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neural_network/numpy_nn/nn.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8f3ff90a",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import nnfs\n",
"from nnfs.datasets import spiral_data\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "439ab0e4",
"metadata": {},
"outputs": [],
"source": [
"nnfs.init()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "2bc1bb6c",
"metadata": {},
"outputs": [],
"source": [
"class Linear():\n",
" def __init__(self, in_features, out_features):\n",
" self.weights = np.random.randn(in_features, out_features) * 0.1\n",
" self.bias = np.zeros((1, out_features))\n",
" def forward(self, inputs):\n",
" self.outputs = np.dot(inputs, self.weights) + self.bias\n",
" self.inputs = inputs\n",
" def backward(self, dvalues):\n",
" #计算权重的梯度\n",
" self.dweights = np.dot(self.inputs.T, dvalues)\n",
" #计算偏置的梯度\n",
" self.dbiases = np.sum(dvalues, axis=0, keepdims=True)\n",
" #计算输入的梯度\n",
" self.dinputs = np.dot(dvalues, self.weights.T)\n",
"class Relu():\n",
" def forward(self, inputs):\n",
" self.outputs = np.maximum(0, inputs)\n",
" self.inputs = inputs\n",
" def backward(self, dvalues):\n",
" self.dinputs = dvalues.copy()\n",
" self.dinputs[self.inputs <= 0] = 0\n",
"class Softmax():\n",
" def forward(self, inputs):\n",
" exp_values = np.exp(inputs - np.max(inputs, axis=1, keepdims=True))\n",
" self.outputs = exp_values / np.sum(exp_values, axis=1, keepdims=True)\n",
" def backward(self, dvalues):\n",
" self.dinputs = np.empty_like(dvalues)\n",
" for index, (single_output, single_dvalues) in enumerate(zip(self.outputs, dvalues)):\n",
" single_output = single_output.reshape(-1, 1)\n",
" jacobian_matrix = np.diagflat(single_output) - np.dot(single_output, single_output.T)\n",
" self.dinputs[index] = np.dot(jacobian_matrix, single_dvalues)\n",
"class Loss():\n",
" def calculate(self, outputs, y):\n",
" sample_loss = self.forward(outputs, y)\n",
" data_loss = np.mean(sample_loss)\n",
" return data_loss\n",
"\"\"\"交叉熵损失函数\"\"\"\n",
"class CategoricalCrossEntropy(Loss):\n",
" def one_hot(self, y_true):\n",
" y_true = np.eye(max(y_true) + 1)[y_true]\n",
" return y_true\n",
" def forward(self, y_pred, y_true):\n",
" y_pred_clipped = np.clip(y_pred, 1e-7, 1-1e-7)#截断，放置np.log溢出\n",
" if len(y_true.shape) == 1:#标签不是独热码，通过one_hot函数转化\n",
" y_true = self.one_hot(y_true)\n",
" confidences = np.sum(y_pred_clipped * y_true, axis=1)\n",
" negative_log_likehoods = -np.log(confidences)\n",
" return negative_log_likehoods\n",
" def backward(self, dvalues, y_true):\n",
" samples = len(dvalues)\n",
" if len(y_true.shape) == 1:#将标签转化为onehot\n",
" y_true = self.one_hot(y_true)\n",
" self.dinputs = -y_true / dvalues\n",
" self.dinputs = self.dinputs /samples\n",
"\"\"\"准确率\"\"\"\n",
"class Accuracy():\n",
" def forward(self, logits, y_true):\n",
" predictions = np.argmax(logits, axis=1)#从激活输出选出最大值对应的下标即使标签\n",
" if len(y_true.shape) == 2:#独热编码\n",
" y_true = np.max(y_true, axis=1)\n",
" accuracy = np.mean(predictions == y_true)\n",
" return accuracy"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "5f334df4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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" [ 0.00112573 0.00109519 -0.00222092]\n",
" [ 0.00112447 0.00109812 -0.00222259]\n",
" [ 0.0011262 0.00109446 -0.00222066]\n",
" [ 0.00112568 0.00109231 -0.00221799]\n",
" [ 0.00112681 0.00109287 -0.00221968]\n",
" [ 0.001127 0.00109251 -0.00221951]\n",
" [ 0.00112619 0.00109166 -0.00221785]\n",
" [ 0.0011269 0.00109405 -0.00222094]\n",
" [ 0.00112576 0.00109672 -0.00222247]\n",
" [ 0.00112763 0.0010924 -0.00222003]\n",
" [ 0.00112169 0.0010934 -0.00221509]\n",
" [ 0.00112547 0.00109102 -0.00221649]\n",
" [ 0.00112268 0.00109248 -0.00221516]\n",
" [ 0.00112286 0.00109225 -0.00221511]\n",
" [ 0.00112159 0.00109304 -0.00221464]\n",
" [ 0.00111771 0.00109615 -0.00221386]\n",
" [ 0.00112716 0.00108963 -0.00221678]]\n"
]
}
],
"source": [
"if \"__main__\" == __name__:\n",
" #数据（3，4）（batch_size, in_features）\n",
" X, y = spiral_data(100, 3)#每个类别100个数据，3个类别就是300\n",
" layer1 = Linear(2, 5)\n",
" layer2 = Linear(5, 3)\n",
" layer1.forward(X)\n",
" act1 = Relu()\n",
" act1.forward(layer1.outputs)\n",
" layer2.forward(act1.outputs)\n",
" act2 = Softmax()\n",
" act2.forward(layer2.outputs)\n",
" loss = CategoricalCrossEntropy()\n",
" dvalues = loss.forward(act2.outputs, y)\n",
" loss.backward(act2.outputs, y)\n",
" print(loss.dinputs.shape)\n",
" act2.backward(loss.dinputs)\n",
" print(act2.dinputs)\n",
"# cost = loss.calculate(act2.outputs, y)\n",
"# print(cost)\n",
"# acc = Accuracy()\n",
"# accuracy = acc.forward(act2.outputs, y)\n",
"# print(accuracy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c588b453",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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