.