123 lines
3.7 KiB
Plaintext
123 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import sys\n",
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"# sys.path.append('/path/to/mlpractical')\n",
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"from mlp.test_methods import test_dropout_layer\n",
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"import numpy as np\n",
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"\n",
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"fprop_test, fprop_output, fprop_correct, \\\n",
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"bprop_test, bprop_output, bprop_correct = test_dropout_layer()\n",
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"\n",
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"assert fprop_test == 1.0, (\n",
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"'The dropout layer fprop functionality test failed'\n",
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"'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
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".format(fprop_correct, fprop_output, fprop_output-fprop_correct)\n",
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")\n",
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"\n",
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"print(\"Dropout Layer Fprop Functionality Test Passed\")\n",
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"\n",
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"assert bprop_test == 1.0, (\n",
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"'The dropout layer bprop functionality test failed'\n",
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"'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
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".format(bprop_correct, bprop_output, bprop_output-bprop_correct)\n",
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")\n",
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"\n",
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"print(\"Dropout Layer Bprop Test Passed\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from mlp.test_methods import test_L1_Penalty\n",
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"\n",
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"\n",
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"call_test, call_output, call_correct, \\\n",
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"grad_test, grad_output, grad_correct = test_L1_Penalty()\n",
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"\n",
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"\n",
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"assert call_test == 1.0, (\n",
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"'The call function for L1 Penalty test failed'\n",
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"'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
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".format(call_correct, call_output, call_output-call_correct)\n",
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")\n",
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"\n",
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"print(\"L1 Penalty Call Functionality Test Passed\")\n",
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"\n",
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"assert grad_test == 1.0, (\n",
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"'The grad function for L1 Penalty test failed'\n",
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"'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
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".format(grad_correct, grad_output, grad_output-grad_correct, grad_output/grad_correct)\n",
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")\n",
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"\n",
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"\n",
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"\n",
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"print(\"L1 Penalty Grad Function Test Passed\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from mlp.test_methods import test_L2_Penalty\n",
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"\n",
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"\n",
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"call_test, call_output, call_correct, \\\n",
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"grad_test, grad_output, grad_correct = test_L2_Penalty()\n",
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"\n",
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"\n",
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"assert call_test == 1.0, (\n",
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"'The call function for L2 Penalty test failed'\n",
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"'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
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".format(call_correct, call_output, call_output-call_correct)\n",
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")\n",
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"\n",
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"print(\"L2 Penalty Call Functionality Test Passed\")\n",
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"\n",
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"assert grad_test == 1.0, (\n",
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"'The grad function for L2 Penalty test failed'\n",
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"'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
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".format(grad_correct, grad_output, grad_output-grad_correct, grad_output/grad_correct)\n",
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")\n",
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"\n",
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"\n",
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"\n",
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"print(\"L2 Penalty Grad Function Test Passed\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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