assignment2/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 dout (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/Layer Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "\n",
    "print(platform.python_implementation())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.769847728806635e-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 e-9 or less.\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": "markdown",
   "metadata": {},
   "source": [
    "hxuaj note: \n",
    "\n",
    "### Detailed calculation\n",
    "The gradient here is the gradient of the final output(y) with respect to x, w and b. The final output is scalar.\n",
    "\n",
    "$out = XW + b$, $\\ \\ X: N\\times D, \\ \\ W: D\\times M, \\ \\ b: 1\\times M, \\ \\ out: N\\times M$\n",
    "\n",
    "First find out the gradient with respect to $X$ using chain rule. \n",
    "$$tr(\\text{d}y) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}out) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}X\\cdot W) = tr(W(\\frac {\\partial y} {\\partial out})^T \\text{d}X)$$\n",
    "\n",
    "So, $\\frac{\\partial y}{\\partial X} = \\frac{\\partial y}{\\partial out} \\cdot W^T$, where $\\frac{\\partial y}{\\partial out}$ is the upstream derivative \"dout\" in `cs231n/layers.py`.\n",
    "\n",
    "The gradient with respect to $W$ is similar:\n",
    "$$tr(\\text{d}y) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}out) = tr((\\frac {\\partial y} {\\partial out})^T X\\text{d}W)$$\n",
    "So, $\\frac{\\partial y}{\\partial W} = X^T \\cdot \\frac{\\partial y}{\\partial out}$.\n",
    "\n",
    "Should be careful with the gradient with respect to $b$, where $b$ is defined as a $1\\times M$ vector. While in code the \"add\" operation is implemented row-wise, which means each row of the dot product $XW$ add vector $b$ element-wise.\n",
    "$$tr(\\text{d}y) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}out) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}b_{hidden}), \\ \\ \\ b_{hidden} = \\begin{bmatrix} \\longleftarrow b \\longrightarrow \\\\ \\longleftarrow b \\longrightarrow \\\\ \\dots \\\\\\longleftarrow b \\longrightarrow \\end{bmatrix}_{N\\times M} = \\begin{bmatrix}1\\\\1\\\\ \\cdots \\\\1 \\end{bmatrix}_{N\\times 1} \\begin{bmatrix}\\longleftarrow b \\longrightarrow \\end{bmatrix}_{1\\times M}$$\n",
    "So, $tr(\\text{d}y) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}out) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}b_{hidden}) = tr((\\frac {\\partial y} {\\partial out})^T {\\Bbb1_{N\\times 1}} \\text{d}b)$, $\\frac{\\partial y}{\\partial b} = \\Bbb 1^T \\cdot \\frac{\\partial y}{\\partial out}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.399100368651805e-11\n",
      "dw error:  9.904211865398145e-11\n",
      "db error:  2.4122867568119087e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\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 e-10 or less\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 activation: 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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-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 on the order of e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: 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": "markdown",
   "metadata": {},
   "source": [
    "### Detailed calculation\n",
    "\n",
    "$out = ReLU(x)$, $x\\longrightarrow out \\longrightarrow y$\n",
    "```python\n",
    "mask[out > 0] = 1\n",
    "```\n",
    "\n",
    "$$tr(\\text{d}y) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}out) = tr((\\frac {\\partial y} {\\partial out})^T \\text{d}(mask\\odot x)) = tr((\\frac {\\partial y} {\\partial out})^T (mask\\odot \\text{d}X)) = tr((\\frac {\\partial y} {\\partial out} \\odot mask)^T\\text{d}x)$$\n",
    "\n",
    "So, the gradient with respect to x is $\\frac {\\partial y} {\\partial out}\\odot mask$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "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 on the order of e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1: \n",
    "\n",
    "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n",
    "1. Sigmoid\n",
    "2. ReLU\n",
    "3. Leaky ReLU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "[FILL THIS IN]\n",
    "\n",
    "Sigmoid will have the zero gradient flow problem, because the activation will easily get saturated.\n",
    "\n",
    "In the one dimensional case: \n",
    "* when the abs of input is too large sigmoid will be saturate.\n",
    "* when the input is negative, ReLU's activation is 0.\n",
    "* When the input is 0, Leaky ReLU's activation is 0."
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward and affine_relu_backward:\n",
      "dx error:  6.750562121603446e-11\n",
      "dw error:  8.162015570444288e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\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",
    "# Relative error should be around e-10 or less\n",
    "print('Testing affine_relu_forward and affine_relu_backward:')\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "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 around the order of e-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 close to 2.3 and dx error should be around e-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": 12,
   "metadata": {},
   "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: 3.48e-10\n",
      "b1 relative error: 6.55e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 8.18e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.09e-09\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "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-3\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",
    "# Errors should be around e-7 or less\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": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.304305\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_0.pkl\"\n",
      "(Epoch 0 / 10) train acc: 0.132000; val_acc: 0.130000\n",
      "(Iteration 101 / 4900) loss: 1.862001\n",
      "(Iteration 201 / 4900) loss: 1.755473\n",
      "(Iteration 301 / 4900) loss: 1.872329\n",
      "(Iteration 401 / 4900) loss: 1.466895\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_1.pkl\"\n",
      "(Epoch 1 / 10) train acc: 0.444000; val_acc: 0.432000\n",
      "(Iteration 501 / 4900) loss: 1.604912\n",
      "(Iteration 601 / 4900) loss: 1.517498\n",
      "(Iteration 701 / 4900) loss: 1.437288\n",
      "(Iteration 801 / 4900) loss: 1.396899\n",
      "(Iteration 901 / 4900) loss: 1.391467\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_2.pkl\"\n",
      "(Epoch 2 / 10) train acc: 0.484000; val_acc: 0.473000\n",
      "(Iteration 1001 / 4900) loss: 1.378383\n",
      "(Iteration 1101 / 4900) loss: 1.524760\n",
      "(Iteration 1201 / 4900) loss: 1.133807\n",
      "(Iteration 1301 / 4900) loss: 1.178968\n",
      "(Iteration 1401 / 4900) loss: 1.184108\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_3.pkl\"\n",
      "(Epoch 3 / 10) train acc: 0.534000; val_acc: 0.489000\n",
      "(Iteration 1501 / 4900) loss: 1.330608\n",
      "(Iteration 1601 / 4900) loss: 1.189489\n",
      "(Iteration 1701 / 4900) loss: 1.329472\n",
      "(Iteration 1801 / 4900) loss: 1.137892\n",
      "(Iteration 1901 / 4900) loss: 1.251658\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_4.pkl\"\n",
      "(Epoch 4 / 10) train acc: 0.542000; val_acc: 0.496000\n",
      "(Iteration 2001 / 4900) loss: 1.400220\n",
      "(Iteration 2101 / 4900) loss: 1.276764\n",
      "(Iteration 2201 / 4900) loss: 1.395837\n",
      "(Iteration 2301 / 4900) loss: 1.283414\n",
      "(Iteration 2401 / 4900) loss: 1.260643\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_5.pkl\"\n",
      "(Epoch 5 / 10) train acc: 0.599000; val_acc: 0.490000\n",
      "(Iteration 2501 / 4900) loss: 1.175149\n",
      "(Iteration 2601 / 4900) loss: 1.480159\n",
      "(Iteration 2701 / 4900) loss: 1.063832\n",
      "(Iteration 2801 / 4900) loss: 1.232631\n",
      "(Iteration 2901 / 4900) loss: 1.365638\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_6.pkl\"\n",
      "(Epoch 6 / 10) train acc: 0.570000; val_acc: 0.525000\n",
      "(Iteration 3001 / 4900) loss: 1.404251\n",
      "(Iteration 3101 / 4900) loss: 1.148190\n",
      "(Iteration 3201 / 4900) loss: 1.199218\n",
      "(Iteration 3301 / 4900) loss: 1.147920\n",
      "(Iteration 3401 / 4900) loss: 1.305050\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_7.pkl\"\n",
      "(Epoch 7 / 10) train acc: 0.614000; val_acc: 0.531000\n",
      "(Iteration 3501 / 4900) loss: 1.167591\n",
      "(Iteration 3601 / 4900) loss: 1.023930\n",
      "(Iteration 3701 / 4900) loss: 1.051806\n",
      "(Iteration 3801 / 4900) loss: 1.020001\n",
      "(Iteration 3901 / 4900) loss: 1.288267\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_8.pkl\"\n",
      "(Epoch 8 / 10) train acc: 0.594000; val_acc: 0.519000\n",
      "(Iteration 4001 / 4900) loss: 1.230017\n",
      "(Iteration 4101 / 4900) loss: 1.221618\n",
      "(Iteration 4201 / 4900) loss: 1.110177\n",
      "(Iteration 4301 / 4900) loss: 1.214582\n",
      "(Iteration 4401 / 4900) loss: 1.212733\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_9.pkl\"\n",
      "(Epoch 9 / 10) train acc: 0.606000; val_acc: 0.531000\n",
      "(Iteration 4501 / 4900) loss: 1.152541\n",
      "(Iteration 4601 / 4900) loss: 1.308475\n",
      "(Iteration 4701 / 4900) loss: 1.248372\n",
      "(Iteration 4801 / 4900) loss: 1.102512\n",
      "Saving checkpoint to \"TwoLayerNet_epoch_10.pkl\"\n",
      "(Epoch 10 / 10) train acc: 0.601000; val_acc: 0.537000\n",
      "Test accuracy:  0.531\n"
     ]
    }
   ],
   "source": [
    "#model = cs231n.classifiers.fc_net.TwoLayerNet()\n",
    "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={'learning_rate':1e-3},\n",
    "                lr_decay=0.75,\n",
    "                num_epochs=10, batch_size=100,\n",
    "                print_every=100,\n",
    "                checkpoint_name='TwoLayerNet')\n",
    "solver.train()\n",
    "\n",
    "# Check out the accuracy in test set.\n",
    "# scores = solver.model.loss(data['X_test'])\n",
    "# y_pred = np.argmax(scores, axis=1)\n",
    "# acc = np.mean(y_pred == data['y_test'])\n",
    "\n",
    "# use hashtagged code above or simply call the func in solver\n",
    "acc = solver.check_accuracy(data['X_test'], data['y_test'])\n",
    "print(\"Test accuracy: \", acc)\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The checkpoint is saved into a .pkl file. Here is the method to load it from .pkl.\n",
    "\n",
    "```python\n",
    "import pickle\n",
    "\n",
    "with open('TwoLayerNet_epoch_10.pkl', 'rb') as f:\n",
    "    a = pickle.load(f)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "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/layer 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-7 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 6.86e-09\n",
      "W2 relative error: 3.52e-08\n",
      "W3 relative error: 1.32e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.80e-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([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",
    "  # 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('%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. In the following cell, tweak the learning rate and initialization scale to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "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": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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\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": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hidden layer 1 has mean 3.606589 and std 64.216258\n",
      "hidden layer 2 has mean 19.292471 and std 31.168629\n",
      "hidden layer 3 has mean 3.200037 and std 4.452110\n"
     ]
    }
   ],
   "source": [
    "layer_means = [np.mean(H) for i,H in model.layerout.items()]\n",
    "layer_stds = [np.std(H) for i,H in model.layerout.items()]\n",
    "for i in range(0, len(layer_means)):\n",
    "    print(\"hidden layer %d has mean %f and std %f\"% (i+1, layer_means[i], layer_stds[i]))"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 166.501707\n",
      "(Epoch 0 / 20) train acc: 0.240000; val_acc: 0.116000\n",
      "(Epoch 1 / 20) train acc: 0.240000; val_acc: 0.085000\n",
      "(Epoch 2 / 20) train acc: 0.340000; val_acc: 0.110000\n",
      "(Epoch 3 / 20) train acc: 0.460000; val_acc: 0.139000\n",
      "(Epoch 4 / 20) train acc: 0.700000; val_acc: 0.134000\n",
      "(Epoch 5 / 20) train acc: 0.800000; val_acc: 0.120000\n",
      "(Iteration 11 / 40) loss: 7.619979\n",
      "(Epoch 6 / 20) train acc: 0.800000; val_acc: 0.140000\n",
      "(Epoch 7 / 20) train acc: 0.820000; val_acc: 0.128000\n",
      "(Epoch 8 / 20) train acc: 0.880000; val_acc: 0.140000\n",
      "(Epoch 9 / 20) train acc: 0.920000; val_acc: 0.121000\n",
      "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.127000\n",
      "(Iteration 21 / 40) loss: 1.669786\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.131000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.125000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.137000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Iteration 31 / 40) loss: 0.000445\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.132000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 = 5e-4 #5e-3\n",
    "weight_scale = 1e-1 #5e-2 \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": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hidden layer 1 has mean 3.606589 and std 64.216258\n",
      "hidden layer 2 has mean 135.097649 and std 209.848394\n",
      "hidden layer 3 has mean 76.493917 and std 129.150701\n",
      "hidden layer 4 has mean 47.708569 and std 80.987172\n",
      "hidden layer 5 has mean 21.991468 and std 41.516531\n"
     ]
    }
   ],
   "source": [
    "layer_means = [np.mean(H) for i,H in model.layerout.items()]\n",
    "layer_stds = [np.std(H) for i,H in model.layerout.items()]\n",
    "for i in range(0, len(layer_means)):\n",
    "    print(\"hidden layer %d has mean %f and std %f\"% (i+1, layer_means[i], layer_stds[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 2: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "[FILL THIS IN]\n",
    "\n",
    "Training three-layer net is easier.\n",
    "\n",
    "Five-layer net is more sensitive to the initialization scale. \n",
    "It looks like that five-layer net is more complex, which is a complex function with a lot of local minimum or saddle points. Depending on the initialization, it's easier to converge to a local minimum or saddle point. \n",
    "\n",
    "Actually, 训练迭代的过程中activation为0，导致梯度为0，从而停止了对W的更新。The reason is that there is a gradient vanishing problem especially when the depth is deeper. See the calculation below(weight_scale=1e-3, which is much smaller than 1e-1), the std has vanished. The activations will be zero in deeper layer. So during the backprop, gradient will be zero and there is no update.\n",
    "\n",
    "$$Var(activation) = Var(\\sum\\limits_{i=1}^n w_i x_i) = (nVar(w))Var(x)$$\n",
    "\n",
    "Activation的方差会随着初始化W的方差变小而越来越小，最终趋近于零。若使得activation和输入X的方差保持一致的话，应初始化W的方差为$\\frac{1}{\\sqrt n}$即$\\frac{1}{\\sqrt 50} \\approx 0.14142$。而0.001远远小于0.14142，过度地补偿了。反而0.05和0.1则较为接近理想的初始化W的方差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hidden layer 1 has mean 3.606589 and std 64.216258\n",
      "hidden layer 2 has mean 1.349202 and std 2.060211\n",
      "hidden layer 3 has mean 0.009690 and std 0.015233\n",
      "hidden layer 4 has mean 0.000068 and std 0.000108\n",
      "hidden layer 5 has mean 0.000001 and std 0.000002\n"
     ]
    }
   ],
   "source": [
    "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 = 5e-4\n",
    "weight_scale = 1e-3\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",
    "                }, verbose=False\n",
    "         )\n",
    "solver.train()\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()\n",
    "\n",
    "layer_means = [np.mean(H) for i,H in model.layerout.items()]\n",
    "layer_stds = [np.std(H) for i,H in model.layerout.items()]\n",
    "for i in range(0, len(layer_means)):\n",
    "    print(\"hidden layer %d has mean %f and std %f\"% (i+1, layer_means[i], layer_stds[i]))"
   ]
  },
  {
   "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 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": 29,
   "metadata": {},
   "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": {},
   "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": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.816758\n",
      "(Epoch 0 / 5) train acc: 0.120000; val_acc: 0.120000\n",
      "(Iteration 11 / 200) loss: 2.134241\n",
      "(Iteration 21 / 200) loss: 2.116729\n",
      "(Iteration 31 / 200) loss: 2.138373\n",
      "(Epoch 1 / 5) train acc: 0.276000; val_acc: 0.248000\n",
      "(Iteration 41 / 200) loss: 2.035947\n",
      "(Iteration 51 / 200) loss: 2.048115\n",
      "(Iteration 61 / 200) loss: 1.918045\n",
      "(Iteration 71 / 200) loss: 1.930706\n",
      "(Epoch 2 / 5) train acc: 0.364000; val_acc: 0.296000\n",
      "(Iteration 81 / 200) loss: 1.679049\n",
      "(Iteration 91 / 200) loss: 1.944931\n",
      "(Iteration 101 / 200) loss: 1.926890\n",
      "(Iteration 111 / 200) loss: 1.670167\n",
      "(Epoch 3 / 5) train acc: 0.381000; val_acc: 0.267000\n",
      "(Iteration 121 / 200) loss: 1.743596\n",
      "(Iteration 131 / 200) loss: 1.874660\n",
      "(Iteration 141 / 200) loss: 1.793996\n",
      "(Iteration 151 / 200) loss: 1.654038\n",
      "(Epoch 4 / 5) train acc: 0.451000; val_acc: 0.316000\n",
      "(Iteration 161 / 200) loss: 1.739581\n",
      "(Iteration 171 / 200) loss: 1.689559\n",
      "(Iteration 181 / 200) loss: 1.563626\n",
      "(Iteration 191 / 200) loss: 1.644917\n",
      "(Epoch 5 / 5) train acc: 0.450000; val_acc: 0.315000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.591695\n",
      "(Epoch 0 / 5) train acc: 0.136000; val_acc: 0.119000\n",
      "(Iteration 11 / 200) loss: 2.260409\n",
      "(Iteration 21 / 200) loss: 2.078807\n",
      "(Iteration 31 / 200) loss: 1.989002\n",
      "(Epoch 1 / 5) train acc: 0.305000; val_acc: 0.273000\n",
      "(Iteration 41 / 200) loss: 1.832667\n",
      "(Iteration 51 / 200) loss: 2.057640\n",
      "(Iteration 61 / 200) loss: 1.821623\n",
      "(Iteration 71 / 200) loss: 1.801602\n",
      "(Epoch 2 / 5) train acc: 0.376000; val_acc: 0.346000\n",
      "(Iteration 81 / 200) loss: 1.648802\n",
      "(Iteration 91 / 200) loss: 1.626869\n",
      "(Iteration 101 / 200) loss: 1.566771\n",
      "(Iteration 111 / 200) loss: 1.621779\n",
      "(Epoch 3 / 5) train acc: 0.423000; val_acc: 0.319000\n",
      "(Iteration 121 / 200) loss: 1.603200\n",
      "(Iteration 131 / 200) loss: 1.751761\n",
      "(Iteration 141 / 200) loss: 1.436574\n",
      "(Iteration 151 / 200) loss: 1.577707\n",
      "(Epoch 4 / 5) train acc: 0.469000; val_acc: 0.346000\n",
      "(Iteration 161 / 200) loss: 1.560670\n",
      "(Iteration 171 / 200) loss: 1.397601\n",
      "(Iteration 181 / 200) loss: 1.283742\n",
      "(Iteration 191 / 200) loss: 1.428619\n",
      "(Epoch 5 / 5) train acc: 0.532000; val_acc: 0.372000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xuhaomin/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "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 list(solvers.items()):\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",
    "**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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-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": 35,
   "metadata": {},
   "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": {},
   "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": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "\n",
      "running with  rmsprop\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xuhaomin/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "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=False)\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 list(solvers.items()):\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": [
    "## Inline Question 3:\n",
    "\n",
    "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n",
    "\n",
    "```\n",
    "cache += dw**2\n",
    "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n",
    "```\n",
    "\n",
    "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "\n",
    "After training for a long time, the catch becomes larger monotonically and the updates decay to zero.\n",
    "\n",
    "Adam will not have this problem. The beta1 and beta2 are the decay rates. Adam still modulates the learning rate of each weight based on the magnitudes of its gradients, but the updates do not get monotonically smaller."
   ]
  },
  {
   "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": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 17.395138\n",
      "(Epoch 0 / 10) train acc: 0.102000; val_acc: 0.117000\n",
      "(Epoch 1 / 10) train acc: 0.446000; val_acc: 0.430000\n",
      "(Iteration 501 / 4900) loss: 1.355509\n",
      "(Epoch 2 / 10) train acc: 0.502000; val_acc: 0.460000\n",
      "(Iteration 1001 / 4900) loss: 1.385160\n",
      "(Epoch 3 / 10) train acc: 0.527000; val_acc: 0.491000\n",
      "(Iteration 1501 / 4900) loss: 1.317855\n",
      "(Epoch 4 / 10) train acc: 0.545000; val_acc: 0.498000\n",
      "(Iteration 2001 / 4900) loss: 1.270969\n",
      "(Epoch 5 / 10) train acc: 0.574000; val_acc: 0.518000\n",
      "(Iteration 2501 / 4900) loss: 1.212078\n",
      "(Epoch 6 / 10) train acc: 0.624000; val_acc: 0.517000\n",
      "(Iteration 3001 / 4900) loss: 1.038487\n",
      "(Epoch 7 / 10) train acc: 0.656000; val_acc: 0.525000\n",
      "(Iteration 3501 / 4900) loss: 0.934803\n",
      "(Epoch 8 / 10) train acc: 0.701000; val_acc: 0.511000\n",
      "(Iteration 4001 / 4900) loss: 0.845222\n",
      "(Epoch 9 / 10) train acc: 0.710000; val_acc: 0.531000\n",
      "(Iteration 4501 / 4900) loss: 1.106579\n",
      "(Epoch 10 / 10) train acc: 0.753000; val_acc: 0.527000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = None\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",
    "model = FullyConnectedNet([500, 400, 300, 200, 100], weight_scale=5e-2)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "              num_epochs=10, batch_size=100,\n",
    "              update_rule='adam',\n",
    "              optim_config={\n",
    "                'learning_rate': 1e-3\n",
    "              },\n",
    "              lr_decay=0.9,\n",
    "              print_every=500,\n",
    "              verbose=True)\n",
    "solver.train()\n",
    "best_model = model\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history)\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, label='train')\n",
    "plt.plot(solver.val_acc_history, label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Clasification accuracy')\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test your 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": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.531\n",
      "Test set accuracy:  0.512\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())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}