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udlbook
2024-01-10 16:29:08 -05:00
parent 762ac337ae
commit be6a0a07fe
1 changed files with 13 additions and 2 deletions
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15
Notebooks/Chap07/7_2_Backpropagation.ipynb
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@@ -4,7 +4,7 @@
"metadata": { "metadata": {
"colab": { "colab": {
"provenance": [], "provenance": [],
"authorship_tag": "ABX9TyOlKB4TrCJnt91TnHOrfRSJ", "authorship_tag": "ABX9TyM2kkHLr00J4Jeypw41sTkQ",
"include_colab_link": true "include_colab_link": true
}, },
"kernelspec": { "kernelspec": {
@@ -311,10 +311,16 @@
" network_output_2, *_ = compute_network_output(net_input, all_weights, all_biases)\n", " network_output_2, *_ = compute_network_output(net_input, all_weights, all_biases)\n",
" dl_dbias[row] = (least_squares_loss(network_output_1, y) - least_squares_loss(network_output_2,y))/delta_fd\n", " dl_dbias[row] = (least_squares_loss(network_output_1, y) - least_squares_loss(network_output_2,y))/delta_fd\n",
" all_dl_dbiases_fd[layer] = np.array(dl_dbias)\n", " all_dl_dbiases_fd[layer] = np.array(dl_dbias)\n",
" print(\"-----------------------------------------------\")\n",
" print(\"Bias %d, derivatives from backprop:\"%(layer))\n", " print(\"Bias %d, derivatives from backprop:\"%(layer))\n",
" print(all_dl_dbiases[layer])\n", " print(all_dl_dbiases[layer])\n",
" print(\"Bias %d, derivatives from finite differences\"%(layer))\n", " print(\"Bias %d, derivatives from finite differences\"%(layer))\n",
" print(all_dl_dbiases_fd[layer])\n", " print(all_dl_dbiases_fd[layer])\n",
" if np.allclose(all_dl_dbiases_fd[layer],all_dl_dbiases[layer],rtol=1e-05, atol=1e-08, equal_nan=False):\n",
" print(\"Success! Derivatives match.\")\n",
" else:\n",
" print(\"Failure! Derivatives different.\")\n",
"\n",
"\n", "\n",
"\n", "\n",
"# Test the derivatives of the weights matrices\n", "# Test the derivatives of the weights matrices\n",
@@ -330,10 +336,15 @@
" network_output_2, *_ = compute_network_output(net_input, all_weights, all_biases)\n", " network_output_2, *_ = compute_network_output(net_input, all_weights, all_biases)\n",
" dl_dweight[row][col] = (least_squares_loss(network_output_1, y) - least_squares_loss(network_output_2,y))/delta_fd\n", " dl_dweight[row][col] = (least_squares_loss(network_output_1, y) - least_squares_loss(network_output_2,y))/delta_fd\n",
" all_dl_dweights_fd[layer] = np.array(dl_dweight)\n", " all_dl_dweights_fd[layer] = np.array(dl_dweight)\n",
" print(\"-----------------------------------------------\")\n",
" print(\"Weight %d, derivatives from backprop:\"%(layer))\n", " print(\"Weight %d, derivatives from backprop:\"%(layer))\n",
" print(all_dl_dweights[layer])\n", " print(all_dl_dweights[layer])\n",
" print(\"Weight %d, derivatives from finite differences\"%(layer))\n", " print(\"Weight %d, derivatives from finite differences\"%(layer))\n",
" print(all_dl_dweights_fd[layer])" " print(all_dl_dweights_fd[layer])\n",
" if np.allclose(all_dl_dweights_fd[layer],all_dl_dweights[layer],rtol=1e-05, atol=1e-08, equal_nan=False):\n",
" print(\"Success! Derivatives match.\")\n",
" else:\n",
" print(\"Failure! Derivatives different.\")"
], ],
"metadata": { "metadata": {
"id": "PK-UtE3hreAK" "id": "PK-UtE3hreAK"