BatchNormalization.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was proposed by [1].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the deeplearning directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\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 deeplearning.classifiers.fc_net import *\n",
    "from deeplearning.data_utils import get_CIFAR10_data\n",
    "from deeplearning.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from deeplearning.solver import Solver\n",
    "import random \n",
    "import torch\n",
    "seed = 7\n",
    "torch.manual_seed(seed)\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning/datasets/cifar-10-batches-py/data_batch_1\n",
      "deeplearning/datasets/cifar-10-batches-py/data_batch_2\n",
      "deeplearning/datasets/cifar-10-batches-py/data_batch_3\n",
      "deeplearning/datasets/cifar-10-batches-py/data_batch_4\n",
      "deeplearning/datasets/cifar-10-batches-py/data_batch_5\n",
      "deeplearning/datasets/cifar-10-batches-py/test_batch\n",
      "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 data.items():\n",
    "    print ('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `deeplearning/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [-32.66672903  46.34189958 -10.73722359]\n",
      "  stds:  [28.59998266 29.27625648 30.1092915 ]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [ 8.88178420e-18 -4.78506124e-16  1.40998324e-16]\n",
      "  std:  [0.99999999 0.99999999 0.99999999]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:  [0.99999999 1.99999999 2.99999998]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print ('Before batch normalization:')\n",
    "print ('  means: ', a.mean(axis=0))\n",
    "print ('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print ('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print ('  mean: ', a_norm.mean(axis=0))\n",
    "print ('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print ('After batch normalization (nontrivial gamma, beta)')\n",
    "print ('  means: ', a_norm.mean(axis=0))\n",
    "print ('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.05537581 -0.09695608  0.00107002]\n",
      "  stds:  [1.03868853 1.04585392 0.99462474]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "    X = np.random.randn(N, D1)\n",
    "    a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "    batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print ('After batch normalization (test-time):')\n",
    "print ('  means: ', a_norm.mean(axis=0))\n",
    "print ('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  8.277227185180498e-09\n",
      "dgamma error:  1.8892345318193607e-11\n",
      "dbeta error:  1.213332521508922e-11\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print ('dx error: ', rel_error(dx_num, dx))\n",
    "print ('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print ('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: You can still complete the rest of the assignment if you don't figure this part out, so don't worry too much if you can't get it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  7.652470371139525e-12\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.97x\n"
     ]
    }
   ],
   "source": [
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "# repeat backwards passes multiple times for stability\n",
    "for r in range(1000):\n",
    "    dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "for r in range(1000):\n",
    "    dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print ('dx difference: ', rel_error(dx1, dx2))\n",
    "print ('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print ('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print ('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `deeplearning/layer_utils.py`. If you decide to do so, do it in the file `deeplearning/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial predictions error: 5.00e-08\n",
      "Running check with reg =  0\n",
      "Initial loss:  2.4919233642920133\n",
      "W1 relative error: 6.17e-05\n",
      "W2 relative error: 2.14e-06\n",
      "W3 relative error: 3.26e-10\n",
      "b1 relative error: 3.42e-07\n",
      "b2 relative error: 4.44e-08\n",
      "b3 relative error: 1.11e-10\n",
      "beta1 relative error: 5.44e-09\n",
      "beta2 relative error: 3.91e-09\n",
      "gamma1 relative error: 5.41e-09\n",
      "gamma2 relative error: 3.46e-09\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.102039719123686\n",
      "W1 relative error: 1.95e-05\n",
      "W2 relative error: 3.90e-06\n",
      "W3 relative error: 4.57e-08\n",
      "b1 relative error: 5.55e-08\n",
      "b2 relative error: 8.88e-08\n",
      "b3 relative error: 3.00e-10\n",
      "beta1 relative error: 1.44e-08\n",
      "beta2 relative error: 3.50e-07\n",
      "gamma1 relative error: 2.59e-08\n",
      "gamma2 relative error: 1.08e-07\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "\n",
    "model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=0, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H1).reshape(D, H1)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H1)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H1*H2).reshape(H1, H2)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=H2)\n",
    "model.params['W3'] = np.linspace(-0.3, 0.4, num=H2*C).reshape(H2, C)\n",
    "model.params['b3'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "\n",
    "expected_bn_forward_output = np.array([[0.28397701, 0.46532063, 0.64666426, 0.82800788, 1.0093515, 1.19069513,\n",
    "                                            1.37203875, 1.55338238,  1.734726, 1.91606962],\n",
    "                                       [-0.9, -0.78888889, -0.67777778, -0.56666667, -0.45555556, -0.34444444, \n",
    "                                            -0.23333333, -0.12222222, -0.01111111, 0.1]])\n",
    "\n",
    "# Checks if initial forward pass is correct with batchnorm \n",
    "init_scores = model.loss(X)\n",
    "print('initial predictions error: %.2e' % rel_error(init_scores, expected_bn_forward_output))\n",
    "\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",
    "                            use_batchnorm=True)\n",
    "    \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])))\n",
    "    if reg == 0: print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.297752\n",
      "(Epoch 0 / 10) train acc: 0.124000; val_acc: 0.110000\n",
      "(Epoch 1 / 10) train acc: 0.326000; val_acc: 0.266000\n",
      "(Epoch 2 / 10) train acc: 0.436000; val_acc: 0.313000\n",
      "(Epoch 3 / 10) train acc: 0.509000; val_acc: 0.336000\n",
      "(Epoch 4 / 10) train acc: 0.590000; val_acc: 0.344000\n",
      "(Epoch 5 / 10) train acc: 0.597000; val_acc: 0.337000\n",
      "(Epoch 6 / 10) train acc: 0.641000; val_acc: 0.327000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_10849/4249014190.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m                 },\n\u001b[1;32m     22\u001b[0m                 verbose=True, print_every=200)\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mbn_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m solver = Solver(model, small_data,\n",
      "\u001b[0;32m~/cs182_hw1_solution/deeplearning/solver.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    247\u001b[0m             \u001b[0mlast_it\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_iterations\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfirst_it\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlast_it\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mepoch_end\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m                 train_acc = self.check_accuracy(self.X_train, self.y_train,\n\u001b[0m\u001b[1;32m    250\u001b[0m                                                 num_samples=1000)\n\u001b[1;32m    251\u001b[0m                 \u001b[0mval_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_accuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/cs182_hw1_solution/deeplearning/solver.py\u001b[0m in \u001b[0;36mcheck_accuracy\u001b[0;34m(self, X, y, num_samples, batch_size)\u001b[0m\n\u001b[1;32m    212\u001b[0m             \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m             \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m             \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/cs182_hw1_solution/deeplearning/classifiers/fc_net.py\u001b[0m in \u001b[0;36mloss\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_batchnorm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m                 a, cache[i] = affine_relu_bn_forward(a, self.params['W{}'.format(i)], self.params['b{}'.format(i)],\n\u001b[0m\u001b[1;32m    258\u001b[0m                                                      \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gamma{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'beta{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m                                                      self.bn_params[i])\n",
      "\u001b[0;32m~/cs182_hw1_solution/deeplearning/layer_utils.py\u001b[0m in \u001b[0;36maffine_relu_bn_forward\u001b[0;34m(x, w, b, gamma, beta, bn_params)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfc_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maffine_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbn_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatchnorm_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbn_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m     \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrelu_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrelu_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfc_cache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbn_cache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrelu_cache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/cs182_hw1_solution/deeplearning/layers.py\u001b[0m in \u001b[0;36mrelu_forward\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;31m# TODO: Implement the ReLU forward pass.                                    #\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;31m#############################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0;31m#############################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;31m#                             END OF YOUR CODE                              #\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\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",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above and record the results of the experiment. You should find that using batch normalization helps the network to converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\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()\n",
    "\n",
    "solver.record_histories_as_npz('submission_logs/compare_bn_deep_networks_no_bn.npz')\n",
    "bn_solver.record_histories_as_npz('submission_logs/compare_bn_deep_networks_with_bn.npz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cs182/cs182_hw1_solution/deeplearning/layers.py:653: RuntimeWarning: divide by zero encountered in log\n",
      "  loss = -np.sum(np.log(probs[np.arange(N), y])) / N\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\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",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print ('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for i, ws in enumerate(weight_scales):\n",
    "    best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "    bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "\n",
    "    best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "    bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "\n",
    "    final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "    bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "    \n",
    "    \n",
    "    solvers[ws].record_histories_as_npz('submission_logs/bn_and_weight_scale_experiments_scale{}_no_bn.npz'.format(i))\n",
    "    bn_solvers[ws].record_histories_as_npz('submission_logs/bn_and_weight_scale_experiments_scale{}_with_bn.npz'.format(i))\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Batchnorm improves optimization and training speed, especially with very small weight initializations."
   ]
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}