From c49f742921ea9222beb858dce0719741b4fa6ac2 Mon Sep 17 00:00:00 2001
From: Michael Mutote <130656746+mr1Michael@users.noreply.github.com>
Date: Wed, 29 Nov 2023 20:01:04 +0100
Subject: [PATCH] 22202956 - added plots and empty neural network file.
Narender to fill up
---
.../NN_with_Backtracking.py | 0
Reinforcement_Learning/Perceptrons.py | 47 ++++++++++++-------
Reinforcement_Learning/Solution_Testing_1.py | 17 +++++--
3 files changed, 42 insertions(+), 22 deletions(-)
create mode 100644 Reinforcement_Learning/NN_with_Backtracking.py
diff --git a/Reinforcement_Learning/NN_with_Backtracking.py b/Reinforcement_Learning/NN_with_Backtracking.py
new file mode 100644
index 0000000..e69de29
diff --git a/Reinforcement_Learning/Perceptrons.py b/Reinforcement_Learning/Perceptrons.py
index 6767165..7028ba4 100644
--- a/Reinforcement_Learning/Perceptrons.py
+++ b/Reinforcement_Learning/Perceptrons.py
@@ -1,8 +1,9 @@
import numpy as np
import Training_data
+import matplotlib.pyplot as plt
rng = np.random.default_rng(123)
-TEACHDATA = 9999
+TEACHDATA = 99999
TESTDATA = 999
T_NUMBER = 6 # Number to be detected 0-6
@@ -31,6 +32,14 @@ class Neuron:
self.input_count = input_count
self.activation = activation
self.weights = rng.random(input_count + 1)
+ self.errors = []
+
+ def plot_errors(self, titel):
+ plt.plot(self.errors)
+ plt.xlabel('Iteration')
+ plt.ylabel('Error')
+ plt.title(titel)
+ plt.show()
def test(self, data):
ix = np.insert(data.ravel(), 0, 1)
@@ -49,12 +58,13 @@ class ThresholdPerceptron(Neuron):
ix = np.insert(teach_data[j][i].ravel(), 0, 1)
RI = self.activation(ix.dot(self.weights))
if RI != T:
- delta = ETA * \
- (T - self.activation(ix.dot(self.weights))) * ix
+ err = T - self.activation(ix.dot(self.weights))
+ delta = ETA * err * ix
self.weights = self.weights + delta
- if np.linalg.norm(old_weights - self.weights) == 0.00:
- return self.weights
- return self.weights
+ self.errors.append(abs(err))
+ # if np.linalg.norm(old_weights - self.weights) == 0.00:
+ # return
+ return
class SGDPerceptron(Neuron):
@@ -65,16 +75,17 @@ class SGDPerceptron(Neuron):
for i in range(TEACHDATA):
old_weights = np.copy(self.weights)
delta = [0 for _ in range(len(old_weights))]
- for j in rng.choice(rng.permutation(len(teach_data)), 3):
+ for j in rng.choice(rng.permutation(len(teach_data)), 5):
T = (j == T_NUMBER)
ix = np.insert(teach_data[j][i].ravel(), 0, 1)
- z = ix.dot(self.weights)
- RI = self.activation(z)
- delta = ETA * (T - RI) * RI * (1 - RI) * ix
+ RI = self.activation(ix.dot(self.weights))
+ err = T - RI
+ delta = ETA * err * RI * (1 - RI) * ix
+ self.errors.append(abs(err))
self.weights += delta
- # if np.linalg.norm(old_weights - self.weights) == 0.00:
- # return self.weights
- return self.weights
+ if np.linalg.norm(old_weights - self.weights) == 0.00:
+ return
+ return
class LinearPerceptron(Neuron):
@@ -88,8 +99,10 @@ class LinearPerceptron(Neuron):
for j in rng.permutation(len(teach_data)):
T = (j == T_NUMBER)
ix = np.insert(teach_data[j][i].ravel(), 0, 1)
- delta += ETA * (T - self.activation(ix.dot(self.weights))) * ix
+ err = T - self.activation(ix.dot(self.weights))
+ delta += ETA * err * ix
+ self.errors.append(abs(err))
self.weights = self.weights + delta
- # if np.linalg.norm(old_weights - self.weights) == 0.00:
- # return self.weights
- return self.weights
+ if np.linalg.norm(old_weights - self.weights) == 0.00:
+ return
+ return
diff --git a/Reinforcement_Learning/Solution_Testing_1.py b/Reinforcement_Learning/Solution_Testing_1.py
index 3e57838..eb5d8d3 100644
--- a/Reinforcement_Learning/Solution_Testing_1.py
+++ b/Reinforcement_Learning/Solution_Testing_1.py
@@ -1,3 +1,9 @@
+# README
+# Threshold perceptron stagnates although not very accurate so the line
+# for ending the training on the threshold early was commented out.
+# Plot was added but commented out as the plots that happen too frequently cause an error.
+
+
import numpy as np
from prettytable import PrettyTable
import Perceptrons
@@ -10,7 +16,7 @@ trial_data = Perceptrons.test_data
REPETION = 1
-def run_test(neuron, learning_rate):
+def run_test(neuron, learning_rate, titel):
global table
table = PrettyTable()
table.field_names = ["ETA", "0", "1", "2", "3", "4", "5", "6"]
@@ -22,18 +28,19 @@ def run_test(neuron, learning_rate):
res[i] = res[i] + round(abs(neuron.test(trial_array)))
res = ["{:5d}".format(r) for r in res]
table.add_row([ETA] + res)
+ # neuron.plot_errors(titel + f"{ETA}")
print(table)
return neuron
-for i in range(7):
+for i in range(6):
Perceptrons.T_NUMBER = i
E = np.array([0.05, 0.1, 0.2, 0.4, 0.75, 1, 2, 5])
print("Threshold Perceptron is looking for: ", Perceptrons.T_NUMBER)
- run_test(Perceptrons.ThresholdPerceptron(20), E/4)
+ run_test(Perceptrons.ThresholdPerceptron(20), E/4, f"Threshold Perceptron digit {i} " )
print("Linear Perceptron is looking for: ", Perceptrons.T_NUMBER)
- run_test(Perceptrons.LinearPerceptron(20), E / 160)
+ run_test(Perceptrons.LinearPerceptron(20), E / 160, f"Linear Perceptron digit {i} ")
print("Sigmoid Gradient Descent Perceptron is looking for: ", Perceptrons.T_NUMBER)
- x = run_test(Perceptrons.SGDPerceptron(20), E)
+ x = run_test(Perceptrons.SGDPerceptron(20), E, f"SGD Perceptron digit {i} ")
--
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