2-Try-Trainer-class_en.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the Trainer feature\n",
    "\n",
    "Chainer has a feature called `Trainer` that can often be used to simplify the process of training and evaluating a model. This feature supports training a model in a way such that the user is not required to explicitly write the code for the training loop. For many types of models, including our MNIST model, `Trainer` allows us to write our training and evaluation code much more concisely.\n",
    "\n",
    "Chainer contains several extensions that can be used with `Trainer` to visualize your results, evaluate your model, store and manage log files more easily.\n",
    "\n",
    "This example will show how to use the `Trainer` feature to train a fully-connected feed-forward neural network on the MNIST dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare the dataset\n",
    "\n",
    "Load the MNIST dataset, as in the previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from chainer.datasets import mnist\n",
    "\n",
    "train, test = mnist.get_mnist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Prepare the dataset iterators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from chainer import iterators\n",
    "\n",
    "batchsize = 128\n",
    "\n",
    "train_iter = iterators.SerialIterator(train, batchsize)\n",
    "test_iter = iterators.SerialIterator(test, batchsize, False, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Prepare the Model\n",
    "\n",
    "We use the same model as before. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import chainer\n",
    "import chainer.links as L\n",
    "import chainer.functions as F\n",
    "\n",
    "class MLP(chainer.Chain):\n",
    "\n",
    "    def __init__(self, n_mid_units=100, n_out=10):\n",
    "        super(MLP, self).__init__()\n",
    "        with self.init_scope():\n",
    "            self.l1=L.Linear(None, n_mid_units)\n",
    "            self.l2=L.Linear(None, n_mid_units)\n",
    "            self.l3=L.Linear(None, n_out)\n",
    "\n",
    "    def __call__(self, x):\n",
    "        h1 = F.relu(self.l1(x))\n",
    "        h2 = F.relu(self.l2(h1))\n",
    "        return self.l3(h2)\n",
    "\n",
    "gpu_id = 0  # Set to -1 if you don't have a GPU\n",
    "\n",
    "model = MLP()\n",
    "if gpu_id >= 0:\n",
    "    model.to_gpu(gpu_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Prepare the Updater\n",
    "\n",
    "As mentioned above, the trainer object (instance of `Trainer`) actually implements the training loop for us. However, before we can use it, we must first prepare another object that will actually perform one iteration of training operations and pass it to the trainer. This other object will be an subclass of `Updater` such as `StandardUpdater` or a custom subclass. It will therefore need to hold all of the components that are needed in order to perform one iteration of training, such as a dataset iterator, and optimizer that also holds the model.\n",
    "\n",
    "- Updater\n",
    "    - Iterator\n",
    "        - Dataset\n",
    "    - Optimizer\n",
    "        - Model\n",
    "\n",
    "Since we can also write customized updaters, they can perform any kinds of computations. However, for this example we will use the `StandardUpdater` which performs the following steps each time it is called:\n",
    "\n",
    "1. Retrieve a batch of data from a dataset using the iterator that we supplied.\n",
    "2. Feed the batch into the model and calculate the loss using the optimizer that we supplied. Since we will supply the model to the optimizer first, this means that the updater can also access the model from the optimizer.\n",
    "3. Update the model parameters using the optimizer that we supplied.\n",
    "\n",
    "Once the updater has been set up, we can pass it to the trainer and start the training process. The trainer will then automatically create the training loop and call the updater once per iteration.\n",
    "\n",
    "Now let's create the updater object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from chainer import optimizers\n",
    "from chainer import training\n",
    "\n",
    "max_epoch = 10\n",
    "\n",
    "# Note: L.Classifier is actually a chain that wraps 'model' to add a loss\n",
    "# function.\n",
    "# Since we do not specify a loss funciton here, the default \n",
    "# 'softmax_cross_entropy' is used.\n",
    "# The output of this modified 'model' will now be a loss value instead\n",
    "# of a class label prediction.\n",
    "model = L.Classifier(model)\n",
    "\n",
    "# Send the model to the GPU.\n",
    "if gpu_id >= 0:\n",
    "    model.to_gpu(gpu_id)\n",
    "\n",
    "# selection of your optimizing method\n",
    "optimizer = optimizers.SGD()\n",
    "# Give the optimizer a reference to the model\n",
    "optimizer.setup(model)\n",
    "\n",
    "# Get an Updater that uses the Iterator and Optimizer\n",
    "updater = training.StandardUpdater(train_iter, optimizer, device=gpu_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE\n",
    "\n",
    "The `L.Classifier` object is actually a chain that places our `model` in its `predictor` attribute. This modifies `model` so that when it is called, it will now take both the input image batch and a class label batch as inputs to its `__call__` method and output a `Variable` that contains the loss value. The loss function to use can be optionally set but we use the default which is `softmax_cross_entropy`.\n",
    "\n",
    "Specifically, when `__call__` is called on our model (which is now a `L.Classifier` object), the image batch is supplied to its `predictor` attribute, which contains our `MLP` model. The ourput of the `MLP` (which consists of class label predictions) is supplied to the loss function along with the target class labels to compute the loss value which is then returned as a `Variable` object.\n",
    "\n",
    "Note that we use `StandardUpdater`, which is the simplest type of `Updater` in Chainer. There are also other types of updaters available, such as `ParallelUpdater` (which is intended for multiple GPUs) and you can also write a custom updater if you wish."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Setup Trainer\n",
    "\n",
    "Now that we have set up an updater, we can pass it to a `Trainer` object. You can optionally pass a `stop_trigger` to the second trainer argument as a tuple, `(length, unit)` to tell the trainer stop automatically according to your indicated timing. The `length` is given as an arbitrary integer and `unit` is given as a string which currently must be either `epoch` or `iteration`. Without setting `stop_trigger`, the training will not stop automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Send Updater to Trainer\n",
    "trainer = training.Trainer(updater, (max_epoch, 'epoch'),\n",
    "                           out='mnist_result')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `out` argument in the trainer will set up an output directory to save the logfiles, the image files of graphs to show the time progress of loss, accuracy, etc. \n",
    "\n",
    "Next, we will explain how to display/save those outputs by using `extensions`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Add extensions to trainer\n",
    "\n",
    "There are several optional trainer `extensions` that provide the following capabilites: \n",
    "\n",
    "- Save log files automatically (`LogReport`)\n",
    "- Display the training information to the terminal periodically (`PrintReport`)\n",
    "- Visualize the loss progress by plottig a graph periodically and save its image (`PlotReport`)\n",
    "- Automatically serialize the model or the state of Optimizer periodically (`snapshot`/`snapshot_object`)\n",
    "- Display Progress Bar to show the progress of training (`ProgressBar`)\n",
    "- Save the model architecture as a dot format readable by `Graphviz` (`dump_graph`)\n",
    "\n",
    "Now you can utilize the wide variety of tools shown above right away! To do so, simply pass the desired `extensions` object to the `Trainer` object by using the `extend()` method of `Trainer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from chainer.training import extensions\n",
    "\n",
    "trainer.extend(extensions.LogReport())\n",
    "trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'main/accuracy', 'validation/main/loss', 'validation/main/accuracy', 'elapsed_time']))\n",
    "trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss'], x_key='epoch', file_name='loss.png'))\n",
    "trainer.extend(extensions.PlotReport(['main/accuracy', 'validation/main/accuracy'], x_key='epoch', file_name='accuracy.png'))\n",
    "trainer.extend(extensions.snapshot(filename='snapshot_epoch-{.updater.epoch}'))\n",
    "trainer.extend(extensions.snapshot_object(model.predictor, filename='model_epoch-{.updater.epoch}'))\n",
    "trainer.extend(extensions.Evaluator(test_iter, model, device=gpu_id))\n",
    "trainer.extend(extensions.dump_graph('main/loss'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `LogReport`\n",
    "\n",
    "Collect `loss` and `accuracy` automatically every `epoch` or `iteration` and store the information under the `log` file in the directory assigned by the `out` argument of `Trainer`.\n",
    "\n",
    "### `PrintReport`\n",
    "\n",
    "`Reporter` aggregates the results to output to the standard output. The timing for displaying the output can be given by the list. \n",
    "\n",
    "### `PlotReport`\n",
    "\n",
    "`PlotReport` plots the values specified by its arguments, draws the graph and saves the image in the directory set by 'file name'.\n",
    "\n",
    "### `snapshot`\n",
    "\n",
    "The `snapshot` method saves the `Trainer` object at the designated timing (defaut: every epoch) in the directory assigned by `out` argument in `Trainer`. The `Trainer` object, as mentioned before, has an `Updater` which contains an `Optimizer` and a model inside. Therefore, as long as you have the snapshot file, you can use it to come back to the training or make inferences using the previously trained model later. \n",
    "\n",
    "### `snapshot_object`\n",
    "\n",
    "When you save the whole `Trainer` object, in some cases it is very tedious to retrieve only the inside of the model. By using `snapshot_object`, you can save the particular object (in this case, the model wrapped by `Classifier`) in addition to saving the `Trainer` object. `Classifier` is a `Chain` object that keeps the `Chain` object given by the first argument as a property called `predictor` and calculates the loss. `Classifier` doesn't have any parameters other than those inside its predictor model, and so we only save `model.predictor`. \n",
    "\n",
    "### `Evaluator`\n",
    "\n",
    "The `Iterator` that uses the evaluation dataset (such as a validation or test dataset) and the model object are passed to `Evaluator`. The `Evaluator` evaluates the model using the given dataset at the specified timing interval. \n",
    "\n",
    "### `dump_graph`\n",
    "\n",
    "This method saves the computational graph of the model. The graph is saved in `Graphviz` dot format. The output location (directory) to save the graph is set by the `out` argument of `Trainer`. \n",
    "\n",
    "---\n",
    "\n",
    "The `extensions` class has a lot of options other than those mentioned here. For instance, by using the `trigger` option, you can set individual timings to activate the `extensions` more flexibly. Please take a look at the official document in more detail：[Trainer extensions](http://docs.chainer.org/en/latest/reference/extensions.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Start Training\n",
    "\n",
    "To start training, just call `run` method from `Trainer` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch       main/loss   main/accuracy  validation/main/loss  validation/main/accuracy  elapsed_time\n",
      "\u001b[J1           1.58028     0.592451       0.795149              0.820016                  3.08654       \n",
      "\u001b[J2           0.601956    0.849913       0.4602                0.878263                  5.86635       \n",
      "\u001b[J3           0.427225    0.884295       0.371159              0.897449                  9.37594       \n",
      "\u001b[J4           0.36528     0.89792        0.331084              0.905558                  12.5266       \n",
      "\u001b[J5           0.332345    0.905917       0.305378              0.913865                  15.9814       \n",
      "\u001b[J6           0.309809    0.911147       0.287677              0.918018                  19.5191       \n",
      "\u001b[J7           0.292386    0.915878       0.273254              0.921183                  22.5609       \n",
      "\u001b[J8           0.278587    0.920239       0.261446              0.923754                  25.8911       \n",
      "\u001b[J9           0.266574    0.923024       0.25066               0.92771                   29.1613       \n",
      "\u001b[J10          0.256307    0.926023       0.242027              0.929589                  32.2064       \n"
     ]
    }
   ],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the graph of loss saved in the `mnist_result` directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lL6vuZGRkWNdff70VHh5uPfPMM9aECROsqKgoq2HDhl6r7pw9e9a67rrrrAoV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\nIYQQQggh3ND/B1o2n8mFVWrvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='mnist_result/loss.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the accuracy? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jjtaO1K7uyC+natO7qxMa9wds+HkDE9tMxNXiZb7aUJFxYypSu0bhCYiuzMrC6rkXFMxN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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='mnist_result/accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, let's visualize the computational graph output by `dump_graph` of `extensions` using `Graphviz`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "dot -Tpng mnist_result/cg.dot -o mnist_result/cg.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6YrAJx61UKhXS09N1kpCYmBg0NzfDxsZGSD48PDzg7+8Pa2trsUMmEo32A442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\nAAB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FioyMVPPmzTV+/HgNHDiw0I5bAQCgAKFwAMXBoUOH9PHHHyswMFCVKlXS0KFDNWDA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\nDgEAAACgSNr3/wCbw7VUoeiqxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='mnist_result/cg.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the top to the bottom, you can track the data flow of the computations, how data and parameters are passed to what type of `Function` and the calculated loss is output. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Evaluate a pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8NbtLJqR9fHpaJMQ+EyJnR3ghtpGw7XjhJ92qxLdkSXBSbn6YJhLeBvApAL8C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\nCGkRijAhhLQIRZgQQlqEIkwIIS3y/wFYJOCHHvLvRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43d50b5048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: 7\n",
      "predicted_label: 7\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from chainer import serializers\n",
    "from chainer.cuda import to_gpu\n",
    "from chainer.cuda import to_cpu\n",
    "\n",
    "model = MLP()\n",
    "serializers.load_npz('mnist_result/model_epoch-10', model)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x, t = test[0]\n",
    "plt.imshow(x.reshape(28, 28), cmap='gray')\n",
    "plt.show()\n",
    "print('label:', t)\n",
    "\n",
    "if gpu_id >= 0:\n",
    "    model.to_gpu(gpu_id)\n",
    "    x = to_gpu(x[None, ...])\n",
    "    y = model(x)\n",
    "    y = to_cpu(y.data)\n",
    "else:    \n",
    "    x = x[None, ...]\n",
    "    y = model(x)\n",
    "    y = y.data\n",
    "\n",
    "print('predicted_label:', y.argmax(axis=1)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It successfully executed !!"
   ]
  }
 ],
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