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temporary comparison via review nb #2

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62 changes: 41 additions & 21 deletions L03-perceptron/code/perceptron-numpy.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -23,15 +23,22 @@
"Sebastian Raschka \n",
"\n",
"CPython 3.7.1\n",
"IPython 7.10.1\n",
"IPython 7.11.1\n",
"\n",
"numpy 1.17.4\n"
"torch 1.4.0\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -v -p numpy"
"%watermark -a 'Sebastian Raschka' -v -p torch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Runs on CPU or GPU (if available)"
]
},
{
Expand All @@ -45,7 +52,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Implementation of the classic Perceptron by Frank Rosenblatt for binary classification (here: 0/1 class labels) in NumPy"
"Implementation of the classic Perceptron by Frank Rosenblatt for binary classification (here: 0/1 class labels) in PyTorch"
]
},
{
Expand All @@ -63,6 +70,7 @@
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import torch\n",
"%matplotlib inline"
]
},
Expand Down Expand Up @@ -189,15 +197,24 @@
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"\n",
"class Perceptron():\n",
" def __init__(self, num_features):\n",
" self.num_features = num_features\n",
" self.weights = np.zeros((num_features, 1), dtype=np.float)\n",
" self.bias = np.zeros(1, dtype=np.float)\n",
" self.weights = torch.zeros(num_features, 1, \n",
" dtype=torch.float32, device=device)\n",
" self.bias = torch.zeros(1, dtype=torch.float32, device=device)\n",
" \n",
" # placeholder vectors so they don't\n",
" # need to be recreated each time\n",
" self.ones = torch.ones(1)\n",
" self.zeros = torch.zeros(1)\n",
"\n",
" def forward(self, x):\n",
" linear = np.dot(x, self.weights) + self.bias\n",
" predictions = np.where(linear > 0., 1, 0)\n",
" linear = torch.add(torch.mm(x, self.weights), self.bias)\n",
" predictions = torch.where(linear > 0., self.ones, self.zeros)\n",
" return predictions\n",
" \n",
" def backward(self, x, y): \n",
Expand All @@ -209,13 +226,14 @@
" for e in range(epochs):\n",
" \n",
" for i in range(y.shape[0]):\n",
" # use view because backward expects a matrix (i.e., 2D tensor)\n",
" errors = self.backward(x[i].reshape(1, self.num_features), y[i]).reshape(-1)\n",
" self.weights += (errors * x[i]).reshape(self.num_features, 1)\n",
" self.bias += errors\n",
" \n",
" def evaluate(self, x, y):\n",
" predictions = self.forward(x).reshape(-1)\n",
" accuracy = np.sum(predictions == y) / y.shape[0]\n",
" accuracy = torch.sum(predictions == y).float() / y.shape[0]\n",
" return accuracy"
]
},
Expand All @@ -236,24 +254,23 @@
"output_type": "stream",
"text": [
"Model parameters:\n",
"\n",
"\n",
" Weights: [[1.27340847]\n",
" [1.34642288]]\n",
"\n",
" Bias: [-1.]\n",
"\n"
" Weights: tensor([[1.2734],\n",
" [1.3464]])\n",
" Bias: tensor([-1.])\n"
]
}
],
"source": [
"ppn = Perceptron(num_features=2)\n",
"\n",
"ppn.train(X_train, y_train, epochs=5)\n",
"X_train_tensor = torch.tensor(X_train, dtype=torch.float32, device=device)\n",
"y_train_tensor = torch.tensor(y_train, dtype=torch.float32, device=device)\n",
"\n",
"ppn.train(X_train_tensor, y_train_tensor, epochs=5)\n",
"\n",
"print('Model parameters:\\n\\n')\n",
"print(' Weights: %s\\n' % ppn.weights)\n",
"print(' Bias: %s\\n' % ppn.bias)"
"print('Model parameters:')\n",
"print(' Weights: %s' % ppn.weights)\n",
"print(' Bias: %s' % ppn.bias)"
]
},
{
Expand All @@ -277,7 +294,10 @@
}
],
"source": [
"test_acc = ppn.evaluate(X_test, y_test)\n",
"X_test_tensor = torch.tensor(X_test, dtype=torch.float32, device=device)\n",
"y_test_tensor = torch.tensor(y_test, dtype=torch.float32, device=device)\n",
"\n",
"test_acc = ppn.evaluate(X_test_tensor, y_test_tensor)\n",
"print('Test set accuracy: %.2f%%' % (test_acc*100))"
]
},
Expand Down
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