tf.contrib.metrics.aggregate_metric_map(names_to_tuples) Aggregates the metric names to tuple dictionary. This function is useful for pairing metric names with their associated value and update ops when the list of metrics is long. For example: metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map({ 'Mean Absolute Error': new_slim.metrics.streaming_mean_absolute_error( predictions, labels, weights), 'Mean Relative Error': new_slim.metrics.streaming_mean_relative_error( pred

tf.contrib.metrics.confusion_matrix(predictions, labels, num_classes=None, dtype=tf.int32, name=None, weights=None) Computes the confusion matrix from predictions and labels. Calculate the Confusion Matrix for a pair of prediction and label 1-D int arrays. Considering a prediction array such as: [1, 2, 3] And a label array such as: [2, 2, 3] The confusion matrix returned would be the following one: [[0, 0, 0] [0, 1, 0] [0, 1, 0] [0, 0, 1]] If weights is not None, then the confusion matrix

tf.image.rgb_to_hsv(images, name=None) Converts one or more images from RGB to HSV. Outputs a tensor of the same shape as the images tensor, containing the HSV value of the pixels. The output is only well defined if the value in images are in [0,1]. output[..., 0] contains hue, output[..., 1] contains saturation, and output[..., 2] contains value. All HSV values are in [0,1]. A hue of 0 corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. Args: images: A Tensor. Must be one o

tf.test.compute_gradient(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None) Computes and returns the theoretical and numerical Jacobian. If x or y is complex, the Jacobian will still be real but the corresponding Jacobian dimension(s) will be twice as large. This is required even if both input and output is complex since TensorFlow graphs are not necessarily holomorphic, and may have gradients not expressible as complex numbers. For example, if x is complex with shape [

tf.QueueBase.dequeue_many(n, name=None) Dequeues and concatenates n elements from this queue. This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. If the queue is closed and there are less than n elements left, then an OutOfRange exception is raised. At runtime, this operation may raise an error if the queue is closed before or during its executio

tf.constant(value, dtype=None, shape=None, name='Const') Creates a constant tensor. The resulting tensor is populated with values of type dtype, as specified by arguments value and (optionally) shape (see examples below). The argument value can be a constant value, or a list of values of type dtype. If value is a list, then the length of the list must be less than or equal to the number of elements implied by the shape argument (if specified). In the case where the list length is less than the

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.input_dict

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.learn.TensorFlowEstimator.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.