mlffNetworkRead¶
Retrieves a neural network from a file previously saved using
mlffNetworkWrite.
Synopsis¶
mlffNetworkRead (filename)
Required Arguments¶
- char
filename(Input) - The name of an ASCII file containing a description of a trained neural
network previously saved using
mlffNetworkWrite. A complete or relative path can be used. If the optional argumentfileis used,filenameis ignored and the file is not closed before returning.
Return Value¶
An Imsls_d_NN_Network data structure containing the neural network stored
using mlffNetworkWrite. This space can be released by using the
free function.
Optional Arguments¶
t_print, (Input)Prints status of file open, reading and closing.
Default: No printing.
file, FILE (Input)- A
FILEpointer to a file opened for reading. This file is read but not closed. If this option is provided,filenameis 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 offopenandfclose.
Description¶
This function reads an Imsls_d_NN_Network data structure, a neural network
previously stored as an ASCII file using
mlffNetworkWrite. If the optional argument file
is provided, the data structure is read from that file stream and the file
stream is not closed. If this argument is not provided, mlffNetworkRead
opens a file using the path and name provided in filename, reads the
data structure from that file, and then closes the file before returning.
Examples¶
Example 1¶
This example reads a network previously trained using the Draper-Smith data.
These data consist of 13 patterns, each with four continuous attributes and
one dependent variable. The network was stored into 73 lines of an ASCII
file named NeuralNetworkEx1.txt using mlffNetworkWrite (see
Example 1 of mlffNetworkWrite).
from __future__ import print_function
from numpy import empty, double
from pyimsl.stat.dataSets import dataSets
from pyimsl.stat.mlffNetworkForecast import mlffNetworkForecast
from pyimsl.stat.mlffNetworkRead import mlffNetworkRead
filename = "NeuralNetworkEx1.txt"
n_patterns = 13
n_inputs = 4
categoricalAtt = None
continuous = empty((13, 4), dtype=double)
contAtt = empty((4), dtype=double)
y = empty((13), dtype=double)
forecasts = 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]
network = mlffNetworkRead(filename, t_print=True)
for i in range(0, n_patterns):
for j in range(0, n_inputs):
contAtt[j] = continuous[i, j]
forecast = mlffNetworkForecast(network, categoricalAtt, contAtt)
forecasts[i] = forecast[0]
print("OBS X1 X2 X3 X4 Y FORECAST")
ss = 0
for i in range(0, n_patterns):
ss = ss + (y[i] - forecasts[i]) * (y[i] - forecasts[i])
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]))
print("Sum of Squared Residuals: %7.2f\n" % (ss))
Output¶
Notice that the forecasts produced using mlffNetworkForecast are
identical to the original forecasts in
Example 1 of mlffNetworkWrite.
Attempting to open NeuralNetworkEx1.txt for
reading network data structure
File NeuralNetworkEx1.txt Successfully Opened
File NeuralNetworkEx1.txt closed
OBS X1 X2 X3 X4 Y FORECAST
0 7.00 26.00 6.00 60.00 78.50 78.50
1 1.00 29.00 15.00 52.00 74.30 74.30
2 11.00 56.00 8.00 20.00 104.30 104.30
3 11.00 31.00 8.00 47.00 87.60 87.60
4 7.00 52.00 6.00 33.00 95.90 95.90
5 11.00 55.00 9.00 22.00 109.20 109.20
6 3.00 71.00 17.00 6.00 102.70 102.70
7 1.00 31.00 22.00 44.00 72.50 72.50
8 2.00 54.00 18.00 22.00 93.10 93.10
9 21.00 47.00 4.00 26.00 115.90 115.90
10 1.00 40.00 23.00 34.00 83.80 83.80
11 11.00 66.00 9.00 12.00 113.30 113.30
12 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 read
multiple neural networks previously stored into a single file using
mlffNetworkWrite. Two networks were 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 to forecast the
dependent variable using all 4 inputs and the second using only the first 3.
The networks are read from an ASCII file previously created using
mlffNetworkWrite named NeuralNetworkEx2.txt (see
Example 2 of mlffNetworkWrite).
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.mlffNetworkRead import mlffNetworkRead
from pyimsl.stat.mlffNetworkForecast import mlffNetworkForecast
filename = "NeuralNetworkEx2.txt"
n_patterns = 13
n_inputs4 = 4
n_inputs3 = 3
n_cont4 = 4
n_cont3 = 3
n_outputs = 1
n_networks = 2
cont3 = zeros((13, 3), dtype=double)
cont4 = zeros((13, 4), dtype=double)
contAtt3 = zeros((3), dtype=double)
contAtt4 = zeros((4), dtype=double)
y = empty((13), dtype=double)
forecasts = empty((2, 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]
# open filestream
file = fopen(filename, "r")
print("File", filename, " Opened")
n_networks = 2
neural_network = []
print("Reading Networks and Preparing Forecasts...")
for n in range(0, n_networks):
network = mlffNetworkRead(None, file=file)
neural_network.append(network)
n_continuous = network[0].layers[0].n_nodes
print("Preparing forecasts for network with ",
n_continuous, "continuous attributes")
for i in range(0, n_patterns):
if (n_continuous == 3):
for j in range(0, 3):
contAtt3[j] = cont3[i, j]
forecast = mlffNetworkForecast(neural_network[n],
categoricalAtt, contAtt3)
else:
for j in range(0, 4):
contAtt4[j] = cont4[i, j]
forecast = mlffNetworkForecast(neural_network[n],
categoricalAtt, contAtt4)
forecasts[n, i] = forecast[0]
fclose(file)
print("File ", filename, "Closed.\n")
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] - forecasts[0, i]) * (y[i] - forecasts[0, i])
ss3 += (y[i] - forecasts[1, i]) * (y[i] - forecasts[1, 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], forecasts[1, i], forecasts[0, i]))
print("Sum of Squared Residuals for X1-X3: %7.2f" % (ss3))
print("Sum of Squared Residuals for X1-X4: %7.2f" % (ss4))
Output¶
Notice that the forecasts produced using mlffNetworkForecast are
identical to the original forecasts in
Example 2 of mlffNetworkWrite.
File NeuralNetworkEx2.txt Opened
Reading Networks and Preparing Forecasts...
Preparing forecasts for network with 4 continuous attributes
Preparing forecasts for network with 3 continuous attributes
File NeuralNetworkEx2.txt Closed.
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 78.50 78.57
1 1.00 29.00 15.00 52.00 74.30 74.30 74.18
2 11.00 56.00 8.00 20.00 104.30 104.30 104.37
3 11.00 31.00 8.00 47.00 87.60 87.60 87.61
4 7.00 52.00 6.00 33.00 95.90 95.90 95.85
5 11.00 55.00 9.00 22.00 109.20 109.19 109.20
6 3.00 71.00 17.00 6.00 102.70 102.70 102.70
7 1.00 31.00 22.00 44.00 72.50 72.50 72.53
8 2.00 54.00 18.00 22.00 93.10 93.10 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 83.80 83.84
11 11.00 66.00 9.00 12.00 113.30 113.30 113.35
12 10.00 68.00 8.00 12.00 109.40 109.40 109.40
Sum of Squared Residuals for X1-X3: 0.00
Sum of Squared Residuals for X1-X4: 0.04
Fatal Errors¶
IMSLS_FILE_OPEN_FAILURE |
Unable to open file for reading neural network. |