tf.contrib.distributions.Categorical.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.ifft2d(input, name=None) Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most 2 dimensions of input. Args: input: A Tensor of type complex64. A complex64 tensor. name: A name for the operation (optional). Returns: A Tensor of type complex64. A complex64 tensor of the same shape as input. The inner-most 2 dimensions of input are replaced with their inverse 2D Fourier Transform.

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate along a new outer dimension. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) Evaluates given model with provided evaluation data. See superclass Estimator for more details. Args: x: features. y: targets. input_fn: Input function. feed_fn: Function creating a feed dict every time it is called. batch_size: minibatch size to use on the input. steps: Number of steps for which to evaluate model. metrics: Dict of metric ops t

tf.foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None) foldl on the list of tensors unpacked from elems on dimension 0. This foldl operator repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems on dimension 0. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invocation of fn. If initialize

tf.contrib.ffmpeg.decode_audio(contents, file_format=None, samples_per_second=None, channel_count=None) Create an op that decodes the contents of an audio file. Note that ffmpeg is free to select the "best" audio track from an mp4. https://trac.ffmpeg.org/wiki/Map Args: contents: The binary contents of the audio file to decode. This is a scalar. file_format: A string specifying which format the contents will conform to. This can be mp3, mp4, ogg, or wav. samples_per_second: The number of sa

tf.contrib.distributions.BernoulliWithSigmoidP.dtype The DType of Tensors handled by this Distribution.

tf.contrib.graph_editor.check_cios(control_inputs=False, control_outputs=None, control_ios=None) Do various check on control_inputs and control_outputs. Args: control_inputs: A boolean indicating whether control inputs are enabled. control_outputs: An instance of util.ControlOutputs or None. If not None, control outputs are enabled. control_ios: An instance of util.ControlOutputs or None. If not None, both control inputs and control outputs are enabled. This is equivalent to set control_inp

tf.contrib.rnn.LSTMBlockCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shap

tf.imag(input, name=None) Returns the imaginary part of a complex number. Given a tensor input of complex numbers, this operation returns a tensor of type float32 or float64 that is the imaginary part of each element in input. All elements in input must be complex numbers of the form (a + bj), where a is the real part and b is the imaginary part returned by this operation. For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] tf.imag(input) ==> [4.75, 5.75] Args: input: A Tensor.