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@ -1653,9 +1653,6 @@
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"for iteration in range(n_iterations):\n",
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"for iteration in range(n_iterations):\n",
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" logits = X_train.dot(Theta)\n",
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" logits = X_train.dot(Theta)\n",
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" Y_proba = softmax(logits)\n",
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" Y_proba = softmax(logits)\n",
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" xentropy_loss = -np.mean(np.sum(Y_train_one_hot * np.log(Y_proba + epsilon), axis=1))\n",
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" l2_loss = 1/2 * np.sum(np.square(Theta[1:]))\n",
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" loss = xentropy_loss + alpha * l2_loss\n",
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" error = Y_proba - Y_train_one_hot\n",
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" error = Y_proba - Y_train_one_hot\n",
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" gradients = 1/m * X_train.T.dot(error) + np.r_[np.zeros([1, n_outputs]), alpha * Theta[1:]]\n",
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" gradients = 1/m * X_train.T.dot(error) + np.r_[np.zeros([1, n_outputs]), alpha * Theta[1:]]\n",
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" Theta = Theta - eta * gradients\n",
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" Theta = Theta - eta * gradients\n",
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