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RayanPoudyal · Nov 13, 2023 · b9238e3 · b9238e3
1 parent 34235a3
commit b9238e3
Showing 1 changed file with 8 additions and 8 deletions.
diff --git a/Notebooks/Chap07/7_2_Backpropagation.ipynb b/Notebooks/Chap07/7_2_Backpropagation.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyN2nPVR0imZntgj4Oasyvmo",
+      "authorship_tag": "ABX9TyM8DZv6WppyaQxi8igoKV+X",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -143,7 +143,7 @@
         "  # Run through the layers, calculating all_f[0...K-1] and all_h[1...K]\n",
         "  for layer in range(K):\n",
         "      # Update preactivations and activations at this layer according to eqn 7.16\n",
-        "      # Remember to use np.matmul for matrix multiplications\n",
+        "      # Remmember to use np.matmul for matrrix multiplications\n",
         "      # TODO -- Replace the lines below\n",
         "      all_f[layer] = all_h[layer]\n",
         "      all_h[layer+1] = all_f[layer]\n",
@@ -244,28 +244,28 @@
         "  all_dl_dh = [None] * (K+1)\n",
         "  # Again for convenience we'll stick with the convention that all_h[0] is the net input and all_f[k] in the net output\n",
         "\n",
-        "  # Compute derivatives of net output with respect to loss\n",
+        "  # Compute derivatives of the loss with respect to the network output\n",
         "  all_dl_df[K] = np.array(d_loss_d_output(all_f[K],y))\n",
         "\n",
         "  # Now work backwards through the network\n",
         "  for layer in range(K,-1,-1):\n",
-        "    # TODO Calculate the derivatives of biases at layer this from all_dl_df[layer]. (eq 7.21)\n",
+        "    # TODO Calculate the derivatives of the loss with respect to the biases at layer this from all_dl_df[layer]. (eq 7.21)\n",
         "    # NOTE!  To take a copy of matrix X, use Z=np.array(X)\n",
         "    # REPLACE THIS LINE\n",
         "    all_dl_dbiases[layer] = np.zeros_like(all_biases[layer])\n",
         "\n",
-        "    # TODO Calculate the derivatives of weight at layer from all_dl_df[K] and all_h[K] (eq 7.22)\n",
+        "    # TODO Calculate the derivatives of the loss with respect to the weights at layer from all_dl_df[K] and all_h[K] (eq 7.22)\n",
         "    # Don't forget to use np.matmul\n",
         "    # REPLACE THIS LINE\n",
         "    all_dl_dweights[layer] = np.zeros_like(all_weights[layer])\n",
         "\n",
-        "    # TODO: calculate the derivatives of activations from weight and derivatives of next preactivations (eq 7.20)\n",
+        "    # TODO: calculate the derivatives of the loss with respect to th eactivations from weight and derivatives of next preactivations (eq 7.20)\n",
         "    # REPLACE THIS LINE\n",
         "    all_dl_dh[layer] = np.zeros_like(all_h[layer])\n",
         "\n",
         "\n",
         "    if layer > 0:\n",
-        "      # TODO Calculate the derivatives of the pre-activation f with respect to activation h (deriv of ReLu function)\n",
+        "      # TODO Calculate the derivatives of the loss with respect to the pre-activation f with respect to activation h (deriv of ReLu function)\n",
         "      # REPLACE THIS LINE\n",
         "      all_dl_df[layer-1] = np.zeros_like(all_f[layer-1])\n",
         "\n",
@@ -299,7 +299,7 @@
         "# Let's test if we have the derivatives right using finite differences\n",
         "delta_fd = 0.000001\n",
         "\n",
-        "# Test the derivatives of the bias vectors\n",
+        "# Test the dervatives of the bias vectors\n",
         "for layer in range(K):\n",
         "  dl_dbias  = np.zeros_like(all_dl_dbiases[layer])\n",
         "  # For every element in the bias\n",