Fundamentals Of Neural Networks Laurene Fausett Solution Manual Pdf.66golkes 🌠

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Fundamentals Of Neural Networks Laurene Fausett Solution Manual Pdf.66golkes

A:

This question was completely answered by @christian, just wanted to add some code that I used to make the problem much easier to solve.
For the following dataset, the network was trained on a series of 6464 frames with 2 pixels. The network was trained for 300 epochs on a laptop with two 6 core processors and 16 GB of memory.
Full dataset:
2,304,048

Part dataset:
625,320

The network was using the Levenberg Marquardt (LM) algorithm with the following parameters:
F = 0.8;
Fp = 0.5;
c = 1.2;
sigma = 0.3;
epsilon = 0.1;

For the full dataset, with the LM algorithm the network was able to converge in 4 seconds, while the maximum training iteration took a few days, so I switched to SPSA to speed up the training.
SPSA training parameters:
alpha = 0.999;
beta = 0.1;
max_it = 20;

Since LM training usually converges on local optima, I tested the network with 20 different starting points, and the one that finished fastest was the one I used for the example above.
The LM algorithm converges really fast, usually on the first 20 iterations. After that, it takes longer to find the optimal training set.
Here is the code I used to train the network:
import cv2
import numpy as np
from pylab import *

# Read frames
frames = []

# Read and preprocess frame files
for filename in glob.glob(“*-frames*”):
frames.append(cv2.imread(filename))

# Convert to numpy arrays
frames = np.asarray(frames)

# Find max value for each channel
max_frame = frames.max(axis = 2)

# Subtract away frame from max
frames = frames – max_frame

# Write frame files
cv2.imwrite(“{}-frames.jpg”.format(“./data/frames/” + str(frames.shape[0])), frames)

# Read dataset
frame_dataset = []

# Create a counter that starts at zero and counts down for each frame
cnt = 0

# Loop

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