sk-autoencoder1.ipynb

{"cells":[{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":33708,"status":"ok","timestamp":1615222755054,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"m07ztbyAxqkF","outputId":"0f8868ab-a0c3-41eb-8588-c878bb1ded8e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":881,"status":"ok","timestamp":1615225197676,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"HiHSPRmwvL8m"},"outputs":[],"source":["import json\n","import glob\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","%matplotlib inline\n","\n","import keras\n","from keras.models import Sequential\n","from keras.layers.core import Dense, Dropout, Activation\n","\n","from IPython import display\n","\n"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":959,"status":"ok","timestamp":1615226842066,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"pDgKrc0bvL8n"},"outputs":[],"source":["class GameMap:\r\n","    def __init__(self):\r\n","        self.levels = []\r\n","        self.widths = []\r\n","        self.heights = []\r\n","        self.nmaps = 0\r\n","        \r\n","        with open(\"Zelda/zelda.json\") as f:\r\n","            self.tiledefs = json.load(f)\r\n","            #print(self.tiledefs)\r\n","        \r\n","        for fname in sorted(glob.glob(\"Zelda/zelda/zelda_lvl*.txt\")):\r\n","            print(fname)\r\n","            with open(fname) as f:\r\n","                data = [line.strip() for line in f.readlines()]\r\n","                self.levels.append(data)\r\n","                self.heights.append(len(data))\r\n","                self.widths.append(len(data[0]))\r\n","                self.nmaps += 1\r\n","\r\n","    def getMapAsArray(self, level=0):\r\n","        a = np.ndarray((self.heights[level], self.widths[level]))\r\n","        for r, row in enumerate(self.levels[level]):\r\n","            for c, tile in enumerate(row):\r\n","                if tile == 'w':\r\n","                    a[r,c] = 0\r\n","                else:\r\n","                    a[r,c] = 1\r\n","        return a\r\n","    \r\n","\r\n","            \r\n","            "]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":897,"status":"ok","timestamp":1615226847594,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"L-iUJQsWvL8o","outputId":"9264ff59-7cb7-4674-f2a3-60c615b72aa5"},"outputs":[{"name":"stdout","output_type":"stream","text":["Zelda/zelda\\zelda_lvl1.txt\n","Zelda/zelda\\zelda_lvl2.txt\n","Zelda/zelda\\zelda_lvl3.txt\n","Zelda/zelda\\zelda_lvl4.txt\n","Zelda/zelda\\zelda_lvl5.txt\n"]}],"source":["gm = GameMap()\r\n","a = np.concatenate([gm.getMapAsArray(i) for i in range(gm.nmaps)], axis=1)\r\n","at = a.transpose()"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"executionInfo":{"elapsed":939,"status":"ok","timestamp":1615226849600,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"Mg7upfcovL8o","outputId":"6407c351-0a61-40af-f7c8-fe750fb289f8"},"outputs":[{"data":{"text/plain":["(0.0, 65.0)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1152x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig, axes = plt.subplots(1,1,figsize=(16,4))\n","axes.imshow(a,cmap=\"gray\", interpolation=\"nearest\")\n","axes.set_xlim(0,65)"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":904,"status":"ok","timestamp":1615226853353,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"hUAWrUoTvL8p"},"outputs":[],"source":["hiddensize = 7\n","model = Sequential()\n","model.add(Dense(hiddensize, input_shape=(9,)))\n","model.add(Activation(\"relu\"))\n","model.add(Dense(14))\n","# leaving off this final relu seems to do a much better job\n","#model.add(Activation(\"relu\"))\n","model.add(Activation(\"sigmoid\"))\n","\n","# \"mse\" seems to be the best loss function for the problem\n","# \"RMSprop\" seems to be the best, with \"adadelta\" also good\n","model.compile(loss='mse', optimizer='RMSprop', metrics=['accuracy'])\n","\n"]},{"cell_type":"code","execution_count":18,"metadata":{"executionInfo":{"elapsed":887,"status":"ok","timestamp":1615226855600,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"puODQwQdvL8p"},"outputs":[],"source":["model = Sequential()\n","model.add(Dense(12, input_shape=(9,)))\n","model.add(Activation(\"relu\"))\n","model.add(Dropout(.05))\n","\n","model.add(Dense(8))\n","model.add(Activation(\"relu\"))\n","model.add(Dropout(.05))\n","\n","model.add(Dense(12))\n","model.add(Activation(\"relu\"))\n","model.add(Dropout(.05))\n","\n","model.add(Dense(9))\n","\n","# leaving off this final relu seems to do a much better job.  I think you don't want\n","# your final activation to be something with hard cutoffs.\n","#model.add(Activation(\"relu\"))\n","model.add(Activation(\"sigmoid\"))\n","\n","# \"mse\" seems to be the best loss function for the problem\n","# \"RMSprop\" seems to be the best, with \"adadelta\" also good\n","model.compile(loss='mse', optimizer='RMSprop', metrics=['accuracy'])\n","\n"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":842},"executionInfo":{"elapsed":6013,"status":"ok","timestamp":1615226862677,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"EWRNwrScvL8q","outputId":"210f53f8-4781-4fb8-febd-22cfaa47c31a"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1152x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA6IAAABJCAYAAAAnk5tbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAP1UlEQVR4nO3dbYxVx33H8d+fZR/YXWDX5kGAeTIQLKt2SYWcF1aiVG5cp6pCW6mV7arCLRaNFUuN+iZO/aJR5Bdp2vStK1exlEqxndgttlWlAVet6lepAdcyYCCAAbMsLOZxgV1YdvffF3uR9syZe8949+65e/H3I1nszP7vnJkzZ+717D0zx9xdAAAAAACUZU6jKwAAAAAA+HxhIgoAAAAAKBUTUQAAAABAqZiIAgAAAABKxUQUAAAAAFAqJqIAAAAAgFIlTUTN7DEzO2xmR83suZmuFAAAAADgzmVFzxE1sxZJv5b0NUl9knZLesLdP5r56gEAAAAA7jRzE2IeknTU3T+WJDN7TdIWSVUnop2dnb5w4UINDw8XFt7S0pLLGxsby6Tb2tpyMSMjIzXL7ejo+MyviWlvby+MibWztbU1k75169aUjhX+oWB8fLwwJhSeTyl/3mPnOBRrQ1j2nDn5L9lHR0cz6blziy+7lDpPJWbevHmFx065bmPtDPNSrm0zK4yJiV0Hk6W08+bNm4UxsfqFxy66/qoJx0jsfIVjNnascKyvWLFiSvUBAABAmr17955398XTKSNlIrpC0qlJ6T5JX6r1goULF2rbtm3at29fYeFdXV25vOvXr2crEPkfy9OnT9csd+PGjbm8kydPFtYntGHDhsKYWDsXL872y6efflpYzr333pvLCycLsUlSONELJzJXr17NvWb+/PmFxw7FzvmVK1cy6Vh/nj9/PpPu7e3NxYSTkMHBwVzMggULph3zwAMP5F4TSrluY3/oCNve3d2diwnrE5uUx/ordOPGjZq/T2nnkSNHCmNik8Pw2CkT55hwjPT09ORiTp06lUnH/hhy//33Z9IvvPDClOoDAACANGb22SdWgZQ1ovmvRKTc1xJmtt3M9pjZnqGhoenWCwAAAABwh0qZiPZJWjkpfY+k/jDI3V9y983uvrmzs7Ne9QMAAAAA3GFSbs3dLWmDma2VdFrS45KerPWCjo4OrV+/Xs89V7zB7rVr13J5AwMDmXRs/eLKlStzeZPt2rUrl/fUU08V1if03nvvFcbE2nns2LFMet26dYXl7N+/P5cX3rq5aNGiXEx4a24Yk7JW8c033yys39NPP53LC9e1xm6XDW+TPnv2bC4mvMU35XbsqcS88cYbudeEUq7bTz75JJcXXsuxmAcffDCTTllfHLNq1aqav09p55NP1hzGkqS77rorlxeu0wxv804VjpHYGu7wdufYbd07d+6c0vEBAADQOIUTUXcfNbNnJe2U1CLpZXc/MOM1AwAAAADckVK+EZW7/0LSL2a4LgAAAACAz4GUNaIAAAAAANRN0jein5W7y91za8BiYjvshusXY2vHip4JGnvOY7j2NEXKMxJj7QwfWZLi4sWLubxwfWfssR3hGtHw8R+xZ0GG7UppZ19fXy4vXMMaW496+PDhTDrWzrCcWDvDPp9KzFT7MxRrQ/iondh5Dx9HEhO7dkNFz6VNaWfKeAjX7kr5dqU8szQmHCOxcxqud46tQZ7qc0wBAADQOHwjCgAAAAAoFRNRAAAAAECpmIgCAAAAAErFRBQAAAAAUKoZ2azIzDRnzhy1tbUVxsY2Ggk3fYkpKjvc/EaSxsfHC8sNpWwcE6tLeKyUcxGrc3h+YjFdXV2ZdHt7eyYda0NYv5R2xo4dbk7U3d1dWL/h4eFcTFjnmJRzWBQz1f4Mxa6llOsrLDu24VKKerQzpb6xjYjCjZJSzlfK8WPHCmNim2GltBUAAACzC/8HBwAAAAAoFRNRAAAAAECpmIgCAAAAAEo1I2tEpYl1W0uWLCmMu379ei5vbGwsk75y5Uoupqjs/v7+XN7ixYsL65NSTkpdrl27VhgTunDhQmFMT09PLi9cuxmu04ytvwzX4aass4sdO1zXFztWb29vJh1bmxiuM2xtbc3FhGsTpxKT0s6UvgqPI+X7PLaeMTyHsTWiKetli+qY0s6U8RDr83DdcmxdcIrwfMWu/7COCxcuzMUcOnRoSscHAABA4/CNKAAAAACgVExEAQAAAAClYiIKAAAAAChV0hpRMzsh6aqkMUmj7r55JisFAAAAALhzfZbNin7b3c+nBLq7bt26pePHjxfGDg8P5ysVbL5z6dKlXMzo6GjNcmOvMbPC+oRim9KEYu0MN15JOfb58/nTG254E24yFCs73Pins7Mz95qhoaFMOqWdp0+fzuWFmxXFNiIKN5i5fPlyLiZsQ0dHRy4m3NhnKjFT7c/Q4OBgLi/sm/AcS9KZM2cy6ZSNm2JGRkZq/j6lnbHrLRTbKCy8JlPqGxOOkVibwjqGGxxJaW0FAADA7MKtuQAAAACAUqVORF3SLjPba2bbYwFmtt3M9pjZnti3FgAAAAAASOm35j7s7v1mtkTSO2Z2yN3fnRzg7i9JekmS1qxZ47FCAAAAAABImoi6e3/l33NmtkPSQ5LerRZvZmppadF9991XWHbs29Nw3Wh3d3cuZu3atTXLPXv2bC5v5cqVhfUJnTp1qjAm1s5jx45l0uvWrSssJ7bWLVyPd/fddxeWs2DBgky6q6srF3P9+vVMOqxvzJo1a3J5YdnhGk1JWr16dSY9MDCQiwlft2jRolxMuF5wKjEHDhzIvSaUct2Gaz0l6erVq5n0uXPncjHLly/PpMfGxnIxsTW9oWXLltX8fUo7U8bD/PnzC2Ni4zNFyjUXnotYfY4cOTKl4wMAAKBxCm/NNbMuM5t/+2dJj0raP9MVAwAAAADcmVK+EV0qaUdlV9O5kl5x91/OaK0AAAAAAHeswomou38s6TdLqAsAAAAA4HOAx7cAAAAAAEpl7vXf4Hb16tX+/PPP5zZviQk345Gk0dHRTDrcfEfKbwwTim3qE9tgpkjK5kCxurS2tmbSsY2IQrFNX1JeFwrP6eDgYC4mPKexfgiNjIzk8sJNj2IbCF2+fLnwWOEmNOFrJKmnp2faMSmb76Rct7FzEfZ5R0dHLmZ8fLyw7PCcxrS1tdX8fUo7L1y4UBgzb968XN7Nmzcz6blzUzffzgrPV+zchBuXzZmT/9tZb29vJr19e/QJUwAAAKgTM9vr7punUwbfiAIAAAAASsVEFAAAAABQKiaiAAAAAIBSTW1xV4EbN27o0KFDevHFFwtjY2vQwvVv4bo/STp58mTNch955JFc3sGDBwvrE9qyZUthTKyd69evz6SPHj1aWM6mTZtyeeF62bNnz+ZiwvWKN27cyKTDNX2S1N7enklv27atsH6vv/56Lm9oaKjwdRcvXsykwzV9klR5PFDNcjs7O6cd88wzz9SurOL9GVq+fHkuL1x3u2rVqlzMhx9+mEnH1gWnnNP+/v6av09p51tvvVUYc+nSpVxeSl+lCMfI0qVLczHHjx/PpGPrgrdu3Tql4wMAAKBx+EYUAAAAAFAqJqIAAAAAgFIxEQUAAAAAlIqJKAAAAACgVObu9S/U7Kqkw3UvGGVYJOl8oyuBKaP/mhd919zov+ZG/zUv+q650X/Na6O7z59OATOya66kw+6+eYbKxgwysz30XfOi/5oXfdfc6L/mRv81L/quudF/zcvM9ky3DG7NBQAAAACUiokoAAAAAKBUMzURfWmGysXMo++aG/3XvOi75kb/NTf6r3nRd82N/mte0+67GdmsCAAAAACAarg1FwAAAABQqrpORM3sMTM7bGZHzey5epaN+jOzlWb232Z20MwOmNlfVfK/Z2anzeyDyn+/1+i6Is/MTpjZvkof7ank3WVm75jZkcq/vY2uJ/LMbOOk8fWBmQ2a2bcZe7OXmb1sZufMbP+kvKrjzcy+W/ksPGxmv9uYWkOq2nd/b2aHzOxDM9thZj2V/DVmNjxpDP5TwyoOSVX7r+p7JWNv9qjSdz+b1G8nzOyDSj5jb5apMU+o22df3W7NNbMWSb+W9DVJfZJ2S3rC3T+qywFQd2a2TNIyd3/fzOZL2ivpDyT9iaRr7v4PjawfajOzE5I2u/v5SXk/lHTR3X9Q+WNQr7t/p1F1RLHKe+dpSV+S9Odi7M1KZvYVSdck/Yu7/0YlLzrezOx+Sa9KekjSckn/KekL7j7WoOp/rlXpu0cl/Ze7j5rZ30lSpe/WSPr323FovCr99z1F3isZe7NLrO+C3/9I0hV3/z5jb/apMU94SnX67KvnN6IPSTrq7h+7+4ik1yRtqWP5qDN3P+Pu71d+virpoKQVja0VpmmLpJ9Ufv6JJt4wMLs9IumYu59sdEVQnbu/K+likF1tvG2R9Jq733T345KOauIzEg0Q6zt33+Xuo5XkryTdU3rFkKTK2KuGsTeL1Oo7MzNNfPHxaqmVQrIa84S6ffbVcyK6QtKpSek+MalpGpW/RH1R0v9Wsp6t3LL0Mrd3zlouaZeZ7TWz7ZW8pe5+Rpp4A5G0pGG1Q6rHlf0gZuw1j2rjjc/D5vIXkv5jUnqtmf2fmf2PmX25UZVCodh7JWOveXxZ0oC7H5mUx9ibpYJ5Qt0+++o5EbVIHlvyNgEz65b0r5K+7e6Dkl6UtE7SJklnJP2ocbVDDQ+7+29J+rqkb1VugUETMbM2Sd+Q9Holi7F3Z+DzsEmY2fOSRiX9tJJ1RtIqd/+ipL+W9IqZLWhU/VBVtfdKxl7zeELZP8Iy9mapyDyhamgkr+b4q+dEtE/SyknpeyT117F8zAAza9XExfVTd/83SXL3AXcfc/dxSf8sbmuZldy9v/LvOUk7NNFPA5V7+m/f23+ucTVEgq9Let/dByTGXhOqN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Sakn/VYP4UISZbZf0TUlfc/fPpuQvKWwgJjO7T5N9d6o2UaKYEtdKxl5j+LKkE+4+cDODsVd/is0TVMHPvpZKBVvYee5FST+V1CzpVXf/qFL1Y0Y8KumPJR25uX22pL+U9JyZfVGTt9NPS3qhFsGhpGWS9k9eI9Qi6TV3/4mZHZK0z8z+XNLHkp6uYYwowczmaHKX8anjaw9jrz6Z2euSHpe02MwGJP21pO8qMt7c/SMz2yfpmCYf+/w6u3bWTpG++7akdknvFK6jP3P3nZK+JGm3mY1JGpe0093zbpSDGVCk/x6PXSsZe/Ul1nfu/n2l90aQGHv1qNg8oWKffRX7+hYAAAAAAPKo5KO5AAAAAABkYiIKAAAAAKgqJqIAAAAAgKpiIgoAAAAAqComogAAAACAqmIiCgAAAACoKiaiAAAAAICqYiIKAAAAAKiq/wOEuI/oF3Zc9gAAAABJRU5ErkJggg==","text/plain":["<Figure 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Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["<Figure size 1152x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["for i in range(10):\r\n","    model.fit(at, at, epochs=50, batch_size=64, shuffle=True, verbose=False)\r\n","    pt = model.predict(at)\r\n","    p = pt.transpose()\r\n","    fig, axes = plt.subplots(1,1,figsize=(16,4))\r\n","    axes.imshow(p,cmap=\"gray\", interpolation=\"nearest\")\r\n","    axes.set_xlim(0,200)\r\n","    display.clear_output(wait=True)\r\n","    display.display(plt.gcf())\r\n","    #axes[1].imshow(a,cmap=\"gray\", interpolation=\"nearest\")"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"executionInfo":{"elapsed":1397,"status":"ok","timestamp":1615226889894,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"HsBjF-HEvL8q","outputId":"ebbb1ced-b101-4f99-83fb-63f157821565"},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x26ad212bd00>"]},"execution_count":20,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1152x288 with 3 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["#test_a = gm.getMapAsArray(1) For multiple levels\n","test_a = gm.getMapAsArray(0)\n","test_at = test_a.transpose()\n","test_pt = model.predict(test_at)\n","test_p = test_pt.transpose()\n","fig, axes = plt.subplots(3,1,figsize=(16,4))\n","axes[0].imshow(test_p,cmap=\"gray\", interpolation=\"nearest\")\n","axes[1].imshow(test_a,cmap=\"gray\", interpolation=\"nearest\")\n","axes[2].imshow(test_a-test_p, interpolation=\"nearest\", cmap=\"hot\", vmin=-1, vmax=1)\n"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"executionInfo":{"elapsed":1014,"status":"ok","timestamp":1615226914767,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"DTo-n3WZvL8q","outputId":"ba185ab9-b3a3-41da-e158-bd3cec4bcde5"},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x26ad1929cd0>"]},"execution_count":21,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1152x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["pt = model.predict(at)\n","p = pt.transpose()\n","fig, axes = plt.subplots(2,1,figsize=(16,4))\n","axes[0].imshow(p,cmap=\"gray\", interpolation=\"nearest\")\n","axes[1].imshow(a,cmap=\"gray\", interpolation=\"nearest\")"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"executionInfo":{"elapsed":1162,"status":"ok","timestamp":1615226918043,"user":{"displayName":"Jot Developers","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjnfoa4TuAEkvD5W1I4Nq7qutjW7IN3vXuM-1vw=s64","userId":"07569265833334137014"},"user_tz":-300},"id":"cvLTKsK5vL8r","outputId":"90c1e242-d61f-4d7c-9bdd-c2607cc0b13d"},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x26ad4613550>"]},"execution_count":22,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1152x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["pt = model.predict(at)\n","p = pt.transpose()\n","fig, axes = plt.subplots(1,1,figsize=(16,4))\n","axes.imshow(a-p,cmap=\"gray\", interpolation=\"nearest\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"lt0x2QlRvL8r"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":[],"name":"autoencoder1.ipynb","provenance":[]},"interpreter":{"hash":"71ff158078b9094704a9ca0f88dd88feb63a8763717434d1ce66e321efc9e249"},"kernelspec":{"display_name":"Python 3.8.8 64-bit ('base': conda)","name":"python3"},"language_info":{"name":"python","version":""}},"nbformat":4,"nbformat_minor":0}