ProtoNet-MiniImageNet-v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import glob\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv_block(inputs, out_channels, name='conv'):\n",
    "    with tf.variable_scope(name):\n",
    "        conv = tf.layers.conv2d(inputs, out_channels, kernel_size=3, padding='SAME')\n",
    "        conv = tf.contrib.layers.batch_norm(conv, updates_collections=None, decay=0.99, scale=True, center=True)\n",
    "        conv = tf.nn.relu(conv)\n",
    "        conv = tf.contrib.layers.max_pool2d(conv, 2)\n",
    "        return conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def encoder(x, h_dim, z_dim, reuse=False):\n",
    "    with tf.variable_scope('encoder', reuse=reuse):\n",
    "        net = conv_block(x, h_dim, name='conv_1')\n",
    "        net = conv_block(net, h_dim, name='conv_2')\n",
    "        net = conv_block(net, h_dim, name='conv_3')\n",
    "        net = conv_block(net, z_dim, name='conv_4')\n",
    "        net = tf.contrib.layers.flatten(net)\n",
    "        return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def euclidean_distance(a, b):\n",
    "    # a.shape = N x D\n",
    "    # b.shape = M x D\n",
    "    N, D = tf.shape(a)[0], tf.shape(a)[1]\n",
    "    M = tf.shape(b)[0]\n",
    "    a = tf.tile(tf.expand_dims(a, axis=1), (1, M, 1))\n",
    "    b = tf.tile(tf.expand_dims(b, axis=0), (N, 1, 1))\n",
    "    return tf.reduce_mean(tf.square(a - b), axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_epochs = 100\n",
    "n_episodes = 100\n",
    "n_way = 20\n",
    "n_shot = 5\n",
    "n_query = 15\n",
    "n_examples = 350\n",
    "im_width, im_height, channels = 84, 84, 3\n",
    "h_dim = 64\n",
    "z_dim = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 350, 84, 84, 3)\n"
     ]
    }
   ],
   "source": [
    "# Load Train Dataset\n",
    "train_dataset = np.load('mini-imagenet-train.npy')\n",
    "n_classes = train_dataset.shape[0]\n",
    "print(train_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, None, im_height, im_width, channels])\n",
    "q = tf.placeholder(tf.float32, [None, None, im_height, im_width, channels])\n",
    "x_shape = tf.shape(x)\n",
    "q_shape = tf.shape(q)\n",
    "num_classes, num_support = x_shape[0], x_shape[1]\n",
    "num_queries = q_shape[1]\n",
    "y = tf.placeholder(tf.int64, [None, None])\n",
    "y_one_hot = tf.one_hot(y, depth=num_classes)\n",
    "emb_in = encoder(tf.reshape(x, [num_classes * num_support, im_height, im_width, channels]), h_dim, z_dim)\n",
    "emb_dim = tf.shape(emb_in)[-1]\n",
    "emb_x = tf.reduce_mean(tf.reshape(emb_in, [num_classes, num_support, emb_dim]), axis=1)\n",
    "emb_q = encoder(tf.reshape(q, [num_classes * num_queries, im_height, im_width, channels]), h_dim, z_dim, reuse=True)\n",
    "dists = euclidean_distance(emb_q, emb_x)\n",
    "log_p_y = tf.reshape(tf.nn.log_softmax(-dists), [num_classes, num_queries, -1])\n",
    "ce_loss = -tf.reduce_mean(tf.reshape(tf.reduce_sum(tf.multiply(y_one_hot, log_p_y), axis=-1), [-1]))\n",
    "acc = tf.reduce_mean(tf.to_float(tf.equal(tf.argmax(log_p_y, axis=-1), y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_op = tf.train.AdamOptimizer().minimize(ce_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[epoch 1/100, episode 50/100] => loss: 2.69017, acc: 0.20667\n",
      "[epoch 1/100, episode 100/100] => loss: 2.62193, acc: 0.22000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[epoch 67/100, episode 50/100] => loss: 0.88438, acc: 0.76333\n",
      "[epoch 67/100, episode 100/100] => loss: 0.86021, acc: 0.70000\n",
      "[epoch 68/100, episode 50/100] => loss: 0.90512, acc: 0.71333\n",
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      "[epoch 90/100, episode 50/100] => loss: 0.91102, acc: 0.69333\n",
      "[epoch 90/100, episode 100/100] => loss: 0.55505, acc: 0.80000\n",
      "[epoch 91/100, episode 50/100] => loss: 0.62219, acc: 0.80333\n",
      "[epoch 91/100, episode 100/100] => loss: 0.78228, acc: 0.74667\n",
      "[epoch 92/100, episode 50/100] => loss: 0.66140, acc: 0.74667\n",
      "[epoch 92/100, episode 100/100] => loss: 0.81554, acc: 0.72000\n",
      "[epoch 93/100, episode 50/100] => loss: 0.76939, acc: 0.74000\n",
      "[epoch 93/100, episode 100/100] => loss: 0.75672, acc: 0.74333\n",
      "[epoch 94/100, episode 50/100] => loss: 0.81159, acc: 0.71000\n",
      "[epoch 94/100, episode 100/100] => loss: 0.80201, acc: 0.72667\n",
      "[epoch 95/100, episode 50/100] => loss: 0.86675, acc: 0.74333\n",
      "[epoch 95/100, episode 100/100] => loss: 0.60115, acc: 0.79333\n",
      "[epoch 96/100, episode 50/100] => loss: 0.75666, acc: 0.73667\n",
      "[epoch 96/100, episode 100/100] => loss: 0.80603, acc: 0.73667\n",
      "[epoch 97/100, episode 50/100] => loss: 0.79783, acc: 0.72333\n",
      "[epoch 97/100, episode 100/100] => loss: 0.84759, acc: 0.71333\n",
      "[epoch 98/100, episode 50/100] => loss: 0.73558, acc: 0.73333\n",
      "[epoch 98/100, episode 100/100] => loss: 0.74539, acc: 0.74333\n",
      "[epoch 99/100, episode 50/100] => loss: 0.90639, acc: 0.68667\n",
      "[epoch 99/100, episode 100/100] => loss: 0.56291, acc: 0.80333\n",
      "[epoch 100/100, episode 50/100] => loss: 0.71471, acc: 0.76667\n",
      "[epoch 100/100, episode 100/100] => loss: 0.70881, acc: 0.76333\n"
     ]
    }
   ],
   "source": [
    "for ep in range(n_epochs):\n",
    "    for epi in range(n_episodes):\n",
    "        epi_classes = np.random.permutation(n_classes)[:n_way]\n",
    "        support = np.zeros([n_way, n_shot, im_height, im_width, channels], dtype=np.float32)\n",
    "        query = np.zeros([n_way, n_query, im_height, im_width, channels], dtype=np.float32)\n",
    "        for i, epi_cls in enumerate(epi_classes):\n",
    "            selected = np.random.permutation(n_examples)[:n_shot + n_query]\n",
    "            support[i] = train_dataset[epi_cls, selected[:n_shot]]\n",
    "            query[i] = train_dataset[epi_cls, selected[n_shot:]]\n",
    "        # support = np.expand_dims(support, axis=-1)\n",
    "        # query = np.expand_dims(query, axis=-1)\n",
    "        labels = np.tile(np.arange(n_way)[:, np.newaxis], (1, n_query)).astype(np.uint8)\n",
    "        _, ls, ac = sess.run([train_op, ce_loss, acc], feed_dict={x: support, q: query, y:labels})\n",
    "        if (epi+1) % 50 == 0:\n",
    "            print('[epoch {}/{}, episode {}/{}] => loss: {:.5f}, acc: {:.5f}'.format(ep+1, n_epochs, epi+1, n_episodes, ls, ac))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 350, 84, 84, 3)\n"
     ]
    }
   ],
   "source": [
    "# Load Test Dataset\n",
    "test_dataset = np.load('mini-imagenet-test.npy')\n",
    "n_test_classes = test_dataset.shape[0]\n",
    "print(test_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_test_episodes = 600\n",
    "n_test_way = 5\n",
    "n_test_shot = 5\n",
    "n_test_query = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing...\n",
      "[test episode 50/600] => loss: 1.05772, acc: 0.58667\n",
      "[test episode 100/600] => loss: 1.33291, acc: 0.48000\n",
      "[test episode 150/600] => loss: 0.58527, acc: 0.73333\n",
      "[test episode 200/600] => loss: 0.65924, acc: 0.69333\n",
      "[test episode 250/600] => loss: 1.12209, acc: 0.52000\n",
      "[test episode 300/600] => loss: 0.97975, acc: 0.68000\n",
      "[test episode 350/600] => loss: 1.05668, acc: 0.65333\n",
      "[test episode 400/600] => loss: 0.98864, acc: 0.68000\n",
      "[test episode 450/600] => loss: 1.04565, acc: 0.65333\n",
      "[test episode 500/600] => loss: 0.72200, acc: 0.72000\n",
      "[test episode 550/600] => loss: 0.77793, acc: 0.77333\n",
      "[test episode 600/600] => loss: 0.94674, acc: 0.62667\n",
      "Average Test Accuracy: 0.61807\n"
     ]
    }
   ],
   "source": [
    "print('Testing...')\n",
    "avg_acc = 0.\n",
    "for epi in range(n_test_episodes):\n",
    "    epi_classes = np.random.permutation(n_test_classes)[:n_test_way]\n",
    "    support = np.zeros([n_test_way, n_test_shot, im_height, im_width, channels], dtype=np.float32)\n",
    "    query = np.zeros([n_test_way, n_test_query, im_height, im_width, channels], dtype=np.float32)\n",
    "    for i, epi_cls in enumerate(epi_classes):\n",
    "        selected = np.random.permutation(n_examples)[:n_test_shot + n_test_query]\n",
    "        support[i] = test_dataset[epi_cls, selected[:n_test_shot]]\n",
    "        query[i] = test_dataset[epi_cls, selected[n_test_shot:]]\n",
    "    # support = np.expand_dims(support, axis=-1)\n",
    "    # query = np.expand_dims(query, axis=-1)\n",
    "    labels = np.tile(np.arange(n_test_way)[:, np.newaxis], (1, n_test_query)).astype(np.uint8)\n",
    "    ls, ac = sess.run([ce_loss, acc], feed_dict={x: support, q: query, y:labels})\n",
    "    avg_acc += ac\n",
    "    if (epi+1) % 50 == 0:\n",
    "        print('[test episode {}/{}] => loss: {:.5f}, acc: {:.5f}'.format(epi+1, n_test_episodes, ls, ac))\n",
    "avg_acc /= n_test_episodes\n",
    "print('Average Test Accuracy: {:.5f}'.format(avg_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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