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Add learning rate parameter to Perceptron training functions 

Co-authored-by: leestott <2511341+leestott@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot] 2025-10-03 16:21:23 +00:00
parent edc99e61db
commit d7ae722726
1 changed files with 20 additions and 7 deletions
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27
lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
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@ -232,7 +232,7 @@
},
"outputs": [],
"source": [
"def train(positive_examples, negative_examples, num_iterations = 100):\n",
"def train(positive_examples, negative_examples, num_iterations = 100, learning_rate = 0.01):\n",
" num_dims = positive_examples.shape[1]\n",
" \n",
" # Initialize weights. \n",
@ -251,11 +251,11 @@
"\n",
" z = np.dot(pos, weights) \n",
" if z < 0: # positive example was classified as negative\n",
" weights = weights + pos.reshape(weights.shape)\n",
" weights = weights + learning_rate * pos.reshape(weights.shape)\n",
"\n",
" z = np.dot(neg, weights)\n",
" if z >= 0: # negative example was classified as positive\n",
" weights = weights - neg.reshape(weights.shape)\n",
" weights = weights - learning_rate * neg.reshape(weights.shape)\n",
" \n",
" # Periodically, print out the current accuracy on all examples \n",
" if i % report_frequency == 0: \n",
@ -268,6 +268,19 @@
" return weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note on Learning Rate**: The `learning_rate` parameter (default `0.01`) controls how much we adjust the weights during each training step. This implements the gradient descent update formula:\n",
"\n",
"$$\\mathbf{w}^{\\tau + 1}=\\mathbf{w}^{\\tau} + \\eta \\mathbf{x}_{n} t_{n}$$\n",
"\n",
"- A larger learning rate (e.g., `1.0`) makes the perceptron learn faster but may overshoot the optimal solution\n",
"- A smaller learning rate (e.g., `0.001`) learns more slowly but may converge more precisely\n",
"- You can experiment by calling: `train(pos_examples, neg_examples, learning_rate=0.1)`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -435,7 +448,7 @@
},
"outputs": [],
"source": [
"def train_graph(positive_examples, negative_examples, num_iterations = 100):\n",
"def train_graph(positive_examples, negative_examples, num_iterations = 100, learning_rate = 0.01):\n",
" num_dims = positive_examples.shape[1]\n",
" weights = np.zeros((num_dims,1)) # initialize weights\n",
" \n",
@ -451,11 +464,11 @@
"\n",
" z = np.dot(pos, weights) \n",
" if z < 0:\n",
" weights = weights + pos.reshape(weights.shape)\n",
" weights = weights + learning_rate * pos.reshape(weights.shape)\n",
"\n",
" z = np.dot(neg, weights)\n",
" if z >= 0:\n",
" weights = weights - neg.reshape(weights.shape)\n",
" weights = weights - learning_rate * neg.reshape(weights.shape)\n",
" \n",
" if i % report_frequency == 0: \n",
" pos_out = np.dot(positive_examples, weights)\n",
@ -1077,4 +1090,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}