mlffNetworkWrite¶

Writes a trained neural network to an ASCII file for later retrieval using mlffNetworkRead.

Synopsis¶

mlffNetworkWrite (network, filename)

Required Arguments¶

Imsls_d_NN_Network network (Input): A trained neural network.
char filename (Input): The name of an ASCII file to be created. A complete or relative path can be used. If this file exists, it is replaced with a description of the neural network. If it does not exist, it is created. If the optional argument file is used, filename is ignored.

Optional Arguments¶

t_print, (Input)

Prints status of file open, writing and closing.

Default: no printing.

file, FILE (Input/Output)

A FILE pointer to a file opened for writing. This file is written but not closed. If this option is provided, filename is ignored. This option allows users to read additional user-defined data and multiple networks from the same file (see Example 2). To ensure this file is opened and closed with the same C run-time library used by the product, open and close this file using fopen and fclose instead of fopen and fclose.

Description¶

This function stores an Imsls_d_NN_Network data structure containing a trained neural network into an ASCII file. If the optional argument file is provided, mlffNetworkWrite writes the data structure and returns without closing the file. If this argument is not provided, mlffNetworkWrite creates a file using the path and name provided in filename, writes the data structure to that file, and then closes the file before returning.

Examples¶

Example 1¶

This example trains a network using the Draper-Smith data. These data consist of 13 patterns. The input attributes consist of four continuous attributes and one dependent variable. The network is stored into 73 lines of an ASCII file named NeuralNetworkEx1.txt.

from __future__ import print_function
from numpy import empty, double
from pyimsl.stat.dataSets import dataSets
from pyimsl.stat.mlffNetworkInit import mlffNetworkInit
from pyimsl.stat.mlffNetwork import mlffNetwork
from pyimsl.stat.mlffNetworkTrainer import mlffNetworkTrainer
from pyimsl.stat.mlffNetworkWrite import mlffNetworkWrite
from pyimsl.stat.randomSeedSet import randomSeedSet

filename = "NeuralNetworkEx1.txt"
n_patterns = 13
n_inputs = 4
n_continuous = 4
n_outputs = 1
nominalAtt = None
continuous = empty((13, 4), dtype=double)
y = empty((13), dtype=double)

draperSmithData = dataSets(5)

for i in range(0, n_patterns):
    y[i] = draperSmithData[i, 4]
    for j in range(0, 4):
        continuous[i, j] = draperSmithData[i, j]

networkStructure = mlffNetworkInit(n_inputs, n_outputs)
mlffNetwork(networkStructure, createHiddenLayer=4, linkAll=True)
randomSeedSet(5555)
forecasts = []
trainStats = mlffNetworkTrainer(networkStructure,
                                nominalAtt, continuous, y,
                                stageI={'nEpochs': 100, 'epochSize': 13},
                                forecasts=forecasts)

print("OBS    X1      X2      X3      X4      Y    FORECAST")

ss = 0
for i in range(0, n_patterns):
    ss = ss + (y[i] - forecasts[i][0]) * (y[i] - forecasts[i][0])
    print("%2d %7.2f %7.2f %7.2f %7.2f %7.2f %7.2f" %
          (i, continuous[i, 0], continuous[i, 1], continuous[i, 2],
           continuous[i, 3], y[i], forecasts[i][0]))

print("Sum of Squared Residuals: %7.2f\n" % (ss))

mlffNetworkWrite(networkStructure, filename)

Output¶

OBS    X1      X2      X3      X4      Y    FORECAST
  7.00   26.00    6.00   60.00   78.50   78.50
  1.00   29.00   15.00   52.00   74.30   74.30
 11.00   56.00    8.00   20.00  104.30  104.30
 11.00   31.00    8.00   47.00   87.60   87.60
  7.00   52.00    6.00   33.00   95.90   95.90
 11.00   55.00    9.00   22.00  109.20  109.20
  3.00   71.00   17.00    6.00  102.70  102.70
  1.00   31.00   22.00   44.00   72.50   72.50
  2.00   54.00   18.00   22.00   93.10   93.10
 21.00   47.00    4.00   26.00  115.90  115.90
  1.00   40.00   23.00   34.00   83.80   83.80
 11.00   66.00    9.00   12.00  113.30  113.30
 10.00   68.00    8.00   12.00  109.40  109.40
Sum of Squared Residuals:    0.00

Example 2¶

This example illustrates the use of the optional argument file to store multiple neural networks into one file. Two networks are trained using the Draper-Smith data. These data consist of 13 patterns each with four continuous attributes and one dependent variable. The first network is trained for forecasting the dependent variable using all four attributes, and the second is trained using only the first three. The networks are stored into 133 lines of an ASCII file named NeuralNetworkEx2.txt.

from __future__ import print_function
from numpy import empty, double, zeros
from pyimsl.stat.dataSets import dataSets
from pyimsl.stat.fclose import fclose
from pyimsl.stat.fopen import fopen
from pyimsl.stat.mlffNetwork import mlffNetwork
from pyimsl.stat.mlffNetworkInit import mlffNetworkInit
from pyimsl.stat.mlffNetworkFree import mlffNetworkFree
from pyimsl.stat.mlffNetworkTrainer import mlffNetworkTrainer
from pyimsl.stat.mlffNetworkWrite import mlffNetworkWrite
from pyimsl.stat.randomSeedSet import randomSeedSet

filename = "NeuralNetworkEx2.txt"
n_patterns = 13
n_inputs4 = 4
n_inputs3 = 3
n_cont4 = 4
n_cont3 = 3
n_outputs = 1
cont3 = zeros((13, 3), dtype=double)
cont4 = zeros((13, 4), dtype=double)
y = empty((13), dtype=double)

n_outputs = 1
categoricalAtt = None
draperSmithData = dataSets(5)

for i in range(0, n_patterns):
    y[i] = draperSmithData[i, 4]
    for j in range(0, 4):
        cont4[i, j] = draperSmithData[i, j]
        if (j < 3):
            cont3[i, j] = draperSmithData[i, j]

networkStructure = mlffNetworkInit(n_inputs4, n_outputs)
mlffNetwork(networkStructure, createHiddenLayer=4, linkAll=True)
randomSeedSet(5555)
forecasts4 = []
trainStats = mlffNetworkTrainer(networkStructure,
                                categoricalAtt, cont4, y,
                                maxStep=100.0,
                                stageI={'nEpochs': 50,
                                        'epochSize': n_patterns},
                                forecasts=forecasts4)

# open filestream
file = fopen(filename, "w")

# Write the number of network being placed into this file

print("Writing network for model with 4 continuous attributes")
mlffNetworkWrite(networkStructure, None, file=file)

# Create second neural network

mlffNetworkFree(networkStructure)
networkStructure = mlffNetworkInit(n_inputs3, n_outputs)
mlffNetwork(networkStructure, createHiddenLayer=4, linkAll=True)

randomSeedSet(5555)
forecasts3 = []
trainStats = mlffNetworkTrainer(networkStructure,
                                categoricalAtt, cont3, y,
                                maxStep=100.0,
                                stageI={'nEpochs': 50,
                                        'epochSize': n_patterns},
                                forecasts=forecasts3)

print("Writing network for model with 3 continuous attributes")
mlffNetworkWrite(networkStructure, None, file=file)
fclose(file)
print("File ", filename, " Closed.")
print("\nPrinting Forecasts for models with 3 and 4 continuous attributes:")
print("")
print("FORECAST  FORECAST")
print("OBS    X1      X2      X3      X4      Y   n_cont=3  n_cont=4")

ss4 = 0
ss3 = 0
for i in range(0, n_patterns):
    ss4 += (y[i] - forecasts4[i]) * (y[i] - forecasts4[i])
    ss3 += (y[i] - forecasts3[i]) * (y[i] - forecasts3[i])
    print("%2d %7.2f %7.2f %7.2f %7.2f %7.2f %7.2f %9.2f" %
          (i, cont4[i, 0], cont4[i, 1], cont4[i, 2],
           cont4[i, 3], y[i], forecasts3[i][0], forecasts4[i][0]))

print("Sum of Squared Residuals for X1-X3: %7.2f" % (ss3))
print("Sum of Squared Residuals for X1-X4: %7.2f" % (ss4))

Output¶

Writing network for model with 4 continuous attributes
Writing network for model with 3 continuous attributes
File  NeuralNetworkEx2.txt  Closed.

Printing Forecasts for models with 3 and 4 continuous attributes:

FORECAST  FORECAST
OBS    X1      X2      X3      X4      Y   n_cont=3  n_cont=4
 0    7.00   26.00    6.00   60.00   78.50   78.45     78.57
 1    1.00   29.00   15.00   52.00   74.30   74.36     74.18
 2   11.00   56.00    8.00   20.00  104.30  104.28    104.37
 3   11.00   31.00    8.00   47.00   87.60   87.61     87.61
 4    7.00   52.00    6.00   33.00   95.90   95.89     95.85
 5   11.00   55.00    9.00   22.00  109.20  109.19    109.20
 6    3.00   71.00   17.00    6.00  102.70  102.68    102.70
 7    1.00   31.00   22.00   44.00   72.50   72.49     72.53
 8    2.00   54.00   18.00   22.00   93.10   93.14     93.10
 9   21.00   47.00    4.00   26.00  115.90  115.90    115.81
10    1.00   40.00   23.00   34.00   83.80   83.76     83.84
11   11.00   66.00    9.00   12.00  113.30  113.29    113.35
12   10.00   68.00    8.00   12.00  109.40  109.41    109.40
Sum of Squared Residuals for X1-X3:    0.01
Sum of Squared Residuals for X1-X4:    0.04

Fatal Errors¶

IMSLS_FILE_OPEN_FAILURE Unable to open file for writing network.