tf.image.encode_jpeg(image, format=None, quality=None, progressive=None, optimize_size=None, chroma_downsampling=None, density_unit=None, x_density=None, y_density=None, xmp_metadata=None, name=None) JPEG-encode an image. image is a 3-D uint8 Tensor of shape [height, width, channels]. The attr format can be used to override the color format of the encoded output. Values can be: '': Use a default format based on the number of channels in the image. grayscale: Output a grayscale JPEG image. Th

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.

tf.contrib.learn.monitors.ValidationMonitor.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.batch_matmul(x, y, adj_x=None, adj_y=None, name=None) Multiplies slices of two tensors in batches. Multiplies all slices of Tensor x and y (each slice can be viewed as an element of a batch), and arranges the individual results in a single output tensor of the same batch size. Each of the individual slices can optionally be adjointed (to adjoint a matrix means to transpose and conjugate it) before multiplication by setting the adj_x or adj_y flag to True, which are by default False. The inp

tf.contrib.distributions.MultivariateNormalDiag.mu

tf.contrib.rnn.LayerNormBasicLSTMCell.state_size

tf.contrib.learn.monitors.CaptureVariable.every_n_step_end(step, outputs)

tf.contrib.distributions.NormalWithSoftplusSigma.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.learn.monitors.ExportMonitor.post_step(step, session)