notebooks-4/3-rnn-gluon.ipynb

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   "source": [
    "# Concise Implementation of Recurrent Neural Networks"
   ]
  },
  {
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   "execution_count": 1,
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     "end_time": "2019-07-03T23:01:16.582933Z",
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   "source": [
    "import d2l\n",
    "import math\n",
    "from mxnet import gluon, init, np, npx\n",
    "from mxnet.gluon import nn, rnn\n",
    "npx.set_np()\n",
    "\n",
    "batch_size, num_steps = 32, 35\n",
    "train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "Creating a RNN layer with 256 hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "end_time": "2019-07-03T23:01:16.591409Z",
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   "source": [
    "rnn_layer = rnn.RNN(256)\n",
    "rnn_layer.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "Initializing the hidden state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "(1, (1, 1, 256))"
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     "execution_count": 3,
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   "source": [
    "state = rnn_layer.begin_state(batch_size=1)\n",
    "len(state), state[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Defining a class to wrap the RNN layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "class RNNModel(nn.Block):\n",
    "    def __init__(self, rnn_layer, vocab_size, **kwargs):\n",
    "        super(RNNModel, self).__init__(**kwargs)\n",
    "        self.rnn = rnn_layer\n",
    "        self.vocab_size = vocab_size\n",
    "        self.dense = nn.Dense(vocab_size)\n",
    "\n",
    "    def forward(self, inputs, state):\n",
    "        X = npx.one_hot(inputs.T, self.vocab_size)\n",
    "        Y, state = self.rnn(X, state)\n",
    "        # The fully connected layer will first change the shape of Y to\n",
    "        # (num_steps * batch_size, num_hiddens)\n",
    "        # Its output shape is (num_steps * batch_size, vocab_size)\n",
    "        output = self.dense(Y.reshape((-1, Y.shape[-1])))\n",
    "        return output, state\n",
    "\n",
    "    def begin_state(self, *args, **kwargs):\n",
    "        return self.rnn.begin_state(*args, **kwargs)"
   ]
  },
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   "source": [
    "Training"
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  {
   "cell_type": "code",
   "execution_count": 5,
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      "Perplexity 1.2, 158013 tokens/sec on gpu(0)\n",
      "time traveller  you can show black is white by argument said fil\n",
      "traveller after the pauserequired for the little go the geo\n"
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   "source": [
    "num_epochs, lr, ctx = 500, 1, d2l.try_gpu()\n",
    "model = RNNModel(rnn_layer, len(vocab))\n",
    "model.initialize(force_reinit=True, ctx=ctx)\n",
    "d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, ctx)"
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