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Zwleisa patch #632

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Zwleisa patch #632

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11d2c44
Update 04_training_linear_models.ipynb
zwleisa Feb 13, 2024
5fc640d
Update 04_training_linear_models.ipynb
zwleisa Feb 13, 2024
b5ff044
Update 04_training_linear_models.ipynb
zwleisa Feb 13, 2024
48db98c
Update 04_training_linear_models.ipynb
zwleisa Feb 13, 2024
d8ddbac
Update 04_training_linear_models.ipynb
zwleisa Feb 13, 2024
c676da2
Update 05_support_vector_machines.ipynb
zwleisa Feb 13, 2024
6b09318
Update 10_neural_nets_with_keras.ipynb
zwleisa Feb 15, 2024
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12 changes: 6 additions & 6 deletions 04_training_linear_models.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1381,8 +1381,8 @@
"X_val_poly_scaled = poly_scaler.transform(X_val)\n",
"\n",
"sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True,\n",
" penalty=None, learning_rate=\"constant\", eta0=0.0005, random_state=42)\n",
"\n",
" penalty=None, learning_rate=\"constant\", eta0=0.0005, random_state=42)\n",
"\n",
"minimum_val_error = float(\"inf\")\n",
"best_epoch = None\n",
"best_model = None\n",
Expand Down Expand Up @@ -1429,8 +1429,8 @@
}
],
"source": [
"sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True,\n",
" penalty=None, learning_rate=\"constant\", eta0=0.0005, random_state=42)\n",
"sgd_reg = SGDRegressor(max_iter=1, tol=None, warm_start=True,\n",
" penalty=None, learning_rate=\"constant\", eta0=0.0005, random_state=42)\n",
"\n",
"n_epochs = 500\n",
"train_errors, val_errors = [], []\n",
Expand Down Expand Up @@ -1773,7 +1773,7 @@
"outputs": [],
"source": [
"X = iris[\"data\"][:, 3:] # petal width\n",
"y = (iris[\"target\"] == 2).astype(np.int) # 1 if Iris virginica, else 0"
"y = (iris[\"target\"] == 2).astype(np.int32) # 1 if Iris virginica, else 0"
]
},
{
Expand Down Expand Up @@ -1876,7 +1876,7 @@
"source": [
"X_new = np.linspace(0, 3, 1000).reshape(-1, 1)\n",
"y_proba = log_reg.predict_proba(X_new)\n",
"decision_boundary = X_new[y_proba[:, 1] >= 0.5][0]\n",
"decision_boundary = X_new[y_proba[:, 1] >= 0.5][0,0]\n",
"\n",
"plt.figure(figsize=(8, 3))\n",
"plt.plot(X[y==0], y[y==0], \"bs\")\n",
Expand Down
2 changes: 1 addition & 1 deletion 05_support_vector_machines.ipynb
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Expand Up @@ -125,7 +125,7 @@
"y = y[setosa_or_versicolor]\n",
"\n",
"# SVM Classifier model\n",
"svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
"svm_clf = SVC(kernel=\"linear\", C=float(10e10))\n",
"svm_clf.fit(X, y)"
]
},
Expand Down
2 changes: 1 addition & 1 deletion 10_neural_nets_with_keras.ipynb
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Expand Up @@ -116,7 +116,7 @@
"\n",
"iris = load_iris()\n",
"X = iris.data[:, (2, 3)] # petal length, petal width\n",
"y = (iris.target == 0).astype(np.int)\n",
"y = (iris.target == 0).astype(np.int32)\n",
"\n",
"per_clf = Perceptron(max_iter=1000, tol=1e-3, random_state=42)\n",
"per_clf.fit(X, y)\n",
Expand Down