[MOD] the arquitecture for the CNN

CNN - MNIST.ipynb

@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1293d25d0>"
+       "<matplotlib.image.AxesImage at 0x7f66cc282e90>"
       ]
      },
      "execution_count": 3,
@@ -76,14 +76,11 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
+     "name": "stdout",
-      "text/plain": [
+     "output_type": "stream",
-       "4"
+     "text": [
-      ]
+      "4\n"
-     },
+     ]
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -109,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -118,7 +115,7 @@
        "100"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -132,7 +129,47 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "class CNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(CNN,self).__init__()\n",
+    "        # Same padding means that input_size = output_size\n",
+    "        # same_padding = (filter_size - 1) / 2\n",
+    "        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3,3), stride=1, padding=1)\n",
+    "        # The output from this layer has size:\n",
+    "        # [(input_size - filter_size + 2*(padding)) / stride] + 1\n",
+    "        self.batchnorm1 = nn.BatchNorm2d(8)\n",
+    "        self.relu = nn.ReLU()\n",
+    "        self.maxpool = nn.MaxPool2d(kernel_size=(2,2))\n",
+    "        # Pooling output size is 28/2 = 14 (downsampling)\n",
+    "        # Same padding size is (5 - 1)/2 = 2\n",
+    "        self.cnn2 = nn.Conv2d(in_channels=8, out_channels=32, kernel_size=(5,5), stride=1, padding=2)\n",
+    "        self.batchnorm2 = nn.BatchNorm2d(32)\n",
+    "        # Pooling output size is 14/2 = 7 (downsampling)\n",
+    "        # We have to flatten the output channels 32*7*7 = 1568\n",
+    "        self.fc1 = nn.Linear(1568,600)\n",
+    "        self.dropout = nn.Dropout(p=0.5)\n",
+    "        self.fc2 = nn.Linear(600,10)\n",
+    "        \n",
+    "    def forward(self,x):\n",
+    "        out = self.cnn1(x)\n",
+    "        out = self.batchnorm1(out)\n",
+    "        out = self.relu(out)\n",
+    "        out = self.maxpool(out)\n",
+    "        out = self.cnn2(out)\n",
+    "        out = self.batchnorm2(out)\n",
+    "        out = self.relu(out)\n",
+    "        out = self.maxpool(out)\n",
+    "        # we have to flatten the 32 feature maps, output of our last maxpool (100, 1568)\n",
+    "        out = out.view(batch_size, 1568)\n",
+    "        out = self.fc1(out)\n",
+    "        out = self.relu(out)\n",
+    "        out = self.dropout(out)\n",
+    "        out = self.fc2(out)\n",
+    "        \n",
+    "        return out\n",
+    "        "
+   ]
   }
  ],
  "metadata": {

Convolutional_NN.ipynb

@@ -1,32 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}