tutorial-contents-notebooks/305_batch_train.ipynb

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   "source": [
    "# 305 Batch Train\n",
    "\n",
    "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n",
    "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
    "\n",
    "Dependencies:\n",
    "* torch: 0.1.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7faffc159918>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.utils.data as Data\n",
    "\n",
    "torch.manual_seed(1)    # reproducible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 5\n",
    "# BATCH_SIZE = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = torch.linspace(1, 10, 10)       # this is x data (torch tensor)\n",
    "y = torch.linspace(10, 1, 10)       # this is y data (torch tensor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\n",
    "loader = Data.DataLoader(\n",
    "    dataset=torch_dataset,      # torch TensorDataset format\n",
    "    batch_size=BATCH_SIZE,      # mini batch size\n",
    "    shuffle=True,               # random shuffle for training\n",
    "    num_workers=2,              # subprocesses for loading data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | Step:  0 | batch x:  [ 6.  7.  2.  3.  1.] | batch y:  [  5.   4.   9.   8.  10.]\n",
      "Epoch:  0 | Step:  1 | batch x:  [  9.  10.   4.   8.   5.] | batch y:  [ 2.  1.  7.  3.  6.]\n",
      "Epoch:  1 | Step:  0 | batch x:  [  3.   4.   2.   9.  10.] | batch y:  [ 8.  7.  9.  2.  1.]\n",
      "Epoch:  1 | Step:  1 | batch x:  [ 1.  7.  8.  5.  6.] | batch y:  [ 10.   4.   3.   6.   5.]\n",
      "Epoch:  2 | Step:  0 | batch x:  [ 3.  9.  2.  6.  7.] | batch y:  [ 8.  2.  9.  5.  4.]\n",
      "Epoch:  2 | Step:  1 | batch x:  [ 10.   4.   8.   1.   5.] | batch y:  [  1.   7.   3.  10.   6.]\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(3):   # train entire dataset 3 times\n",
    "    for step, (batch_x, batch_y) in enumerate(loader):  # for each training step\n",
    "        # train your data...\n",
    "        print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\n",
    "              batch_x.numpy(), '| batch y: ', batch_y.numpy())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppose a different batch size that cannot be fully divided by the number of data entreis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | Step:  0 | batch x:  [  3.  10.   9.   4.   7.   8.   2.   1.] | batch y:  [  8.   1.   2.   7.   4.   3.   9.  10.]\n",
      "Epoch:  0 | Step:  1 | batch x:  [ 5.  6.] | batch y:  [ 6.  5.]\n",
      "Epoch:  1 | Step:  0 | batch x:  [  4.   8.   3.   2.   1.  10.   5.   6.] | batch y:  [  7.   3.   8.   9.  10.   1.   6.   5.]\n",
      "Epoch:  1 | Step:  1 | batch x:  [ 7.  9.] | batch y:  [ 4.  2.]\n",
      "Epoch:  2 | Step:  0 | batch x:  [  6.   2.   4.  10.   9.   3.   8.   5.] | batch y:  [ 5.  9.  7.  1.  2.  8.  3.  6.]\n",
      "Epoch:  2 | Step:  1 | batch x:  [ 7.  1.] | batch y:  [  4.  10.]\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 8\n",
    "loader = Data.DataLoader(\n",
    "    dataset=torch_dataset,      # torch TensorDataset format\n",
    "    batch_size=BATCH_SIZE,      # mini batch size\n",
    "    shuffle=True,               # random shuffle for training\n",
    "    num_workers=2,              # subprocesses for loading data\n",
    ")\n",
    "for epoch in range(3):   # train entire dataset 3 times\n",
    "    for step, (batch_x, batch_y) in enumerate(loader):  # for each training step\n",
    "        # train your data...\n",
    "        print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\n",
    "              batch_x.numpy(), '| batch y: ', batch_y.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  }
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