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update assignment 5

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ligerfotis 2023-05-15 12:22:10 +02:00
parent 240b9d8695
commit 89a3d15e0e
1 changed files with 19 additions and 6 deletions
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assignment 5/iml_assignmnet5_unsolved.ipynb
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@ -75,15 +75,28 @@
"outputs": [],
"source": [
"# Define the perceptron algorithm\n",
"class Perceptron:\n",
" def __init__(self, learning_rate=0.01, n_iters=1000):\n",
"class MultiClassPerceptron:\n",
" def __init__(self, input_dim, output_dim, lr=0.01, epochs=1000):\n",
" self.W = np.random.randn(input_dim, output_dim)\n",
" self.b = np.zeros((1, output_dim))\n",
" self.lr = lr\n",
" self.epochs = epochs\n",
"\n",
" def forward(self, X):\n",
" # ToDo: implement the forward() function\n",
" pass\n",
"\n",
" # define the fit function to train the model\n",
" def backward(self, X, y):\n",
" # ToDo: implement the backward() function\n",
" pass\n",
"\n",
" # define the predict function to predict labels\n",
" def fit(self, X, y):\n",
" for epoch in range(self.epochs):\n",
" self.forward(X)\n",
" self.backward(X, y)\n",
"\n",
" def _unit_step_func(self, x):\n",
" def predict(self, X):\n",
" # ToDo: implement the predict() function\n",
" pass"
],
"metadata": {
@ -105,7 +118,7 @@
"outputs": [],
"source": [
"# Train the model\n",
"p = Perceptron(learning_rate=0.01, n_iters=1000)\n",
"p = MultiClassPerceptron(input_dim=X_train.shape[1], output_dim=3, lr=0.01, epochs=1000)\n",
"p.fit(X_train, y_train)\n",
"predictions_train = p.predict(X_train)\n",
"predictions = p.predict(X_test)"