Python代码实现整个神经网络搭建.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deriv_sigmoid(x):\n",
    "    fx = sigmoid(x)\n",
    "    return fx * (1 - fx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mse_loss(y_true,y_pred):\n",
    "    return ((y_true - y_pred) ** 2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 loss:0.324\n",
      "Epoch 0 loss:0.318\n",
      "Epoch 0 loss:0.312\n",
      "Epoch 0 loss:0.312\n",
      "Epoch 10 loss:0.199\n",
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      "Epoch 10 loss:0.190\n",
      "Epoch 10 loss:0.190\n",
      "Epoch 20 loss:0.122\n",
      "Epoch 20 loss:0.120\n",
      "Epoch 20 loss:0.119\n",
      "Epoch 20 loss:0.118\n",
      "Epoch 30 loss:0.083\n",
      "Epoch 30 loss:0.082\n",
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      "Epoch 40 loss:0.061\n",
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     ]
    }
   ],
   "source": [
    "class OurNeuralNetwork:\n",
    "    '''\n",
    "    - 2 inputs\n",
    "    - a hidden layer with 2 neurons (h1, h2)\n",
    "    - an output layer with 1 neuron (o1)\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        # Weights\n",
    "        self.w1 = np.random.normal()\n",
    "        self.w2 = np.random.normal()\n",
    "        self.w3 = np.random.normal()\n",
    "        self.w4 = np.random.normal()\n",
    "        self.w5 = np.random.normal()\n",
    "        self.w6 = np.random.normal()\n",
    "        # Bias\n",
    "        self.b1 = np.random.normal()\n",
    "        self.b2 = np.random.normal()\n",
    "        self.b3 = np.random.normal()\n",
    "    def feedforward(self,x):\n",
    "        h1 = sigmoid(self.w1 * x[0] + self.w2 * x[1] + self.b1)\n",
    "        h2 = sigmoid(self.w3 * x[0] + self.w4 * x[1] + self.b2)\n",
    "        o1 = sigmoid(self.w5 * h1 + self.w6 * h2 + self.b3)\n",
    "        return o1\n",
    "    def train(self,data,all_y_trues):\n",
    "        learn_rate = 0.1\n",
    "        epochs = 1000\n",
    "        for epoch in range(epochs):\n",
    "            for x,y_true in zip(data,all_y_trues):\n",
    "                sum_h1 = self.w1 * x[0] + self.w2 * x[1] + self.b1\n",
    "                h1 = sigmoid(sum_h1)\n",
    "                sum_h2 = self.w3 * x[0] + self.w4 * x[1] + self.b2\n",
    "                h2 = sigmoid(sum_h2)\n",
    "                sum_o1 = self.w5 * h1 + self.w6 * h2 + self.b3\n",
    "                o1 = sigmoid(sum_o1)\n",
    "                y_pred = o1\n",
    "                # calculate partial derivatives\n",
    "                d_L_d_ypred = -2 * (y_true - y_pred)\n",
    "                # Neuron o1\n",
    "                d_ypred_d_w5 = h1 * deriv_sigmoid(sum_o1)\n",
    "                d_ypred_d_w6 = h2 * deriv_sigmoid(sum_o1)\n",
    "                d_ypred_d_b3 = deriv_sigmoid(sum_o1)\n",
    "                d_ypred_d_h1 = self.w5 * deriv_sigmoid(sum_o1)\n",
    "                d_ypred_d_h2 = self.w6 * deriv_sigmoid(sum_o1)\n",
    "                # Neuron h1\n",
    "                d_h1_d_w1 = x[0] * deriv_sigmoid(sum_h1)\n",
    "                d_h1_d_w2 = x[1] * deriv_sigmoid(sum_h1)\n",
    "                d_h1_d_b1 = deriv_sigmoid(sum_h1)\n",
    "                # Neuron h2\n",
    "                d_h2_d_w3 = x[0] * deriv_sigmoid(sum_h2)\n",
    "                d_h2_d_w4 = x[1] * deriv_sigmoid(sum_h2)\n",
    "                d_h2_d_b2 = deriv_sigmoid(sum_h2)\n",
    "                # Updata weights and biases\n",
    "                # Neuron h1\n",
    "                self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w1\n",
    "                self.w2 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w2\n",
    "                self.b1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_b1\n",
    "                # Neuron h2\n",
    "                self.w3 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w3\n",
    "                self.w4 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w4\n",
    "                self.b2 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_b2\n",
    "                # Neuron o1\n",
    "                self.w5 -= learn_rate * d_L_d_ypred * d_ypred_d_w5\n",
    "                self.w6 -= learn_rate * d_L_d_ypred * d_ypred_d_w6\n",
    "                self.b3 -= learn_rate * d_L_d_ypred * d_ypred_d_b3\n",
    "                # Calculate total loss at the end of each epoch\n",
    "                if epoch % 10 == 0:\n",
    "                    y_preds = np.apply_along_axis(self.feedforward,1,data)\n",
    "                    loss = mse_loss(all_y_trues,y_preds)\n",
    "                    print('Epoch %d loss:%.3f'%(epoch,loss))\n",
    "# Define dataset\n",
    "data = np.array([\n",
    "    [-2, -1], # Alice\n",
    "    [25, 6], # Bob\n",
    "    [17, 4], # Charlie\n",
    "    [-15, -6], # Diana\n",
    "])\n",
    "all_y_trues = np.array([\n",
    "    1, # Alice\n",
    "    0, # Bob\n",
    "    0, # Charlie\n",
    "    1, # Diana\n",
    "])\n",
    "# Train our neural network\n",
    "network = OurNeuralNetwork()\n",
    "network.train(data,all_y_trues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emily: 0.963\n",
      "Frank: 0.038\n"
     ]
    }
   ],
   "source": [
    "# Make some predictions\n",
    "emily = np.array([-7,-3])\n",
    "frank = np.array([20,2])\n",
    "print(\"Emily: %.3f\" % network.feedforward(emily)) \n",
    "print(\"Frank: %.3f\" % network.feedforward(frank)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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