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  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "import sys\n",
    "# sys.path.append('/path/to/mlpractical')\n",
    "from mlp.test_methods import test_dropout_layer\n",
    "import numpy as np\n",
    "\n",
    "fprop_test, fprop_output, fprop_correct, \\\n",
    "bprop_test, bprop_output, bprop_correct = test_dropout_layer()\n",
    "\n",
    "assert fprop_test == 1.0, (\n",
    "'The dropout layer fprop functionality test failed'\n",
    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
    ".format(fprop_correct, fprop_output, fprop_output-fprop_correct)\n",
    ")\n",
    "\n",
    "print(\"Dropout Layer Fprop Functionality Test Passed\")\n",
    "\n",
    "assert bprop_test == 1.0, (\n",
    "'The dropout layer bprop functionality test failed'\n",
    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
    ".format(bprop_correct, bprop_output, bprop_output-bprop_correct)\n",
    ")\n",
    "\n",
    "print(\"Dropout Layer Bprop Test Passed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlp.test_methods import test_L1_Penalty\n",
    "\n",
    "\n",
    "call_test, call_output, call_correct, \\\n",
    "grad_test, grad_output, grad_correct = test_L1_Penalty()\n",
    "\n",
    "\n",
    "assert call_test == 1.0, (\n",
    "'The call function for L1 Penalty test failed'\n",
    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
    ".format(call_correct, call_output, call_output-call_correct)\n",
    ")\n",
    "\n",
    "print(\"L1 Penalty Call Functionality Test Passed\")\n",
    "\n",
    "assert grad_test == 1.0, (\n",
    "'The grad function for L1 Penalty test failed'\n",
    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
    ".format(grad_correct, grad_output, grad_output-grad_correct, grad_output/grad_correct)\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "print(\"L1 Penalty Grad Function Test Passed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlp.test_methods import test_L2_Penalty\n",
    "\n",
    "\n",
    "call_test, call_output, call_correct, \\\n",
    "grad_test, grad_output, grad_correct = test_L2_Penalty()\n",
    "\n",
    "\n",
    "assert call_test == 1.0, (\n",
    "'The call function for L2 Penalty test failed'\n",
    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
    ".format(call_correct, call_output, call_output-call_correct)\n",
    ")\n",
    "\n",
    "print(\"L2 Penalty Call Functionality Test Passed\")\n",
    "\n",
    "assert grad_test == 1.0, (\n",
    "'The grad function for L2 Penalty test failed'\n",
    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
    ".format(grad_correct, grad_output, grad_output-grad_correct, grad_output/grad_correct)\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "print(\"L2 Penalty Grad Function Test Passed\")"
   ]
  }
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