FullyConnectedNets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "\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",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\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": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984772881e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print 'Testing affine_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  9.58567310126e-10\n",
      "dw error:  1.62268370119e-10\n",
      "db error:  7.28853211137e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print 'Testing affine_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-8\n",
    "print 'Testing relu_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2755751031e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-12\n",
    "print 'Testing relu_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  5.44645772585e-08\n",
      "dw error:  1.14396982864e-10\n",
      "db error:  4.39079998124e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print 'Testing affine_relu_forward:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.9990773303\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.30249318238\n",
      "dx error:  7.21326680211e-09\n"
     ]
    }
   ],
   "source": [
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print 'Testing svm_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print '\\nTesting softmax_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.22e-08\n",
      "W2 relative error: 3.32e-10\n",
      "b1 relative error: 8.37e-09\n",
      "b2 relative error: 2.53e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 8.89e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-2\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print 'Testing initialization ... '\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print 'Testing test-time forward pass ... '\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print 'Testing training loss (no regularization)'\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print 'Running numeric gradient check with reg = ', reg\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\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)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.301182\n",
      "(Epoch 0 / 10) train acc: 0.139000; val_acc: 0.130000\n",
      "(Iteration 101 / 4900) loss: 1.879989\n",
      "(Iteration 201 / 4900) loss: 1.636185\n",
      "(Iteration 301 / 4900) loss: 1.410916\n",
      "(Iteration 401 / 4900) loss: 1.654348\n",
      "(Epoch 1 / 10) train acc: 0.428000; val_acc: 0.430000\n",
      "(Iteration 501 / 4900) loss: 1.514665\n",
      "(Iteration 601 / 4900) loss: 1.592253\n",
      "(Iteration 701 / 4900) loss: 1.515267\n",
      "(Iteration 801 / 4900) loss: 1.374762\n",
      "(Iteration 901 / 4900) loss: 1.505734\n",
      "(Epoch 2 / 10) train acc: 0.517000; val_acc: 0.477000\n",
      "(Iteration 1001 / 4900) loss: 1.503369\n",
      "(Iteration 1101 / 4900) loss: 1.324890\n",
      "(Iteration 1201 / 4900) loss: 1.565586\n",
      "(Iteration 1301 / 4900) loss: 1.563032\n",
      "(Iteration 1401 / 4900) loss: 1.488092\n",
      "(Epoch 3 / 10) train acc: 0.512000; val_acc: 0.483000\n",
      "(Iteration 1501 / 4900) loss: 1.453178\n",
      "(Iteration 1601 / 4900) loss: 1.377953\n",
      "(Iteration 1701 / 4900) loss: 1.432707\n",
      "(Iteration 1801 / 4900) loss: 1.340795\n",
      "(Iteration 1901 / 4900) loss: 1.118487\n",
      "(Epoch 4 / 10) train acc: 0.522000; val_acc: 0.515000\n",
      "(Iteration 2001 / 4900) loss: 1.333532\n",
      "(Iteration 2101 / 4900) loss: 1.289967\n",
      "(Iteration 2201 / 4900) loss: 1.179547\n",
      "(Iteration 2301 / 4900) loss: 1.359798\n",
      "(Iteration 2401 / 4900) loss: 1.169625\n",
      "(Epoch 5 / 10) train acc: 0.551000; val_acc: 0.504000\n",
      "(Iteration 2501 / 4900) loss: 1.154116\n",
      "(Iteration 2601 / 4900) loss: 1.179887\n",
      "(Iteration 2701 / 4900) loss: 1.412906\n",
      "(Iteration 2801 / 4900) loss: 1.459391\n",
      "(Iteration 2901 / 4900) loss: 1.067051\n",
      "(Epoch 6 / 10) train acc: 0.558000; val_acc: 0.511000\n",
      "(Iteration 3001 / 4900) loss: 1.297444\n",
      "(Iteration 3101 / 4900) loss: 1.246288\n",
      "(Iteration 3201 / 4900) loss: 1.373830\n",
      "(Iteration 3301 / 4900) loss: 1.318969\n",
      "(Iteration 3401 / 4900) loss: 1.151832\n",
      "(Epoch 7 / 10) train acc: 0.572000; val_acc: 0.507000\n",
      "(Iteration 3501 / 4900) loss: 1.231649\n",
      "(Iteration 3601 / 4900) loss: 1.224317\n",
      "(Iteration 3701 / 4900) loss: 1.273314\n",
      "(Iteration 3801 / 4900) loss: 1.189628\n",
      "(Iteration 3901 / 4900) loss: 1.125258\n",
      "(Epoch 8 / 10) train acc: 0.596000; val_acc: 0.529000\n",
      "(Iteration 4001 / 4900) loss: 1.134878\n",
      "(Iteration 4101 / 4900) loss: 1.080516\n",
      "(Iteration 4201 / 4900) loss: 1.080427\n",
      "(Iteration 4301 / 4900) loss: 1.147761\n",
      "(Iteration 4401 / 4900) loss: 1.219607\n",
      "(Epoch 9 / 10) train acc: 0.600000; val_acc: 0.502000\n",
      "(Iteration 4501 / 4900) loss: 0.884663\n",
      "(Iteration 4601 / 4900) loss: 1.089387\n",
      "(Iteration 4701 / 4900) loss: 1.146644\n",
      "(Iteration 4801 / 4900) loss: 0.987549\n",
      "(Epoch 10 / 10) train acc: 0.597000; val_acc: 0.506000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "solver = Solver(model, data,\n",
    "                  update_rule='sgd',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  lr_decay=0.95,\n",
    "                  num_epochs=10, batch_size=100,\n",
    "                  print_every=100)        \n",
    "solver.train()   \n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bNiwiGJwD/BfwO+AVktfQjRq1PWENXUXFHgoKFjN+/EHGjXue0aNnEIns7szpi4iIiIiI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\nypSBTpIkSZLKlIFOkiRJksrU/wd6gb8qpTX9cQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d657e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
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      "Initial loss:  2.30145043419\n",
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      "2.30145044141\n",
      "2.30145042698\n",
      "W1 relative error: 1.51e-06\n",
      "2.30145043419\n",
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      "2.30145043419\n",
      "2.30145043419\n",
      "W2 relative error: 2.12e-06\n",
      "2.30145043419\n",
      "2.30145043739\n",
      "2.301450431\n",
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      "2.30145042778\n",
      "2.30145040293\n",
      "2.30145046546\n",
      "2.30145044065\n",
      "2.30145042774\n",
      "2.30145044065\n",
      "2.30145042773\n",
      "2.30145044061\n",
      "2.30145042778\n",
      "2.30145044062\n",
      "2.30145042777\n",
      "2.30145044061\n",
      "2.30145042778\n",
      "2.30145041397\n",
      "2.30145045441\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043972\n",
      "2.30145042866\n",
      "2.30145043975\n",
      "2.30145042864\n",
      "2.30145043969\n",
      "2.30145042869\n",
      "2.30145043973\n",
      "2.30145042865\n",
      "2.30145043975\n",
      "2.30145042864\n",
      "2.30145043973\n",
      "2.30145042865\n",
      "2.30145043971\n",
      "2.30145042868\n",
      "2.30145043973\n",
      "2.30145042866\n",
      "2.30145043971\n",
      "2.30145042868\n",
      "2.30145038442\n",
      "2.30145048397\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "W3 relative error: 1.90e-07\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.3014504396\n",
      "2.30145042878\n",
      "2.30145040394\n",
      "2.30145046445\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145042036\n",
      "2.30145044802\n",
      "2.30145036134\n",
      "2.30145050705\n",
      "2.30145039551\n",
      "2.30145047288\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043671\n",
      "2.30145043168\n",
      "2.30145039007\n",
      "2.30145047832\n",
      "2.30145041217\n",
      "2.30145045622\n",
      "2.30145037066\n",
      "2.30145049773\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145049622\n",
      "2.30145037217\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.3014504999\n",
      "2.30145036848\n",
      "2.30145047442\n",
      "2.30145039397\n",
      "2.30145041711\n",
      "2.30145045127\n",
      "b1 relative error: 1.25e-08\n",
      "2.30145043419\n",
      "2.30145077914\n",
      "2.30145008925\n",
      "2.30145042597\n",
      "2.30145044242\n",
      "2.30145018349\n",
      "2.3014506849\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145055028\n",
      "2.30145031811\n",
      "2.30145038563\n",
      "2.30145048275\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145076474\n",
      "2.30145010365\n",
      "2.30145031903\n",
      "2.30145054936\n",
      "2.30145097298\n",
      "2.30144989541\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.3014503392\n",
      "2.30145052919\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145044875\n",
      "2.30145041964\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30144976065\n",
      "2.30145110774\n",
      "2.30145032778\n",
      "2.30145054061\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.3014500628\n",
      "2.30145080559\n",
      "2.30145018318\n",
      "2.30145068521\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145044957\n",
      "2.30145041881\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "2.30145043419\n",
      "b2 relative error: 3.78e-09\n",
      "2.30145043419\n",
      "2.30145143292\n",
      "2.30144943547\n",
      "2.30145143854\n",
      "2.30144942985\n",
      "2.30145142981\n",
      "2.30144943858\n",
      "2.30144643454\n",
      "2.30145443385\n",
      "2.30145143732\n",
      "2.30144943108\n",
      "2.30145143732\n",
      "2.30144943108\n",
      "2.30145143053\n",
      "2.30144943786\n",
      "2.30145143251\n",
      "2.30144943588\n",
      "2.30145143095\n",
      "2.30144943744\n",
      "2.30144643752\n",
      "2.30145443088\n",
      "b3 relative error: 1.20e-10\n",
      "Running check with reg =  3.14\n",
      "7.14322965776\n",
      "Initial loss:  7.14322965776\n",
      "7.14322965776\n",
      "7.14323109508\n",
      "7.14322822075\n",
      "7.14323033761\n",
      "7.14322897822\n",
      "7.14323077309\n",
      "7.14322854274\n",
      "7.14322928678\n",
      "7.14323002905\n",
      "7.14323042934\n",
      "7.14322888649\n",
      "7.14323234689\n",
      "7.14322696893\n",
      "7.14322743492\n",
      "7.14323188091\n",
      "7.14322911876\n",
      "7.14323019706\n",
      "7.14322910315\n",
      "7.14323021268\n",
      "7.14322776627\n",
      "7.14323154955\n",
      "7.14322994441\n",
      "7.14322937142\n",
      "7.14323113327\n",
      "7.14322818255\n",
      "7.14322836782\n",
      "7.143230948\n",
      "7.14323029461\n",
      "7.14322902122\n",
      "7.14322865723\n",
      "7.1432306586\n",
      "7.14323089096\n",
      "7.14322842487\n",
      "7.14322851673\n",
      "7.1432307991\n",
      "7.14322818042\n",
      "7.14323113541\n",
      "7.14323085979\n",
      "7.14322845604\n",
      "7.14322812231\n",
      "7.14323119351\n",
      "7.14323043104\n",
      "7.14322888479\n",
      "7.1432303206\n",
      "7.14322899523\n",
      "7.14322914331\n",
      "7.14323017251\n",
      "7.14323015142\n",
      "7.14322916441\n",
      "7.14322551232\n",
      "7.14323380351\n",
      "7.14322997702\n",
      "7.1432293388\n",
      "7.14323066132\n",
      "7.1432286545\n",
      "7.1432329733\n",
      "7.14322634253\n",
      "7.14323233261\n",
      "7.14322698322\n",
      "7.1432291183\n",
      "7.14323019753\n",
      "7.1432299372\n",
      "7.14322937863\n",
      "7.14323039489\n",
      "7.14322892094\n",
      "7.14322793301\n",
      "7.14323138282\n",
      "7.14322897695\n",
      "7.14323033888\n",
      "7.14323301839\n",
      "7.14322629744\n",
      "7.14322989096\n",
      "7.14322942487\n",
      "7.14323209955\n",
      "7.14322721628\n",
      "7.1432309224\n",
      "7.14322839343\n",
      "7.14322742718\n",
      "7.14323188864\n",
      "7.14322953465\n",
      "7.14322978118\n",
      "7.14322883941\n",
      "7.14323047642\n",
      "7.14323077096\n",
      "7.14322854487\n",
      "7.14323004063\n",
      "7.14322927519\n",
      "7.14322882816\n",
      "7.14323048767\n",
      "7.14322921816\n",
      "7.14323009767\n",
      "7.14322738202\n",
      "7.14323193381\n",
      "7.14322901644\n",
      "7.14323029938\n",
      "7.14322986795\n",
      "7.14322944788\n",
      "7.14323063201\n",
      "7.14322868381\n",
      "7.14323038985\n",
      "7.14322892598\n",
      "7.14322907497\n",
      "7.14323024086\n",
      "7.14322678791\n",
      "7.14323252792\n",
      "7.14322764724\n",
      "7.14323166859\n",
      "7.14323124763\n",
      "7.14322806819\n",
      "7.14322914345\n",
      "7.14323017238\n",
      "7.14322739821\n",
      "7.14323191762\n",
      "7.14322918374\n",
      "7.14323013209\n",
      "7.14323014313\n",
      "7.14322917269\n",
      "7.14323059877\n",
      "7.14322871705\n",
      "7.14323027168\n",
      "7.14322904415\n",
      "7.14322951911\n",
      "7.14322979672\n",
      "7.14322879352\n",
      "7.1432305223\n",
      "7.14322898431\n",
      "7.14323033152\n",
      "7.1432308062\n",
      "7.14322850963\n",
      "7.14322882448\n",
      "7.14323049135\n",
      "7.14323115243\n",
      "7.1432281634\n",
      "7.14323176928\n",
      "7.14322754654\n",
      "7.14322863656\n",
      "7.14323067926\n",
      "7.14323083754\n",
      "7.14322847829\n",
      "7.14322892767\n",
      "7.14323038816\n",
      "7.14322889948\n",
      "7.14323041635\n",
      "7.14323131788\n",
      "7.14322799795\n",
      "7.1432313185\n",
      "7.14322799733\n",
      "7.14322687655\n",
      "7.14323243927\n",
      "7.14322734596\n",
      "7.14323196986\n",
      "7.14323062176\n",
      "7.14322869407\n",
      "7.14323281178\n",
      "7.14322650404\n",
      "7.14322584928\n",
      "7.14323346654\n",
      "7.14323104577\n",
      "7.14322827006\n",
      "7.1432317188\n",
      "7.14322759703\n",
      "7.14323023909\n",
      "7.14322907673\n",
      "7.14322989648\n",
      "7.14322941935\n",
      "7.14322928876\n",
      "7.14323002707\n",
      "7.14323094608\n",
      "7.14322836975\n",
      "7.14322998325\n",
      "7.14322933257\n",
      "7.14322715734\n",
      "7.14323215849\n",
      "7.14322961593\n",
      "7.1432296999\n",
      "7.14323216133\n",
      "7.1432271545\n",
      "7.14323118388\n",
      "7.14322813195\n",
      "7.14322978852\n",
      "7.14322952731\n",
      "7.14323045566\n",
      "7.14322886016\n",
      "7.14323096921\n",
      "7.14322834661\n",
      "7.14322973638\n",
      "7.14322957945\n",
      "7.14322909276\n",
      "7.14323022307\n",
      "7.14322788632\n",
      "7.14323142951\n",
      "7.14322996768\n",
      "7.14322934814\n",
      "7.14323034825\n",
      "7.14322896758\n",
      "7.14323297771\n",
      "7.14322633812\n",
      "7.14323001766\n",
      "7.14322929817\n",
      "7.14322919083\n",
      "7.14323012499\n",
      "7.14322850557\n",
      "7.14323081026\n",
      "7.14323116151\n",
      "7.14322815432\n",
      "7.14322843763\n",
      "7.14323087819\n",
      "7.14323105386\n",
      "7.14322826197\n",
      "7.14322902259\n",
      "7.14323029324\n",
      "7.14322931625\n",
      "7.14322999958\n",
      "7.14323099501\n",
      "7.14322832082\n",
      "7.14323166559\n",
      "7.14322765024\n",
      "7.14322828995\n",
      "7.14323102588\n",
      "7.14322612144\n",
      "7.14323319439\n",
      "7.14322680977\n",
      "7.14323250606\n",
      "7.14322968397\n",
      "7.14322963185\n",
      "7.14322791333\n",
      "7.1432314025\n",
      "7.14322786066\n",
      "7.14323145516\n",
      "7.14323064491\n",
      "7.14322867092\n",
      "7.14322984358\n",
      "7.14322947225\n",
      "7.14323051416\n",
      "7.14322880167\n",
      "7.14322872813\n",
      "7.1432305877\n",
      "7.143230674\n",
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      "7.14322684932\n",
      "7.14323246651\n",
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      "7.14323042121\n",
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      "7.14323265114\n",
      "7.14322666468\n",
      "7.14323060074\n",
      "7.14322871509\n",
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      "7.14323090863\n",
      "7.14322839042\n",
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      "7.14322973728\n",
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      "7.14323060039\n",
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      "7.14323009892\n",
      "7.14322921691\n",
      "7.14322802898\n",
      "7.14323128684\n",
      "7.14322945786\n",
      "7.14322985797\n",
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      "7.14322774422\n",
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      "7.14322795802\n",
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      "W1 relative error: 9.72e-09\n",
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      "W2 relative error: 1.29e-07\n",
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      "7.14323226353\n",
      "7.1432270523\n",
      "7.14323123351\n",
      "7.14322808231\n",
      "W3 relative error: 1.99e-08\n",
      "7.14322965776\n",
      "7.14322965776\n",
      "7.14322965776\n",
      "7.14322966001\n",
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      "7.14322964345\n",
      "7.14322965776\n",
      "7.14322965776\n",
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      "7.14322965776\n",
      "7.14322965088\n",
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      "7.14322967522\n",
      "7.14322965776\n",
      "7.14322965776\n",
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      "7.14322965776\n",
      "7.14322965776\n",
      "7.14322965776\n",
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      "7.14322962342\n",
      "7.14322969209\n",
      "7.14322960338\n",
      "7.14322971213\n",
      "b1 relative error: 1.81e-08\n",
      "7.14322965776\n",
      "7.14322965915\n",
      "7.14322965637\n",
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      "7.1432296383\n",
      "7.14322967429\n",
      "7.14322964123\n",
      "7.14322965776\n",
      "7.14322965776\n",
      "7.14322965776\n",
      "7.14322965776\n",
      "7.14322963091\n",
      "7.1432296846\n",
      "7.14323003314\n",
      "7.14322928237\n",
      "7.14322958668\n",
      "7.14322972883\n",
      "7.14322965776\n",
      "7.14322965776\n",
      "7.14322959343\n",
      "7.14322972208\n",
      "7.1432293728\n",
      "7.14322994271\n",
      "7.14322965776\n",
      "7.14322965776\n",
      "b2 relative error: 1.05e-08\n",
      "7.14322965776\n",
      "7.14323065748\n",
      "7.14322865804\n",
      "7.14323065915\n",
      "7.14322865637\n",
      "7.14323065891\n",
      "7.14322865662\n",
      "7.14322566504\n",
      "7.14323365048\n",
      "7.14323065836\n",
      "7.14322865717\n",
      "7.14323064986\n",
      "7.14322866566\n",
      "7.14323065904\n",
      "7.14322865648\n",
      "7.14323065945\n",
      "7.14322865607\n",
      "7.14323065084\n",
      "7.14322866469\n",
      "7.14322565949\n",
      "7.14323365603\n",
      "b3 relative error: 2.17e-10\n"
     ]
    }
   ],
   "source": [
    "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([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print 'Initial loss: ', loss\n",
    "\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 '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 3.116047\n",
      "(Epoch 0 / 20) train acc: 0.340000; val_acc: 0.106000\n",
      "(Epoch 1 / 20) train acc: 0.360000; val_acc: 0.129000\n",
      "(Epoch 2 / 20) train acc: 0.560000; val_acc: 0.118000\n",
      "(Epoch 3 / 20) train acc: 0.840000; val_acc: 0.145000\n",
      "(Epoch 4 / 20) train acc: 0.920000; val_acc: 0.165000\n",
      "(Epoch 5 / 20) train acc: 0.920000; val_acc: 0.140000\n",
      "(Iteration 11 / 40) loss: 0.452440\n",
      "(Epoch 6 / 20) train acc: 0.960000; val_acc: 0.139000\n",
      "(Epoch 7 / 20) train acc: 1.000000; val_acc: 0.166000\n",
      "(Epoch 8 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.156000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Iteration 21 / 40) loss: 0.046835\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.160000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.163000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.164000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.164000\n",
      "(Iteration 31 / 40) loss: 0.011730\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.164000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.165000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.166000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.163000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.163000\n"
     ]
    },
    {
     "data": {
      "image/png": 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JUgG8+1KSJGlIGcokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCrBt0AVo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\nkqQCGMokSZIK8P8BvF9PcJHPIxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d6e6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\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 = 2e-2\n",
    "learning_rate = 1e-2\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 3.512054\n",
      "(Epoch 0 / 20) train acc: 0.120000; val_acc: 0.105000\n",
      "(Epoch 1 / 20) train acc: 0.180000; val_acc: 0.100000\n",
      "(Epoch 2 / 20) train acc: 0.300000; val_acc: 0.105000\n",
      "(Epoch 3 / 20) train acc: 0.600000; val_acc: 0.129000\n",
      "(Epoch 4 / 20) train acc: 0.780000; val_acc: 0.122000\n",
      "(Epoch 5 / 20) train acc: 0.840000; val_acc: 0.136000\n",
      "(Iteration 11 / 40) loss: 0.995333\n",
      "(Epoch 6 / 20) train acc: 0.880000; val_acc: 0.149000\n",
      "(Epoch 7 / 20) train acc: 0.940000; val_acc: 0.136000\n",
      "(Epoch 8 / 20) train acc: 0.940000; val_acc: 0.136000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.137000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.155000\n",
      "(Iteration 21 / 40) loss: 0.203252\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.138000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.155000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.146000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.151000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Iteration 31 / 40) loss: 0.063457\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.151000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.148000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.146000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.150000\n"
     ]
    },
    {
     "data": {
      "image/png": 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FFkwFmA8CF0yJuVgA88PCpzAllncAYDMCFwBAZwIX7IJJL5i6U4PBICsrK1lZ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133ce890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\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 = 5e-2\n",
    "learning_rate = 1e-2\n",
    "\n",
    "model = FullyConnectedNet([100, 100,100,100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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 stochstic gradient descent.\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 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd, 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",
    "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": {},
   "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": 118,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.817051\n",
      "(Epoch 0 / 5) train acc: 0.113000; val_acc: 0.103000\n",
      "(Iteration 11 / 200) loss: 2.205383\n",
      "(Iteration 21 / 200) loss: 2.078117\n",
      "(Iteration 31 / 200) loss: 2.040247\n",
      "(Epoch 1 / 5) train acc: 0.265000; val_acc: 0.233000\n",
      "(Iteration 41 / 200) loss: 2.068026\n",
      "(Iteration 51 / 200) loss: 2.004828\n",
      "(Iteration 61 / 200) loss: 1.867958\n",
      "(Iteration 71 / 200) loss: 1.910531\n",
      "(Epoch 2 / 5) train acc: 0.363000; val_acc: 0.313000\n",
      "(Iteration 81 / 200) loss: 1.975508\n",
      "(Iteration 91 / 200) loss: 1.799013\n",
      "(Iteration 101 / 200) loss: 1.815761\n",
      "(Iteration 111 / 200) loss: 1.862731\n",
      "(Epoch 3 / 5) train acc: 0.307000; val_acc: 0.272000\n",
      "(Iteration 121 / 200) loss: 1.771410\n",
      "(Iteration 131 / 200) loss: 1.818318\n",
      "(Iteration 141 / 200) loss: 1.725567\n",
      "(Iteration 151 / 200) loss: 1.816248\n",
      "(Epoch 4 / 5) train acc: 0.410000; val_acc: 0.315000\n",
      "(Iteration 161 / 200) loss: 1.711379\n",
      "(Iteration 171 / 200) loss: 1.599890\n",
      "(Iteration 181 / 200) loss: 1.631646\n",
      "(Iteration 191 / 200) loss: 1.601632\n",
      "(Epoch 5 / 5) train acc: 0.432000; val_acc: 0.308000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.721949\n",
      "(Epoch 0 / 5) train acc: 0.115000; val_acc: 0.114000\n",
      "(Iteration 11 / 200) loss: 2.148698\n",
      "(Iteration 21 / 200) loss: 1.927747\n",
      "(Iteration 31 / 200) loss: 1.872777\n",
      "(Epoch 1 / 5) train acc: 0.317000; val_acc: 0.228000\n",
      "(Iteration 41 / 200) loss: 1.919762\n",
      "(Iteration 51 / 200) loss: 1.938394\n",
      "(Iteration 61 / 200) loss: 1.709037\n",
      "(Iteration 71 / 200) loss: 1.637444\n",
      "(Epoch 2 / 5) train acc: 0.408000; val_acc: 0.346000\n",
      "(Iteration 81 / 200) loss: 1.693700\n",
      "(Iteration 91 / 200) loss: 1.487130\n",
      "(Iteration 101 / 200) loss: 1.775475\n",
      "(Iteration 111 / 200) loss: 1.723997\n",
      "(Epoch 3 / 5) train acc: 0.443000; val_acc: 0.316000\n",
      "(Iteration 121 / 200) loss: 1.521722\n",
      "(Iteration 131 / 200) loss: 1.559446\n",
      "(Iteration 141 / 200) loss: 1.400913\n",
      "(Iteration 151 / 200) loss: 1.278240\n",
      "(Epoch 4 / 5) train acc: 0.499000; val_acc: 0.316000\n",
      "(Iteration 161 / 200) loss: 1.655975\n",
      "(Iteration 171 / 200) loss: 1.420473\n",
      "(Iteration 181 / 200) loss: 1.318459\n",
      "(Iteration 191 / 200) loss: 1.320768\n",
      "(Epoch 5 / 5) train acc: 0.521000; val_acc: 0.340000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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YTcv0VHA3MjJiupM8EZFXsLJJVD38vBFRPamrPncM7IiIiIiIiMxhNEVERERERFQHGtwu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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11483c6d0>"
      ]
     },
     "metadata": {},
     "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([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "[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": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\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",
    "\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",
    "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": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.13956917985e-07\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\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",
    "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": {},
   "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": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 3.118128\n",
      "(Epoch 0 / 5) train acc: 0.116000; val_acc: 0.118000\n",
      "(Iteration 11 / 200) loss: 2.083615\n",
      "(Iteration 21 / 200) loss: 1.749668\n",
      "(Iteration 31 / 200) loss: 2.000201\n",
      "(Epoch 1 / 5) train acc: 0.320000; val_acc: 0.303000\n",
      "(Iteration 41 / 200) loss: 1.864468\n",
      "(Iteration 51 / 200) loss: 1.828154\n",
      "(Iteration 61 / 200) loss: 1.785178\n",
      "(Iteration 71 / 200) loss: 1.496694\n",
      "(Epoch 2 / 5) train acc: 0.466000; val_acc: 0.370000\n",
      "(Iteration 81 / 200) loss: 1.801313\n",
      "(Iteration 91 / 200) loss: 1.403521\n",
      "(Iteration 101 / 200) loss: 1.583729\n",
      "(Iteration 111 / 200) loss: 1.323825\n",
      "(Epoch 3 / 5) train acc: 0.487000; val_acc: 0.353000\n",
      "(Iteration 121 / 200) loss: 1.603707\n",
      "(Iteration 131 / 200) loss: 1.739192\n",
      "(Iteration 141 / 200) loss: 1.327837\n",
      "(Iteration 151 / 200) loss: 1.586864\n",
      "(Epoch 4 / 5) train acc: 0.583000; val_acc: 0.379000\n",
      "(Iteration 161 / 200) loss: 1.157177\n",
      "(Iteration 171 / 200) loss: 1.250410\n",
      "(Iteration 181 / 200) loss: 1.078163\n",
      "(Iteration 191 / 200) loss: 1.151740\n",
      "(Epoch 5 / 5) train acc: 0.589000; val_acc: 0.371000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.660074\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.127000\n",
      "(Iteration 11 / 200) loss: 2.067496\n",
      "(Iteration 21 / 200) loss: 1.967562\n",
      "(Iteration 31 / 200) loss: 1.899932\n",
      "(Epoch 1 / 5) train acc: 0.364000; val_acc: 0.296000\n",
      "(Iteration 41 / 200) loss: 1.821239\n",
      "(Iteration 51 / 200) loss: 1.746413\n",
      "(Iteration 61 / 200) loss: 1.778453\n",
      "(Iteration 71 / 200) loss: 1.647641\n",
      "(Epoch 2 / 5) train acc: 0.408000; val_acc: 0.316000\n",
      "(Iteration 81 / 200) loss: 1.613468\n",
      "(Iteration 91 / 200) loss: 1.514946\n",
      "(Iteration 101 / 200) loss: 1.477500\n",
      "(Iteration 111 / 200) loss: 1.510368\n",
      "(Epoch 3 / 5) train acc: 0.473000; val_acc: 0.333000\n",
      "(Iteration 121 / 200) loss: 1.541160\n",
      "(Iteration 131 / 200) loss: 1.601257\n",
      "(Iteration 141 / 200) loss: 1.466909\n",
      "(Iteration 151 / 200) loss: 1.372683\n",
      "(Epoch 4 / 5) train acc: 0.493000; val_acc: 0.340000\n",
      "(Iteration 161 / 200) loss: 1.342383\n",
      "(Iteration 171 / 200) loss: 1.507276\n",
      "(Iteration 181 / 200) loss: 1.405596\n",
      "(Iteration 191 / 200) loss: 1.413718\n",
      "(Epoch 5 / 5) train acc: 0.538000; val_acc: 0.341000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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PmDFj6NOnD4ceeijr16+Pei769OkTTsCSlpYGwBFHHBF+PPJ539+8Xi8FHk9sc/G6p/nt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\nLVsgN9d927wZQkPBbq/7+OBgWL4cxow5/2tti5yWRX5ZWZ09c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Wp8TUSkPmsPrCVzTSbv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\n+LFEvMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc79650>"
      ]
     },
     "metadata": {},
     "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([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 net.\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 nets rather than fully-connected nets.\n",
    "\n",
    "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": null,
   "metadata": {
    "collapsed": false,
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   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(X_test), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(X_val), axis=1)\n",
    "print 'Validation set accuracy: ', (y_val_pred == y_val).mean()\n",
    "print 'Test set accuracy: ', (y_test_pred == y_test).mean()"
   ]
  }
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