Fix typo

Author: singleye

NeuralNetwork/Softmax/experiment-softmax.ipynb

@@ -48,7 +48,6 @@
     "    return 1/(1+np.exp(-z))\n",
     "\n",
     "def cost(activations, expectations):\n",
-    "    #return np.sum(expectations*np.log(activations), 1)\n",
     "    return np.sum(-expectations*np.log(activations))"
    ]
   },
@@ -321,7 +320,6 @@
     "for i in xrange(num_epochs):\n",
     "    # feed forward\n",
     "    z2 = np.dot(input, w2) + b2\n",
-    "    #z2 = np.dot(input, w2)\n",
     "    a2 = sigmoid(z2) # 7x5\n",
     "\n",
     "    z3 = np.dot(a2, w3) + b3\n",
@@ -340,21 +338,14 @@
     "\n",
     "    # 7x2\n",
     "    delta_l3 = a3 - output\n",
-    "    #deriv_w3 = np.dot(delta_l3, w3.T)*a2\n",
     "    deriv_w3 = np.dot(a2.T, delta_l3)\n",
     "    deriv_b3 = delta_l3\n",
     "    w3 -= eta*deriv_w3\n",
     "    b3 -= eta*np.mean(deriv_b3, 0)\n",
     "    \n",
     "    delta_l2 = np.dot(delta_l3, w3.T)*(a2*(1-a2)) # 7x5\n",
-    "    #print \"d2\", delta_l2\n",
-    "    #print \"w2\", w2\n",
-    "    # d: 7x5\n",
-    "    # w: 4x5\n",
     "    deriv_w2 = np.dot(input.T, delta_l2)\n",
     "    deriv_b2 = delta_l2\n",
-    "    #print deriv_w2\n",
-    "    #print deriv_b2\n",
     "    w2 -= eta*deriv_w2\n",
     "    b2 -= eta*np.mean(deriv_b2, 0)"
    ]