pytorch-stuff/Numpy_NN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = sklearn.datasets.make_moons(200,noise=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11f466f90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "input_neurons = 2\n",
    "output_neurons = 2\n",
    "samples = X.shape[0]\n",
    "learning_rate = 0.001\n",
    "lambda_reg = 0.01\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dic = {'W1': W1, 'b1': b1,'W2': W2, 'b2': b2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve(model_dict):\n",
    "    W1 = model_dic['W1']\n",
    "    b1 = model_dic['b1']\n",
    "    W2 = model_dic['W2']\n",
    "    b2 = model_dic['b2']\n",
    "    return W1,b1,W2,b2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(x, model_dict):\n",
    "    W1,b1,W2,b2 = retrieve(model_dict)\n",
    "    z1 = x.dot(W1) + b1\n",
    "    a1 = np.tanh(z1)\n",
    "    z2 = a1.dot(W2) + b2\n",
    "    a2 = np.tanh(z2)\n",
    "    exp_scores = np.exp(a2)\n",
    "    softmax = exp_scores / np.sum(exp_scores, dim=1, keepdims=True)\n",
    "    return softmax\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss(softmax, y):\n",
    "    W1,b1,W2,b2 = retrieve(model_dict)\n",
    "    m = np.zeros(200)\n",
    "    for i,correct_index in enumerate(y):\n",
    "        predicted = softmax[i][correct_index]\n",
    "        m[i] = predicted\n",
    "    log_prob = -np.log(predicted)\n",
    "    softmax_loss = np.sum(log_prob)\n",
    "    reg_loss = lambda_reg / 2*(np.sum(np.square(W1)) + np.sum(np.square(W2)))\n",
    "    loss = softmax_loss + reg_loss\n",
    "    return float(loss/y.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "softmax = np.random.randn(200,2)\n"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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