NET-QUANTITIES: Quantities from log files relating to networks. The quantities below relating to neural network models can be obtained from log files (eg, for use in net-plt). Note that the generic quantities documented in quantities.doc and the quantities relating to Markov chain methods documented in mc-quantities.doc will also be available. xn Array of n'th input values for training cases Xn Array of n'th input values for test cases in Same as xn, except n must not be omitted In Same as Xn, except n must not be omitted on Array of n'th output values for training cases On Array of n'th output values for test cases yn Array of n'th "guessed" values for training cases Yn Array of n'th "guessed" values for test cases gn Array of n'th randomly "guessed" values for training cases Gn Array of n'th randomly "guessed" values for test cases z[n] Array of n'th target values for training cases Z[n] Array of n'th target values for test cases tn Same as zn, except n must not be omitted Tn Same as Zn, except n must not be omitted P Log prior probability of the network parameters with current hyperparameter values l Array of minus log probabilities for training cases. If not specified to be an array, the average minus log probability over training cases. L Array of minus log probabilities for test cases. If not specified to be an array, the average minus log probability over test cases. a[n] Array of absolute errors of n'th target for training cases, or sum of absolute errors if 'n' not specified. If not specified to be an array, the average error over training cases. A[n] Array of absolute errors of n'th target for test cases, or sum of absolute errors if 'n' not specified. If not specified to be an array, the average error over test cases. b[n] Array of squared errors of n'th target for training cases, or sum of squared errors if 'n' not specified. If not specified to be an array, the average error over training cases. B[n] Array of squared errors of n'th target for test cases, or sum of squared errors if 'n' not specified. If not specified to be an array, the average error over test cases. c Expected classification error on training set, assuming that the model is right. C Expected classification error on test set, assuming that the model is right. vn Array of std. dev. over training set of units in hidden layer n; average std. dev. if scalar. Vn Array of std. dev. over test set of units in hidden layer n; average std. dev. if scalar. hn Value of n'th top-level (common) hyperparameter if scalar, or array of associated unit-level hyperparameters. The indexes used are as displayed with net-display. It is not valid to use the scalar form for a group of adjustments. Noise standard deviations cannot be accessed in this way - use 'n'. wn Array of values for n'th group of parameters for network. The indexes used are as displayed with net-display. Wn Square root of average squared magnitude for n'th group of parameters if scalar, or array of square roots of average squared magnitudes of parameters in group associated with each source unit. The indexes used are as displayed with net-display. Mn Magnitudes of last hidden layer to output weights to n'th output, sorted in decreasing order of size n For regression model: Common noise std. dev. if scalar, or array of output-specific noise levels. For class model: expected entropy on training cases, assuming the model is right. N For regression model: Common noise variance if scalar, or array of output-specific noise levels. For class model: expected entropy on test cases, assuming model is right. When a value for 'n' is needed but not specified, it defaults to zero. This isn't allowed with 'i', 'I', 't', and 'T', however, since they have other meanings when 'n' is omitted. The 'o' values are the values of the units in the final layer, before interpretation by the model. The 'y' values are the same as the 'o' values for regression models. For binary and multi-class models, the 'y' values are the bit/class probabilities obtained from the 'o' values. The 'g' values are taken randomly from the predictive distribution. For multi-class models, the 'a', 'A', 'b', and 'B' quantities are based on the difference between the vector of probabilities for the classes and the vector of 0s and 1s with a single 1 indicating the correct class. Depending on whether any training/test cases are available, and if they are, whether they include targets, some of the above quantities may not be defined. The 'c' and 'C' quantities are not defined for regression models. The 'o' quantities are allowed with survival models only when the hazard is constant (independent of time). For multi-class models, the 'z' and 'Z' quantities with a modifier ('n') refer to the representation of the class as a binary vector with the true class indicated by a one; with no modifier, they refer to the numeric value of the class. (Since 'n' can't be omitted for 't' and 'T', these synonyms can't be used for 'z' and 'Z' with no modifier.) For censored survival data, the target for the 'A', 'a', 'B', 'b', 'Z', and 'z' options is taken to be the censoring time, which is not very meaningful. The quantities relating to test cases use the test set specified using data-spec by default. A different test set can be used by including arguments of the form / test-inputs [ test-targets ] as the last arguments of the command that accesses these quantities (eg, net-plt). Copyright (c) 1995-2004 by Radford M. Neal