Fix some type error

7_deep_learning/1_CNN/4-resnet.ipynb

@@ -47,7 +47,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T12:56:06.772059Z",
      "start_time": "2017-12-22T12:56:06.766027Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -69,7 +70,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T12:47:49.222432Z",
      "start_time": "2017-12-22T12:47:49.217940Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -85,7 +87,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T13:14:02.429145Z",
      "start_time": "2017-12-22T13:14:02.383322Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -203,13 +206,14 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T13:27:46.099404Z",
      "start_time": "2017-12-22T13:27:45.986235Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
     "class ResNet(nn.Module):\n",
     "    def __init__(self, in_channel, num_classes, verbose=False):\n",
-    "        super(resnet, self).__init__()\n",
+    "        super(ResNet, self).__init__()\n",
     "        self.verbose = verbose\n",
     "        \n",
     "        self.block1 = nn.Conv2d(in_channel, 64, 7, 2)\n",
@@ -290,7 +294,7 @@
     }
    ],
    "source": [
-    "test_net = resnet(3, 10, True)\n",
+    "test_net = ResNet(3, 10, True)\n",
     "test_x = Variable(torch.zeros(1, 3, 96, 96))\n",
     "test_y = test_net(test_x)\n",
     "print('output: {}'.format(test_y.shape))"
@@ -414,7 +418,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,

7_deep_learning/1_CNN/5-densenet.ipynb

@@ -45,7 +45,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.113030Z",
      "start_time": "2017-12-22T15:38:30.612922Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -73,11 +74,12 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.121249Z",
      "start_time": "2017-12-22T15:38:31.115369Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "def conv_block(in_channel, out_channel):\n",
+    "def Conv_Block(in_channel, out_channel):\n",
     "    layer = nn.Sequential(\n",
     "        nn.BatchNorm2d(in_channel),\n",
     "        nn.ReLU(True),\n",
@@ -100,17 +102,18 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.145274Z",
      "start_time": "2017-12-22T15:38:31.123363Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "class dense_block(nn.Module):\n",
+    "class Dense_Block(nn.Module):\n",
     "    def __init__(self, in_channel, growth_rate, num_layers):\n",
-    "        super(dense_block, self).__init__()\n",
+    "        super(Dense_Block, self).__init__()\n",
     "        block = []\n",
     "        channel = in_channel\n",
     "        for i in range(num_layers):\n",
-    "            block.append(conv_block(channel, growth_rate))\n",
+    "            block.append(Conv_Block(channel, growth_rate))\n",
     "            channel += growth_rate\n",
     "            \n",
     "        self.net = nn.Sequential(*block)\n",
@@ -170,7 +173,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.222120Z",
      "start_time": "2017-12-22T15:38:31.215770Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -234,7 +238,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.318822Z",
      "start_time": "2017-12-22T15:38:31.236857Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -305,7 +310,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:32.894729Z",
      "start_time": "2017-12-22T15:38:31.656356Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -403,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,

7_deep_learning/1_CNN/6-batch-normalization.ipynb

@@ -601,7 +601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,