assignment2/FullyConnectedNets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_sPFIx6TGT27"
   },
   "source": [
    "# Multi-Layer Fully Connected Network\n",
    "In this exercise, you will implement a fully connected network with an arbitrary number of hidden layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QMYSxTlwGT27"
   },
   "source": [
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the network initialization, forward pass, and backward pass. Throughout this assignment, you will be implementing layers in `cs231n/layers.py`. You can re-use your implementations for `affine_forward`, `affine_backward`, `relu_forward`, `relu_backward`, and `softmax_loss` from Assignment 1. For right now, don't worry about implementing dropout or batch/layer normalization yet, as you will add those features later.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4211,
     "status": "ok",
     "timestamp": 1627609452723,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "AbmwStrZGT28",
    "outputId": "4d8fee95-e015-4e41-9589-881abc3c3d3f",
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n",
      "\tYou will need to compile a Cython extension for a portion of this assignment.\n",
      "\tThe instructions to do this will be given in a section of the notebook below.\n"
     ]
    }
   ],
   "source": [
    "# Setup cell.\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 8.0)  # Set default size of plots.\n",
    "plt.rcParams[\"image.interpolation\"] = \"nearest\"\n",
    "plt.rcParams[\"image.cmap\"] = \"gray\"\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\"Returns relative error.\"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13680,
     "status": "ok",
     "timestamp": 1627609468237,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "GxAHlgyAGT28",
    "outputId": "9be7b62c-5e22-4ab4-bafa-a54bd28489eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (49000, 3, 32, 32)\n",
      "y_train: (49000,)\n",
      "X_val: (1000, 3, 32, 32)\n",
      "y_val: (1000,)\n",
      "X_test: (1000, 3, 32, 32)\n",
      "y_test: (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR-10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "    print(f\"{k}: {v.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a3andZtcGT28"
   },
   "source": [
    "## Initial Loss and Gradient Check\n",
    "\n",
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. This is a good way to see if the initial losses seem reasonable.\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-7 or less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0UcVCObIUkz9"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1513,
     "status": "ok",
     "timestamp": 1627609522669,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "DY3GOpBOGT29",
    "outputId": "dce259fe-70bf-4948-bf77-7c0b22928ba0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.300479089768492\n",
      "W1 relative error: 1.0252674471656573e-07\n",
      "W2 relative error: 2.2120479295080622e-05\n",
      "W3 relative error: 4.5623278736665505e-07\n",
      "b1 relative error: 4.6600944653202505e-09\n",
      "b2 relative error: 2.085654276112763e-09\n",
      "b3 relative error: 1.689724888469736e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 1.409028728052923e-08\n",
      "W2 relative error: 6.86942277940646e-08\n",
      "W3 relative error: 2.131129859578198e-08\n",
      "b1 relative error: 1.4752427965311745e-08\n",
      "b2 relative error: 1.7223751746766738e-09\n",
      "b3 relative error: 2.378772438198909e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "    print(\"Running check with reg = \", reg)\n",
    "    model = FullyConnectedNet(\n",
    "        [H1, H2],\n",
    "        input_dim=D,\n",
    "        num_classes=C,\n",
    "        reg=reg,\n",
    "        weight_scale=5e-2,\n",
    "        dtype=np.float64\n",
    "    )\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print(\"Initial loss: \", loss)\n",
    "\n",
    "    # Most of the errors should be on the order of e-7 or smaller.   \n",
    "    # NOTE: It is fine however to see an error for W2 on the order of e-5\n",
    "    # for the check when reg = 0.0\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print(f\"{name} relative error: {rel_error(grad_num, grads[name])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VyhwVT2UGT29"
   },
   "source": [
    "As another sanity check, make sure your network can overfit on a small dataset of 50 images. First, we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 963
    },
    "executionInfo": {
     "elapsed": 1658,
     "status": "ok",
     "timestamp": 1627609528087,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "2rnw16yQGT29",
    "outputId": "6d35dd7b-0caa-4ea8-a6ea-3440383f7945",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.363364\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.108000\n",
      "(Epoch 1 / 20) train acc: 0.320000; val_acc: 0.127000\n",
      "(Epoch 2 / 20) train acc: 0.440000; val_acc: 0.172000\n",
      "(Epoch 3 / 20) train acc: 0.500000; val_acc: 0.184000\n",
      "(Epoch 4 / 20) train acc: 0.540000; val_acc: 0.181000\n",
      "(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.190000\n",
      "(Iteration 11 / 40) loss: 0.839976\n",
      "(Epoch 6 / 20) train acc: 0.740000; val_acc: 0.187000\n",
      "(Epoch 7 / 20) train acc: 0.740000; val_acc: 0.183000\n",
      "(Epoch 8 / 20) train acc: 0.820000; val_acc: 0.177000\n",
      "(Epoch 9 / 20) train acc: 0.860000; val_acc: 0.200000\n",
      "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.191000\n",
      "(Iteration 21 / 40) loss: 0.337174\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.189000\n",
      "(Epoch 12 / 20) train acc: 0.940000; val_acc: 0.180000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.199000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.199000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.195000\n",
      "(Iteration 31 / 40) loss: 0.075911\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.182000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.201000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.207000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.185000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.192000\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  \"X_train\": data[\"X_train\"][:num_train],\n",
    "  \"y_train\": data[\"y_train\"][:num_train],\n",
    "  \"X_val\": data[\"X_val\"],\n",
    "  \"y_val\": data[\"y_val\"],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2   # Experiment with this!\n",
    "learning_rate = 1e-2 # Experiment with this!\n",
    "model = FullyConnectedNet(\n",
    "    [100, 100],\n",
    "    weight_scale=weight_scale,\n",
    "    dtype=np.float64\n",
    ")\n",
    "solver = Solver(\n",
    "    model,\n",
    "    small_data,\n",
    "    print_every=10,\n",
    "    num_epochs=20,\n",
    "    batch_size=25,\n",
    "    update_rule=\"sgd\",\n",
    "    optim_config={\"learning_rate\": learning_rate},\n",
    ")\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history)\n",
    "plt.title(\"Training loss history\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Training loss\")\n",
    "plt.grid(linestyle='--', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5kyoRwOeGT2-"
   },
   "source": [
    "Now, try to use a five-layer network with 100 units on each layer to overfit on 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 963
    },
    "executionInfo": {
     "elapsed": 2493,
     "status": "ok",
     "timestamp": 1627200100920,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "Rzb8SooxGT2-",
    "outputId": "ba13ce5c-0a2e-4a39-fa03-237ec6209d46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.350507\n",
      "(Epoch 0 / 20) train acc: 0.140000; val_acc: 0.111000\n",
      "(Epoch 1 / 20) train acc: 0.200000; val_acc: 0.112000\n",
      "(Epoch 2 / 20) train acc: 0.360000; val_acc: 0.107000\n",
      "(Epoch 3 / 20) train acc: 0.480000; val_acc: 0.141000\n",
      "(Epoch 4 / 20) train acc: 0.740000; val_acc: 0.147000\n",
      "(Epoch 5 / 20) train acc: 0.660000; val_acc: 0.120000\n",
      "(Iteration 11 / 40) loss: 1.591276\n",
      "(Epoch 6 / 20) train acc: 0.520000; val_acc: 0.148000\n",
      "(Epoch 7 / 20) train acc: 0.660000; val_acc: 0.145000\n",
      "(Epoch 8 / 20) train acc: 0.900000; val_acc: 0.154000\n",
      "(Epoch 9 / 20) train acc: 0.940000; val_acc: 0.174000\n",
      "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.169000\n",
      "(Iteration 21 / 40) loss: 0.265300\n",
      "(Epoch 11 / 20) train acc: 0.940000; val_acc: 0.184000\n",
      "(Epoch 12 / 20) train acc: 0.940000; val_acc: 0.166000\n",
      "(Epoch 13 / 20) train acc: 0.820000; val_acc: 0.186000\n",
      "(Epoch 14 / 20) train acc: 0.940000; val_acc: 0.180000\n",
      "(Epoch 15 / 20) train acc: 0.980000; val_acc: 0.173000\n",
      "(Iteration 31 / 40) loss: 0.085784\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.188000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.197000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.188000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.179000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.189000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 3e-2  # Experiment with this!\n",
    "weight_scale = 4e-2  # Experiment with this!\n",
    "model = FullyConnectedNet(\n",
    "    [100, 100, 100, 100],\n",
    "    weight_scale=weight_scale,\n",
    "    dtype=np.float64\n",
    ")\n",
    "solver = Solver(\n",
    "    model,\n",
    "    small_data,\n",
    "    print_every=10,\n",
    "    num_epochs=20,\n",
    "    batch_size=25,\n",
    "    update_rule='sgd',\n",
    "    optim_config={'learning_rate': learning_rate},\n",
    ")\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history)\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.grid(linestyle='--', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Uy5UyUEGT2_"
   },
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pneWQWkAGT2_"
   },
   "source": [
    "## SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 269,
     "status": "ok",
     "timestamp": 1627204729786,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "hGCELaTSGT2_",
    "outputId": "76a2bb5e-4bfe-4073-b907-47192f50ac63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {\"learning_rate\": 1e-3, \"velocity\": v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "# Should see relative errors around e-8 or less\n",
    "print(\"next_w error: \", rel_error(next_w, expected_next_w))\n",
    "print(\"velocity error: \", rel_error(expected_velocity, config[\"velocity\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rXR3BImlGT2_"
   },
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 8332,
     "status": "ok",
     "timestamp": 1627204741353,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "k3S1g03FGT2_",
    "outputId": "4b5f9e80-1e5b-44f5-9965-90159bd60476",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.553222\n",
      "(Epoch 0 / 5) train acc: 0.106000; val_acc: 0.091000\n",
      "(Iteration 11 / 200) loss: 2.229920\n",
      "(Iteration 21 / 200) loss: 2.286426\n",
      "(Iteration 31 / 200) loss: 2.228417\n",
      "(Epoch 1 / 5) train acc: 0.231000; val_acc: 0.208000\n",
      "(Iteration 41 / 200) loss: 2.153593\n",
      "(Iteration 51 / 200) loss: 1.986201\n",
      "(Iteration 61 / 200) loss: 1.955818\n",
      "(Iteration 71 / 200) loss: 1.908696\n",
      "(Epoch 2 / 5) train acc: 0.286000; val_acc: 0.253000\n",
      "(Iteration 81 / 200) loss: 1.980771\n",
      "(Iteration 91 / 200) loss: 1.970192\n",
      "(Iteration 101 / 200) loss: 1.932167\n",
      "(Iteration 111 / 200) loss: 1.804406\n",
      "(Epoch 3 / 5) train acc: 0.339000; val_acc: 0.286000\n",
      "(Iteration 121 / 200) loss: 1.845671\n",
      "(Iteration 131 / 200) loss: 1.972484\n",
      "(Iteration 141 / 200) loss: 1.882012\n",
      "(Iteration 151 / 200) loss: 1.817223\n",
      "(Epoch 4 / 5) train acc: 0.377000; val_acc: 0.322000\n",
      "(Iteration 161 / 200) loss: 1.714783\n",
      "(Iteration 171 / 200) loss: 1.656889\n",
      "(Iteration 181 / 200) loss: 1.675976\n",
      "(Iteration 191 / 200) loss: 1.858087\n",
      "(Epoch 5 / 5) train acc: 0.389000; val_acc: 0.303000\n",
      "Running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 3.021356\n",
      "(Epoch 0 / 5) train acc: 0.104000; val_acc: 0.109000\n",
      "(Iteration 11 / 200) loss: 2.360217\n",
      "(Iteration 21 / 200) loss: 2.127531\n",
      "(Iteration 31 / 200) loss: 1.988621\n",
      "(Epoch 1 / 5) train acc: 0.295000; val_acc: 0.258000\n",
      "(Iteration 41 / 200) loss: 1.926171\n",
      "(Iteration 51 / 200) loss: 1.919440\n",
      "(Iteration 61 / 200) loss: 1.886920\n",
      "(Iteration 71 / 200) loss: 1.876071\n",
      "(Epoch 2 / 5) train acc: 0.387000; val_acc: 0.328000\n",
      "(Iteration 81 / 200) loss: 1.743634\n",
      "(Iteration 91 / 200) loss: 1.844986\n",
      "(Iteration 101 / 200) loss: 1.701560\n",
      "(Iteration 111 / 200) loss: 1.593343\n",
      "(Epoch 3 / 5) train acc: 0.443000; val_acc: 0.336000\n",
      "(Iteration 121 / 200) loss: 1.701433\n",
      "(Iteration 131 / 200) loss: 1.554155\n",
      "(Iteration 141 / 200) loss: 1.426728\n",
      "(Iteration 151 / 200) loss: 1.593596\n",
      "(Epoch 4 / 5) train acc: 0.510000; val_acc: 0.361000\n",
      "(Iteration 161 / 200) loss: 1.494253\n",
      "(Iteration 171 / 200) loss: 1.406433\n",
      "(Iteration 181 / 200) loss: 1.167842\n",
      "(Iteration 191 / 200) loss: 1.572843\n",
      "(Epoch 5 / 5) train acc: 0.534000; val_acc: 0.326000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "    print('Running with ', update_rule)\n",
    "    model = FullyConnectedNet(\n",
    "        [100, 100, 100, 100, 100],\n",
    "        weight_scale=5e-2\n",
    "    )\n",
    "\n",
    "    solver = Solver(\n",
    "        model,\n",
    "        small_data,\n",
    "        num_epochs=5,\n",
    "        batch_size=100,\n",
    "        update_rule=update_rule,\n",
    "        optim_config={'learning_rate': 5e-3},\n",
    "        verbose=True,\n",
    "    )\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "\n",
    "fig, axes = plt.subplots(3, 1, figsize=(15, 15))\n",
    "\n",
    "axes[0].set_title('Training loss')\n",
    "axes[0].set_xlabel('Iteration')\n",
    "axes[1].set_title('Training accuracy')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[2].set_title('Validation accuracy')\n",
    "axes[2].set_xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "    axes[0].plot(solver.loss_history, label=f\"loss_{update_rule}\")\n",
    "    axes[1].plot(solver.train_acc_history, label=f\"train_acc_{update_rule}\")\n",
    "    axes[2].plot(solver.val_acc_history, label=f\"val_acc_{update_rule}\")\n",
    "    \n",
    "for ax in axes:\n",
    "    ax.legend(loc=\"best\", ncol=4)\n",
    "    ax.grid(linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZOv9k1WzGT3A"
   },
   "source": [
    "## RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 285,
     "status": "ok",
     "timestamp": 1627204752035,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "cPGl9uJuGT3A",
    "outputId": "5cab2f53-4bd7-47c3-9e5a-dba8aa110a44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.787544894227884e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 272,
     "status": "ok",
     "timestamp": 1627204920763,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "ubC4KDOqGT3A",
    "outputId": "82fb015d-460a-419e-acd5-ce16493a6c53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.1395691798535431e-07\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ixzVjQiGT3A"
   },
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 10778,
     "status": "ok",
     "timestamp": 1627204951646,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "odOZhJEmGT3B",
    "outputId": "3b3510ec-98c7-4859-a671-d128b97a8771",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running with  adam\n",
      "(Iteration 1 / 200) loss: 2.648561\n",
      "(Epoch 0 / 5) train acc: 0.127000; val_acc: 0.132000\n",
      "(Iteration 11 / 200) loss: 2.058556\n",
      "(Iteration 21 / 200) loss: 1.969781\n",
      "(Iteration 31 / 200) loss: 1.859292\n",
      "(Epoch 1 / 5) train acc: 0.365000; val_acc: 0.325000\n",
      "(Iteration 41 / 200) loss: 1.729914\n",
      "(Iteration 51 / 200) loss: 1.730389\n",
      "(Iteration 61 / 200) loss: 1.773182\n",
      "(Iteration 71 / 200) loss: 1.803022\n",
      "(Epoch 2 / 5) train acc: 0.443000; val_acc: 0.324000\n",
      "(Iteration 81 / 200) loss: 1.511906\n",
      "(Iteration 91 / 200) loss: 1.556245\n",
      "(Iteration 101 / 200) loss: 1.499097\n",
      "(Iteration 111 / 200) loss: 1.508870\n",
      "(Epoch 3 / 5) train acc: 0.521000; val_acc: 0.356000\n",
      "(Iteration 121 / 200) loss: 1.382216\n",
      "(Iteration 131 / 200) loss: 1.352152\n",
      "(Iteration 141 / 200) loss: 1.262126\n",
      "(Iteration 151 / 200) loss: 1.422968\n",
      "(Epoch 4 / 5) train acc: 0.562000; val_acc: 0.363000\n",
      "(Iteration 161 / 200) loss: 1.248351\n",
      "(Iteration 171 / 200) loss: 1.350016\n",
      "(Iteration 181 / 200) loss: 1.284800\n",
      "(Iteration 191 / 200) loss: 1.228044\n",
      "(Epoch 5 / 5) train acc: 0.583000; val_acc: 0.346000\n",
      "\n",
      "Running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.599028\n",
      "(Epoch 0 / 5) train acc: 0.125000; val_acc: 0.102000\n",
      "(Iteration 11 / 200) loss: 1.982261\n",
      "(Iteration 21 / 200) loss: 1.988950\n",
      "(Iteration 31 / 200) loss: 1.845442\n",
      "(Epoch 1 / 5) train acc: 0.357000; val_acc: 0.291000\n",
      "(Iteration 41 / 200) loss: 1.808868\n",
      "(Iteration 51 / 200) loss: 1.693051\n",
      "(Iteration 61 / 200) loss: 1.722077\n",
      "(Iteration 71 / 200) loss: 1.552008\n",
      "(Epoch 2 / 5) train acc: 0.417000; val_acc: 0.331000\n",
      "(Iteration 81 / 200) loss: 1.576861\n",
      "(Iteration 91 / 200) loss: 1.572834\n",
      "(Iteration 101 / 200) loss: 1.631255\n",
      "(Iteration 111 / 200) loss: 1.486992\n",
      "(Epoch 3 / 5) train acc: 0.458000; val_acc: 0.350000\n",
      "(Iteration 121 / 200) loss: 1.439628\n",
      "(Iteration 131 / 200) loss: 1.439926\n",
      "(Iteration 141 / 200) loss: 1.571663\n",
      "(Iteration 151 / 200) loss: 1.660343\n",
      "(Epoch 4 / 5) train acc: 0.497000; val_acc: 0.348000\n",
      "(Iteration 161 / 200) loss: 1.519797\n",
      "(Iteration 171 / 200) loss: 1.391459\n",
      "(Iteration 181 / 200) loss: 1.454956\n",
      "(Iteration 191 / 200) loss: 1.448562\n",
      "(Epoch 5 / 5) train acc: 0.564000; val_acc: 0.372000\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "    print('Running with ', update_rule)\n",
    "    model = FullyConnectedNet(\n",
    "        [100, 100, 100, 100, 100],\n",
    "        weight_scale=5e-2\n",
    "    )\n",
    "    solver = Solver(\n",
    "        model,\n",
    "        small_data,\n",
    "        num_epochs=5,\n",
    "        batch_size=100,\n",
    "        update_rule=update_rule,\n",
    "        optim_config={'learning_rate': learning_rates[update_rule]},\n",
    "        verbose=True\n",
    "    )\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print()\n",
    "    \n",
    "fig, axes = plt.subplots(3, 1, figsize=(15, 15))\n",
    "\n",
    "axes[0].set_title('Training loss')\n",
    "axes[0].set_xlabel('Iteration')\n",
    "axes[1].set_title('Training accuracy')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[2].set_title('Validation accuracy')\n",
    "axes[2].set_xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "    axes[0].plot(solver.loss_history, label=f\"{update_rule}\")\n",
    "    axes[1].plot(solver.train_acc_history, label=f\"{update_rule}\")\n",
    "    axes[2].plot(solver.val_acc_history, label=f\"{update_rule}\")\n",
    "    \n",
    "for ax in axes:\n",
    "    ax.legend(loc='best', ncol=4)\n",
    "    ax.grid(linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cYoVQ0sNGT3B"
   },
   "source": [
    "# Train a Good Model!\n",
    "Train the best fully connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully connected network.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional networks rather than fully connected networks.\n",
    "\n",
    "**Note:** You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 179401,
     "status": "ok",
     "timestamp": 1627613903209,
     "user": {
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    "id": "s862L4aLGT3B",
    "outputId": "86fa12f6-75c3-494d-ffa3-f3b5aa88e641",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.49\n",
      "Validation set accuracy:  0.487\n",
      "Validation set accuracy:  0.51\n",
      "Validation set accuracy:  0.495\n",
      "(Iteration 1 / 1470) loss: 1.449529\n",
      "(Epoch 0 / 3) train acc: 0.529000; val_acc: 0.514000\n",
      "(Iteration 11 / 1470) loss: 1.623766\n",
      "(Iteration 21 / 1470) loss: 1.506464\n",
      "(Iteration 31 / 1470) loss: 1.570191\n",
      "(Iteration 41 / 1470) loss: 1.482663\n",
      "(Iteration 51 / 1470) loss: 1.488586\n",
      "(Iteration 61 / 1470) loss: 1.639955\n",
      "(Iteration 71 / 1470) loss: 1.624726\n",
      "(Iteration 81 / 1470) loss: 1.611414\n",
      "(Iteration 91 / 1470) loss: 1.457830\n",
      "(Iteration 101 / 1470) loss: 1.553596\n",
      "(Iteration 111 / 1470) loss: 1.634922\n",
      "(Iteration 121 / 1470) loss: 1.457063\n",
      "(Iteration 131 / 1470) loss: 1.611879\n",
      "(Iteration 141 / 1470) loss: 1.465759\n",
      "(Iteration 151 / 1470) loss: 1.629953\n",
      "(Iteration 161 / 1470) loss: 1.508283\n",
      "(Iteration 171 / 1470) loss: 1.545049\n",
      "(Iteration 181 / 1470) loss: 1.531931\n",
      "(Iteration 191 / 1470) loss: 1.525315\n",
      "(Iteration 201 / 1470) loss: 1.486471\n",
      "(Iteration 211 / 1470) loss: 1.607882\n",
      "(Iteration 221 / 1470) loss: 1.652025\n",
      "(Iteration 231 / 1470) loss: 1.443156\n",
      "(Iteration 241 / 1470) loss: 1.334473\n",
      "(Iteration 251 / 1470) loss: 1.430323\n",
      "(Iteration 261 / 1470) loss: 1.534256\n",
      "(Iteration 271 / 1470) loss: 1.426028\n",
      "(Iteration 281 / 1470) loss: 1.496787\n",
      "(Iteration 291 / 1470) loss: 1.300758\n",
      "(Iteration 301 / 1470) loss: 1.532366\n",
      "(Iteration 311 / 1470) loss: 1.602859\n",
      "(Iteration 321 / 1470) loss: 1.427328\n",
      "(Iteration 331 / 1470) loss: 1.503745\n",
      "(Iteration 341 / 1470) loss: 1.615326\n",
      "(Iteration 351 / 1470) loss: 1.573409\n",
      "(Iteration 361 / 1470) loss: 1.389071\n",
      "(Iteration 371 / 1470) loss: 1.416282\n",
      "(Iteration 381 / 1470) loss: 1.537610\n",
      "(Iteration 391 / 1470) loss: 1.410797\n",
      "(Iteration 401 / 1470) loss: 1.548436\n",
      "(Iteration 411 / 1470) loss: 1.389764\n",
      "(Iteration 421 / 1470) loss: 1.421140\n",
      "(Iteration 431 / 1470) loss: 1.585632\n",
      "(Iteration 441 / 1470) loss: 1.487499\n",
      "(Iteration 451 / 1470) loss: 1.531669\n",
      "(Iteration 461 / 1470) loss: 1.510680\n",
      "(Iteration 471 / 1470) loss: 1.443866\n",
      "(Iteration 481 / 1470) loss: 1.330165\n",
      "(Epoch 1 / 3) train acc: 0.511000; val_acc: 0.519000\n",
      "(Iteration 491 / 1470) loss: 1.423481\n",
      "(Iteration 501 / 1470) loss: 1.489566\n",
      "(Iteration 511 / 1470) loss: 1.511862\n",
      "(Iteration 521 / 1470) loss: 1.408949\n",
      "(Iteration 531 / 1470) loss: 1.566773\n",
      "(Iteration 541 / 1470) loss: 1.316144\n",
      "(Iteration 551 / 1470) loss: 1.654106\n",
      "(Iteration 561 / 1470) loss: 1.462992\n",
      "(Iteration 571 / 1470) loss: 1.362649\n",
      "(Iteration 581 / 1470) loss: 1.520083\n",
      "(Iteration 591 / 1470) loss: 1.562107\n",
      "(Iteration 601 / 1470) loss: 1.407504\n",
      "(Iteration 611 / 1470) loss: 1.459124\n",
      "(Iteration 621 / 1470) loss: 1.309391\n",
      "(Iteration 631 / 1470) loss: 1.397995\n",
      "(Iteration 641 / 1470) loss: 1.371215\n",
      "(Iteration 651 / 1470) loss: 1.575661\n",
      "(Iteration 661 / 1470) loss: 1.325231\n",
      "(Iteration 671 / 1470) loss: 1.510273\n",
      "(Iteration 681 / 1470) loss: 1.571347\n",
      "(Iteration 691 / 1470) loss: 1.267819\n",
      "(Iteration 701 / 1470) loss: 1.381465\n",
      "(Iteration 711 / 1470) loss: 1.394533\n",
      "(Iteration 721 / 1470) loss: 1.451230\n",
      "(Iteration 731 / 1470) loss: 1.462039\n",
      "(Iteration 741 / 1470) loss: 1.613033\n",
      "(Iteration 751 / 1470) loss: 1.681698\n",
      "(Iteration 761 / 1470) loss: 1.527175\n",
      "(Iteration 771 / 1470) loss: 1.481695\n",
      "(Iteration 781 / 1470) loss: 1.316238\n",
      "(Iteration 791 / 1470) loss: 1.405796\n",
      "(Iteration 801 / 1470) loss: 1.460332\n",
      "(Iteration 811 / 1470) loss: 1.585635\n",
      "(Iteration 821 / 1470) loss: 1.418249\n",
      "(Iteration 831 / 1470) loss: 1.536388\n",
      "(Iteration 841 / 1470) loss: 1.303458\n",
      "(Iteration 851 / 1470) loss: 1.286052\n",
      "(Iteration 861 / 1470) loss: 1.523676\n",
      "(Iteration 871 / 1470) loss: 1.477281\n",
      "(Iteration 881 / 1470) loss: 1.662299\n",
      "(Iteration 891 / 1470) loss: 1.589967\n",
      "(Iteration 901 / 1470) loss: 1.475026\n",
      "(Iteration 911 / 1470) loss: 1.415275\n",
      "(Iteration 921 / 1470) loss: 1.545217\n",
      "(Iteration 931 / 1470) loss: 1.443898\n",
      "(Iteration 941 / 1470) loss: 1.482123\n",
      "(Iteration 951 / 1470) loss: 1.633110\n",
      "(Iteration 961 / 1470) loss: 1.473440\n",
      "(Iteration 971 / 1470) loss: 1.476215\n",
      "(Epoch 2 / 3) train acc: 0.520000; val_acc: 0.507000\n",
      "(Iteration 981 / 1470) loss: 1.479622\n",
      "(Iteration 991 / 1470) loss: 1.568592\n",
      "(Iteration 1001 / 1470) loss: 1.522467\n",
      "(Iteration 1011 / 1470) loss: 1.423834\n",
      "(Iteration 1021 / 1470) loss: 1.465440\n",
      "(Iteration 1031 / 1470) loss: 1.461375\n",
      "(Iteration 1041 / 1470) loss: 1.454706\n",
      "(Iteration 1051 / 1470) loss: 1.533660\n",
      "(Iteration 1061 / 1470) loss: 1.587876\n",
      "(Iteration 1071 / 1470) loss: 1.507188\n",
      "(Iteration 1081 / 1470) loss: 1.486024\n",
      "(Iteration 1091 / 1470) loss: 1.484346\n",
      "(Iteration 1101 / 1470) loss: 1.625359\n",
      "(Iteration 1111 / 1470) loss: 1.602308\n",
      "(Iteration 1121 / 1470) loss: 1.384149\n",
      "(Iteration 1131 / 1470) loss: 1.442236\n",
      "(Iteration 1141 / 1470) loss: 1.763778\n",
      "(Iteration 1151 / 1470) loss: 1.648009\n",
      "(Iteration 1161 / 1470) loss: 1.568362\n",
      "(Iteration 1171 / 1470) loss: 1.344314\n",
      "(Iteration 1181 / 1470) loss: 1.463655\n",
      "(Iteration 1191 / 1470) loss: 1.600070\n",
      "(Iteration 1201 / 1470) loss: 1.277996\n",
      "(Iteration 1211 / 1470) loss: 1.509389\n",
      "(Iteration 1221 / 1470) loss: 1.510343\n",
      "(Iteration 1231 / 1470) loss: 1.453059\n",
      "(Iteration 1241 / 1470) loss: 1.626183\n",
      "(Iteration 1251 / 1470) loss: 1.768949\n",
      "(Iteration 1261 / 1470) loss: 1.352718\n",
      "(Iteration 1271 / 1470) loss: 1.552764\n",
      "(Iteration 1281 / 1470) loss: 1.507158\n",
      "(Iteration 1291 / 1470) loss: 1.288505\n",
      "(Iteration 1301 / 1470) loss: 1.572656\n",
      "(Iteration 1311 / 1470) loss: 1.294737\n",
      "(Iteration 1321 / 1470) loss: 1.405845\n",
      "(Iteration 1331 / 1470) loss: 1.584947\n",
      "(Iteration 1341 / 1470) loss: 1.712634\n",
      "(Iteration 1351 / 1470) loss: 1.468826\n",
      "(Iteration 1361 / 1470) loss: 1.501406\n",
      "(Iteration 1371 / 1470) loss: 1.919522\n",
      "(Iteration 1381 / 1470) loss: 1.305957\n",
      "(Iteration 1391 / 1470) loss: 1.450920\n",
      "(Iteration 1401 / 1470) loss: 1.553306\n",
      "(Iteration 1411 / 1470) loss: 1.502996\n",
      "(Iteration 1421 / 1470) loss: 1.517815\n",
      "(Iteration 1431 / 1470) loss: 1.659034\n",
      "(Iteration 1441 / 1470) loss: 1.630565\n",
      "(Iteration 1451 / 1470) loss: 1.502329\n",
      "(Iteration 1461 / 1470) loss: 1.523597\n",
      "(Epoch 3 / 3) train acc: 0.510000; val_acc: 0.507000\n",
      "Validation set best accuracy:  0.504\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# find batch/layer normalization and dropout useful. Store your best model in  #\n",
    "# the best_model variable.                                                     #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "best_val_acc = 0\n",
    "for learning_late in [1e-3, 5e-4, 1e-4, 5e-5]:\n",
    "    model = FullyConnectedNet(\n",
    "        [100, 100, 100],\n",
    "        normalization = 'batchnorm',\n",
    "        dropout_keep_ratio = 0.8,\n",
    "        weight_scale=5e-3, \n",
    "        )\n",
    "    solver = Solver(\n",
    "        model,\n",
    "        data,\n",
    "        num_epochs=3,\n",
    "        batch_size=100,\n",
    "        update_rule='adam',\n",
    "        optim_config={'learning_rate': learning_rate},\n",
    "        verbose=False\n",
    "        )\n",
    "    solver.train()\n",
    "    y_val_pred = np.argmax(model.loss(data['X_val']), axis=1)\n",
    "    print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "    temp_val_acc = (y_val_pred == data['y_val']).mean()\n",
    "    if temp_val_acc > best_val_acc :\n",
    "        best_val_acc = temp_val_acc\n",
    "        best_model = model\n",
    "\n",
    "solver2 = Solver(\n",
    "    best_model,\n",
    "    data,\n",
    "    num_epochs=3,\n",
    "    batch_size=100,\n",
    "    update_rule='adam',\n",
    "    optim_config={'learning_rate': 1e-6},\n",
    "    verbose=True\n",
    "    )\n",
    "solver2.train()\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set best accuracy: ', (y_val_pred == data['y_val']).mean())    \n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "93VRiGcVGT3B"
   },
   "source": [
    "# Test Your Model!\n",
    "Run your best model on the validation and test sets. You should achieve at least 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 243,
     "status": "ok",
     "timestamp": 1627613907196,
     "user": {
      "displayName": "이재민",
      "photoUrl": "",
      "userId": "14376317564121567982"
     },
     "user_tz": -540
    },
    "id": "W1Fe8SGMGT3B",
    "outputId": "5f20765f-4473-4633-e10f-6e98c01a4c5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.504\n",
      "Test set accuracy:  0.493\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
 ],
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