Test with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL fann_type * FANN_API fann_test(
struct
fann
*
ann,
fann_type
*
input,
fann_type
*
desired_output
)
Test with a set of inputs, and a set of desired outputs.
float test_data(
const
training_data
&
data
)
Test a set of training data and calculates the MSE for the training data.
FANN_EXTERNAL float FANN_API fann_test_data(
struct
fann
*
ann,
struct
fann_train_data
*
data
)
Test a set of training data and calculates the MSE for the training data.
TFannNetwork is a Delphi component that encapsulates the Fann Library.
MSE does not decrease properly.
Threshold activation function.
Threshold activation function.
-support more cards and platforms -Implement training
void train(
fann_type
*
input,
fann_type
*
desired_output
)
Train one iteration with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL void FANN_API fann_train(
struct
fann
*
ann,
fann_type
*
input,
fann_type
*
desired_output
)
Train one iteration with a set of inputs, and a set of desired outputs.
Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set.
Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set.
The Training algorithms used when training on struct fann_train_data with functions like fann_train_on_data or fann_train_on_file.
float train_epoch(
const
training_data
&
data
)
Train one epoch with a set of training data.
FANN_EXTERNAL float FANN_API fann_train_epoch(
struct
fann
*
ann,
struct
fann_train_data
*
data
)
Train one epoch with a set of training data.
Standard backpropagation algorithm, where the weights are updated after each training pattern.
Standard backpropagation algorithm, where the weights are updated after each training pattern.
Constant array consisting of the names for the training algorithms, so that the name of an training function can be received by:
void train_on_data(
const
training_data
&
data,
unsigned
int
max_epochs,
unsigned
int
epochs_between_reports,
float
desired_error
)
Trains on an entire dataset, for a period of time.
FANN_EXTERNAL void FANN_API fann_train_on_data(
struct
fann
*
ann,
struct
fann_train_data
*
data,
unsigned
int
max_epochs,
unsigned
int
epochs_between_reports,
float
desired_error
)
Trains on an entire dataset, for a period of time.
void train_on_file(
const std::
string
&
filename,
unsigned
int
max_epochs,
unsigned
int
epochs_between_reports,
float
desired_error
)
Does the same as train_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_train_on_file(
struct
fann
*
ann,
const
char
*
filename,
unsigned
int
max_epochs,
unsigned
int
epochs_between_reports,
float
desired_error
)
Does the same as fann_train_on_data, but reads the training data directly from a file.
A more advanced batch training algorithm which achieves good results for many problems.
A more advanced batch training algorithm which achieves good results for many problems.
A more advanced batch training algorithm which achieves good results for many problems.
A more advanced batch training algorithm which achieves good results for many problems.
The following is a simple program which trains an ANN with a data set and then saves the ANN to a file.
The ANN cannot be trained in fixed point, which is why the training part is basically the same as for floating point numbers.
When training an ANN with a set of input and output data, we wish to adjust the weights in the ANN, to make the ANN give the same outputs as seen in the training data.
Normally it will be sufficient to use the fann_train_on_file training function, but sometimes you want to have more control and you will have to write a custom training loop.
The Training algorithms used when training on training_data with functions like neural_net::train_on_data or neural_net::train_on_file.
Encapsulation of a training data set struct fann_train_data and associated C API functions.