notebooks-2/7-googlenet.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Inception "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.580708Z",
     "start_time": "2019-07-07T06:08:42.002576Z"
    }
   },
   "outputs": [],
   "source": [
    "import d2l\n",
    "from mxnet import gluon, np, npx\n",
    "from mxnet.gluon import nn\n",
    "npx.set_np()\n",
    "\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=128, resize=96)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The inception block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.592275Z",
     "start_time": "2019-07-07T06:08:45.583648Z"
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    "attributes": {
     "classes": [],
     "id": "",
     "n": "1"
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    "slideshow": {
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   "outputs": [],
   "source": [
    "class Inception(nn.Block):\n",
    "    # c1 - c4 are the number of output channels for each layer in the path.\n",
    "    def __init__(self, c1, c2, c3, c4, **kwargs):\n",
    "        super(Inception, self).__init__(**kwargs)\n",
    "        # Path 1 is a single 1 x 1 convolutional layer.\n",
    "        self.p1_1 = nn.Conv2D(c1, kernel_size=1, activation='relu')\n",
    "        # Path 2 is a 1 x 1 convolutional layer followed by a 3 x 3 convolutional layer.\n",
    "        self.p2_1 = nn.Conv2D(c2[0], kernel_size=1, activation='relu')\n",
    "        self.p2_2 = nn.Conv2D(c2[1], kernel_size=3, padding=1, activation='relu')\n",
    "        # Path 3 is a 1 x 1 convolutional layer followed by a 5 x 5 convolutional layer.\n",
    "        self.p3_1 = nn.Conv2D(c3[0], kernel_size=1, activation='relu')\n",
    "        self.p3_2 = nn.Conv2D(c3[1], kernel_size=5, padding=2, activation='relu')\n",
    "        # Path 4 is a 3 x 3 maximum pooling layer followed by a 1 x 1 convolutional layer.\n",
    "        self.p4_1 = nn.MaxPool2D(pool_size=3, strides=1, padding=1)\n",
    "        self.p4_2 = nn.Conv2D(c4, kernel_size=1, activation='relu')\n",
    "    def forward(self, x):\n",
    "        p1 = self.p1_1(x)\n",
    "        p2 = self.p2_2(self.p2_1(x))\n",
    "        p3 = self.p3_2(self.p3_1(x))\n",
    "        p4 = self.p4_2(self.p4_1(x))\n",
    "        # Concatenate the outputs on the channel dimension.\n",
    "        return np.concatenate((p1, p2, p3, p4), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Inception Model - Stage 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.601078Z",
     "start_time": "2019-07-07T06:08:45.593944Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
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   },
   "outputs": [],
   "source": [
    "b1 = nn.Sequential()\n",
    "b1.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3, activation='relu'),\n",
    "       nn.MaxPool2D(pool_size=3, strides=2, padding=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inception Model - Stage 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.606738Z",
     "start_time": "2019-07-07T06:08:45.602846Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "3"
    }
   },
   "outputs": [],
   "source": [
    "b2 = nn.Sequential()\n",
    "b2.add(nn.Conv2D(64, kernel_size=1),\n",
    "       nn.Conv2D(192, kernel_size=3, padding=1),\n",
    "       nn.MaxPool2D(pool_size=3, strides=2, padding=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Inception Model - Stage 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.615934Z",
     "start_time": "2019-07-07T06:08:45.608077Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "4"
    }
   },
   "outputs": [],
   "source": [
    "b3 = nn.Sequential()\n",
    "b3.add(Inception(64, (96, 128), (16, 32), 32),\n",
    "       Inception(128, (128, 192), (32, 96), 64),\n",
    "       nn.MaxPool2D(pool_size=3, strides=2, padding=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Inception Model - Stage 4. We use a total of 512 channels (128 + 256 + 64 + 64) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.631945Z",
     "start_time": "2019-07-07T06:08:45.617220Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "5"
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   },
   "outputs": [],
   "source": [
    "b4 = nn.Sequential()\n",
    "b4.add(Inception(192, (96, 208), (16, 48), 64),\n",
    "       Inception(160, (112, 224), (24, 64), 64),\n",
    "       Inception(128, (128, 256), (24, 64), 64),\n",
    "       Inception(112, (144, 288), (32, 64), 64),\n",
    "       Inception(256, (160, 320), (32, 128), 128),\n",
    "       nn.MaxPool2D(pool_size=3, strides=2, padding=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Inception Model - Stage 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.641445Z",
     "start_time": "2019-07-07T06:08:45.633251Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "6"
    }
   },
   "outputs": [],
   "source": [
    "b5 = nn.Sequential()\n",
    "b5.add(Inception(256, (160, 320), (32, 128), 128),\n",
    "       Inception(384, (192, 384), (48, 128), 128),\n",
    "       nn.GlobalAvgPool2D())\n",
    "\n",
    "net = nn.Sequential()\n",
    "net.add(b1, b2, b3, b4, b5, nn.Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Priming the network (at full size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-07T06:08:45.760919Z",
     "start_time": "2019-07-07T06:08:45.643249Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequential0 output shape:\t (1, 64, 24, 24)\n",
      "sequential1 output shape:\t (1, 192, 12, 12)\n",
      "sequential2 output shape:\t (1, 480, 6, 6)\n",
      "sequential3 output shape:\t (1, 832, 3, 3)\n",
      "sequential4 output shape:\t (1, 1024, 1, 1)\n",
      "dense0 output shape:\t (1, 10)\n"
     ]
    }
   ],
   "source": [
    "X = np.random.uniform(size=(1, 1, 96, 96))\n",
    "net.initialize()\n",
    "for layer in net:\n",
    "    X = layer(X)\n",
    "    print(layer.name, 'output shape:\\t', X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-07-07T06:08:42.014Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    }
   },
   "outputs": [
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