specifies the activation function for the neurons on each hidden layer. The following functions are available:

IDENTITY	specifies the identify function. Values range between -∞ and ∞. For an input value of t, the function returns the same value, t.
LOGISTIC	specifies the logistic function. Values range between zero and one. For an input value of t, the function returns .
EXP	specifies the exponential function. Values range between zero and ∞. For an input value of t, the function returns .
SIN	specifies the sine function. Values range between zero and 1, inclusively. For an input value of t, the function returns .
TANH	specifies the hyperbolic tangent function. Values range between –1 and 1. For an input value of t, the function returns .

specifies the network architecture to be trained. The GLIM architecture (Generalized Linear Model) specifies a two-layer perceptron (one is the input layer and the other is the output layer) without hidden layers or units. The MLP architecture specifies a multilayer perceptron with one or more hidden layers. The DIRECT architecture is an extension of MLP with direct connections between the input layer and the output layer.

specifies that predicted probabilities are added to the scored data for all the levels of the nominal target variable when scoring is performed with the TEMPTABLE and LASRANN= options. It adds variables _NN_Level_ and _NN_P_ to the results. You can use these predicted probabilities in an ASSESS statement.

specifies a fixed bias value for all the hidden and output neurons. In this case, the bias parameters are fixed and are not optimized.

requests that the server produce SAS scoring code based on the actions that it performed during the analysis. The server generates DATA step code. By default, the code is replayed as an ODS table by the procedure as part of the output of the statement. Frequently, you might want to write the scoring code to an external file by specifying options.

specifies the combination function for the neurons on each hidden layer. The available functions are described in the following list. When the COMBINATION= option is not specified, the hidden units use the LINEAR function.

ADD	adds all the incoming values without using any weights or biases. The function is defined as .
LINEAR	uses a linear combination of the incoming values and weights. The function is defined as
RADIAL	uses a radial basis function with equal heights and unequal widths for all units in the layer. The function is defined as

specifies the annealing parameter when performing a simulated annealing (SA) global optimization. Without the DELTA= option, the step size option (STEP=) and the temperature option (T=) are used to perform a Monte Carlo (MC) global optimization. With the addition of the DELTA= option, the optimization becomes simulated annealing where the temperature is scaled by DELTA × T at every MC step.

specifies to display the convergence status and iteration results for training the network.

specifies the error function to train the network. For interval variables, the default error function is NORMAL. For nominal variables, the default error function is ENTROPY. The available functions are as follows:

specifies a relative function convergence criterion.

specifies the formats for the input variables. If you do not specify the FORMATS= option, the default format is applied for that variable. Enclose each format specification in quotation marks and separate each format specification with a comma.

proc imstat data=lasr1.table1; 
    neural x / input=(a b) formats=("8.3", "$10");
quit;

specifies a relative gradient convergence criterion.

specifies the number of hidden neurons for each hidden layer in the feed-forward artificial neural network model. For example, HIDDEN=(5 3) specifies two hidden layers. The first hidden layer has five hidden neurons and the second has three hidden neurons.

specifies to impute the output values with available target values when data is being scored. In this case, you are interested only in predicting the missing values of the target variable. This option can be used only when scoring is performed with the TEMPTABLE and LASRANN= options.

specifies the input variables for the network. You can add the target variable to the input list if you want to assign a format to the target variable by using the FORMATS= option. The number of input neurons on the input layer is determined by the number of input variables. The number of input variables equals the total number of levels from the nominal variables plus the number of interval variables.

specifies the weight decay number. The value must be zero or greater.

specifies the table that contains the weights from a previously trained model. When the RESUME option is used with the LASRANN= option, training for that model resumes using the previously obtained weights as the new starting weights. Otherwise, the weights are used to score the active table.

specifies the line-search method for the CONGRA and QUANEW optimization techniques.

specifies the nodes to be included in the temporary table that is generated when you score a table with the NEURAL statement. When encoding of the input nodes is requested, the default is HIDDEN. This option is particularly useful when encoding is applied to reduce the dimension of the input nodes. By reusing the node output values, machine learning algorithms in the NEURAL, CLUSTER, DECISIONTREE, and RANDOMWOODS statements can use the newly encoded vectors as input.

specifies a lower bound for the network weights.

specifies the maximum number n of function calls in the iterative model fitting process. The default value depends on the optimization technique as follows:

specifies the maximum number of iterations in the model fitting process. The default value is 10 for all techniques.

specifies how to impute missing values for the input or target variables. When the MISSING= option is not specified, the observations with missing values are ignored. The MIN request imputes missing values for each variable with the minimum value. Similarly, the MAX request imputes the maximum value, and the MEAN request imputes the mean value. For nominal variables, a new missing category is created for the missing values.

specifies the number of networks to select out of the number of tries specified in the NUMTRIES= option. The networks with the smallest errors are selected as the set of best networks. When data is scored, the most frequent predicted values among the selected networks are used to make the final predictions. This option is required when performing Monte Carlo or simulated annealing optimizations. Those optimizations also use the DELTA=, STEP=, and T= options.

prevents the procedure from preparsing and pregenerating code for temporary expressions, scoring programs, and other user-written SAS statements.

specifies no bias parameters for the hidden and output units.

specifies the numeric variables to use as nominal variables. For the nominal input variables, neurons are created for every level. The values are coded as 0 or 1 indicator variables. Character variables are nominal and do not need to be included in the list.

specifies the number of optimizations to perform with different weight initializations when training networks. The network with the smallest error is selected as the best network. This option is required when performing Monte Carlo or simulated annealing global optimizations that also use the DELTA=, STEP=, and T= options.

specifies to resume a training optimization and use the weights that were obtained from previous training. The initial weights for resuming the optimization are read from a temporary table with the LASRANN= option. The specified framework of model options must be the same as the previous framework.

saves the result table so that you can use it in other IMSTAT procedure statements like STORE, REPLAY, and FREE. The value for table-name must be unique within the scope of the procedure execution. The name of a table that has been freed with the FREE statement can be used again in subsequent SAVE= options.

specifies the in-memory table that contains the scoring data. The table must exist in-memory on the server. The NEURAL statement in the IMSTAT procedure does not transfer a local data set to the server.

specifies the random number seed to use for generating random numbers. The random numbers are used to initialize the network weights.

specifies the standardization to use on the input interval variables.

specifies a step size for perturbations on the network weights when performing Monte Carlo or simulated annealing global optimizations.

specifies the artificial temperature parameter when performing Monte Carlo or simulated annealing global optimizations.

specifies the activation function for the neurons on the output layer. The available functions are IDENTITY, LOGISTIC, EXP, SIN, TANH, and SOFTMAX. The definitions of these functions are described in the ACTIVATION=(activation-function-for-a-hidden-layer ...) option. The SOFTMAX function is unique to this option and is described as follows:

SOFTMAX

performs the multiple logistic function. Values range between zero and one. For an input value of t, the function returns

specifies the combination function for the neurons on the target output nodes. The available functions are ADD, LINEAR, and RADIAL. The definitions of these functions are described in the COMBINATION=(combination-function-for-a-hidden-layer ...) option.

specifies the optimization technique for the iterative model-fitting process. The valid values are as follows:

specifies either a quoted string that contains the SAS expression that defines the temporary variables or a file reference to an external file with the SAS statements.

specifies the list of temporary variables for the request. Each temporary variable must be defined through SAS statements that you supply with the TEMPEXPRESS= option.

generates an in-memory temporary table from the result set. The IMSTAT procedure displays the name of the table and stores it in the &_TEMPLAST_ macro variable, provided that the statement executed successfully.

specifies the maximum number of seconds that the server should run the statement. If the time-out is reached, the server terminates the request and generates an error and error message. By default, there is no time-out.

specifies an upper bound for the network weights.

specifies the names of the variables to transfer from the active table to a temporary table that contains the scoring results. This option is ignored unless you score an in-memory table and the TEMPTABLE option is specified. The observations with these variables are copied to the generated temporary table.

specifies a variable to weight the prediction errors (the difference between the output of the network value and the target value specified in the input data set) for each observation during training.