搭建神经网络_神经元.ipynb

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    "搭建基本模块-神经元"
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   "source": [
    "import numpy as np"
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   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))"
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      "0.9990889488055994\n"
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   "source": [
    "class Neuron:\n",
    "    def __init__(self,weights,bias):\n",
    "        self.weights = weights\n",
    "        self.bias = bias\n",
    "    def feedforward(self,inputs):\n",
    "        total = np.dot(self.weights,inputs) + self.bias\n",
    "        return sigmoid(total)\n",
    "    \n",
    "weights = np.array([0,1])\n",
    "bias = 4\n",
    "n = Neuron(weights,bias)\n",
    "x = np.array([2,3])\n",
    "print(n.feedforward(x))"
   ]
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   "cell_type": "markdown",
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   "source": [
    "搭建神经网络"
   ]
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    "class OurNeuralNetwork:\n",
    "    '''\n",
    "  A neural network with:\n",
    "    - 2 inputs\n",
    "    - a hidden layer with 2 neurons (h1, h2)\n",
    "    - an output layer with 1 neuron (o1)\n",
    "  Each neuron has the same weights and bias:\n",
    "    - w = [0, 1]\n",
    "    - b = 0\n",
    "  '''\n",
    "    def __init__(self):\n",
    "        weights = np.array([0,1])\n",
    "        bias = 0\n",
    "        # The Neuron class here is from the previous section\n",
    "        self.h1 = Neuron(weights,bias)\n",
    "        self.h2 = Neuron(weights,bias)\n",
    "        self.o1 = Neuron(weights,bias)\n",
    "    def feedforward(self,x):\n",
    "        out_h1 = self.h1.feedforward(x)\n",
    "        out_h2 = self.h2.feedforward(x)\n",
    "        out_o1 = self.o1.feedforward(np.array([out_h1,out_h2]))\n",
    "        return out_o1\n",
    "    \n",
    "network = OurNeuralNetwork()\n",
    "x = np.array([2,3])\n",
    "print(network.feedforward(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练神经网络"
   ]
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      "0.5\n"
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   "source": [
    "def mse_loss(y_true,y_pred):\n",
    "    return((y_true - y_pred)**2).mean()\n",
    "\n",
    "y_true = np.array([1,0,0,1])\n",
    "y_pred = np.array([0,0,0,0])\n",
    "print(mse_loss(y_true,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "减少神经网络损失"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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