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fann_get_activation_steepness(3) [php man page]

FANN_GET_ACTIVATION_STEEPNESS(3)					 1					  FANN_GET_ACTIVATION_STEEPNESS(3)

fann_get_activation_steepness - Returns the activation steepness for supplied neuron and layer number

SYNOPSIS
float fann_get_activation_steepness (resource $ann, int $layer, int $neuron) DESCRIPTION
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0. It is not possible to get activation steepness for the neurons in the input layer. The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training. When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0). The default activation steepness is 0.5. PARAMETERS
o $ann -Neural network resource. o $layer - Layer number o $neuron - Neuron number RETURN VALUES
The activation steepness for the neuron or -1 if the neuron is not defined in the neural network, or FALSE on error. SEE ALSO
fann_set_activation_function(3), fann_set_activation_steepness_layer(3), fann_set_activation_steepness_hidden(3), fann_set_activa- tion_steepness_output(3), fann_set_activation_steepness(3). PHP Documentation Group FANN_GET_ACTIVATION_STEEPNESS(3)

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FANN_GET_CASCADE_NUM_CANDIDATES(3)					 1					FANN_GET_CASCADE_NUM_CANDIDATES(3)

fann_get_cascade_num_candidates - Returns the number of candidates used during training

SYNOPSIS
int fann_get_cascade_num_candidates (resource $ann) DESCRIPTION
The number of candidates used during training (calculated by multiplying fann_get_cascade_activation_functions_count(3), fann_get_cas- cade_activation_steepnesses_count(3) and fann_get_cascade_num_candidate_groups(3)). The actual candidates is defined by the fann_get_cascade_activation_functions(3) and fann_get_cascade_activation_steepnesses(3) arrays. These arrays define the activation functions and activation steepnesses used for the candidate neurons. If there are 2 activation functions in the activation function array and 3 steepnesses in the steepness array, then there will be 2x3=6 different candidates which will be trained. These 6 different candidates can be copied into several candidate groups, where the only difference between these groups is the initial weights. If the number of groups is set to 2, then the number of candidate neurons will be 2x3x2=12. The number of candidate groups is defined by fann_set_cascade_num_candidate_groups(3). The default number of candidates is 6x4x2 = 48 PARAMETERS
o $ann -Neural network resource. RETURN VALUES
The number of candidates used during training, or FALSE on error. SEE ALSO
fann_get_cascade_activation_functions(3), fann_get_cascade_activation_functions_count(3), fann_get_cascade_activation_steepnesses(3), fann_get_cascade_activation_steepnesses_count(3), fann_get_cascade_num_candidate_groups(3). PHP Documentation Group FANN_GET_CASCADE_NUM_CANDIDATES(3)
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