Activation Function Code

Chapter 9/2. Activation Function Code.ipynb

@@ -29,7 +29,7 @@
     "\n",
     "def softmax(x):\n",
     "    temp=np.exp(x)\n",
-    "    return temp/np.sum(temp,azis=1,keepdims=True)"
+    "    return temp/np.sum(temp,axis=1,keepdims=True)"
    ]
   },
   {
@@ -47,6 +47,55 @@
     "weights_0_1=0.02*np.random.random((pixels_per_image,hidden_size))-0.01\n",
     "weights_1_2=0.2*np.random.random((hidden_size,num_lables))-0.1"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for j in xrange(iterations):\n",
+    "    correct_cnt=0\n",
+    "    for i in xrange(len(images)/batch_size):\n",
+    "        batch_start,batch_end=((i*batch_size),((i+1)*batch_size))\n",
+    "        layer_0=images[batch_start:batch_end]\n",
+    "        layer_1=tanh(np.dot(layer_0,weights_0_1))\n",
+    "        dropout_mask=np.random.randint(2,size=layer_1.shape)\n",
+    "        layer_1*=dropout_masl*2\n",
+    "        layer_2=softmax(np.dot(layer_1,weights_1_1))\n",
+    "        \n",
+    "        for k in xrange(batch_size):\n",
+    "            correct_cnt+=int(np.argmax(layer_2[k:k+1])==\\\n",
+    "                            np.argmax(labels[batch_start+k:batch_start+K+1]))\n",
+    "            layer_2_delta=(labels[batch_start:batch_end]-layer_2)/(batch_size*layer_2.shape[0])\n",
+    "            layer_1_detal=layer_2_delta.dot(weights_1_2.T)*tanh2deriv(layer_1)\n",
+    "            layer_1_delta*=dropout_mask\n",
+    "            \n",
+    "            weights_1_2+=alpha*layer_1.T.dot(layer_2_delta)\n",
+    "            weights_0_1+=alpha*layer_0.T.dot(layer_1_delta)\n",
+    "            \n",
+    "            test_correct_cnt=0\n",
+    "            \n",
+    "            for i in xrange(len(test_images)):\n",
+    "                layer_0=test_images[i:i+1]\n",
+    "                layer_1=relu(np.dot(layer_0,weights_0_1))\n",
+    "                layer_2=np.dot(layer_1,weights_1_2)\n",
+    "                \n",
+    "                test_correct_cnt+=int(np.argmax(layer_2)==np.argmax(test_labels[i:i+1]))\n",
+    "            \n",
+    "            if(j%10==0):\n",
+    "                sys.stdout.write('\\n'+\\\n",
+    "                                'I:'+str(j)+\\\n",
+    "                                'Test-Acc:'+str(test_correct_cnt/float(len(test_images)))+\\\n",
+    "                                'Train-Acc:'+str(correct_cnt/float((len(images)))))\n",
+    "            \n",
+    "            \n",
+    "            \n",
+    "        \n",
+    "    "
+   ]
   }
  ],
  "metadata": {