sorry/demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aallah\n"
     ]
    }
   ],
   "source": [
    "print(\"aallah\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'set_theme'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-a89cf2b5f8d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_theme\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"whitegrid\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Load the example diamonds dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'set_theme'"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Load the example diamonds dataset\n",
    "diamonds = sns.load_dataset(\"diamonds\")\n",
    "\n",
    "# Draw a scatter plot while assigning point colors and sizes to different\n",
    "# variables in the dataset\n",
    "f, ax = plt.subplots(figsize=(6.5, 6.5))\n",
    "sns.despine(f, left=True, bottom=True)\n",
    "clarity_ranking = [\"I1\", \"SI2\", \"SI1\", \"VS2\", \"VS1\", \"VVS2\", \"VVS1\", \"IF\"]\n",
    "sns.scatterplot(x=\"carat\", y=\"price\",\n",
    "                hue=\"clarity\", size=\"depth\",\n",
    "                palette=\"ch:r=-.2,d=.3_r\",\n",
    "                hue_order=clarity_ranking,\n",
    "                sizes=(1, 8), linewidth=0,\n",
    "                data=diamonds, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use('_mpl-gallery')\n",
    "\n",
    "# make data\n",
    "x = np.linspace(0, 10, 100)\n",
    "y = 4 + 2 * np.sin(2 * x)\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, y, linewidth=2.0)\n",
    "\n",
    "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n",
    "       ylim=(0, 8), yticks=np.arange(1, 8))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(2, 1, 1) # two rows, one column, first plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.subplots_adjust(top=0.8)\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.set_ylabel('volts')\n",
    "ax1.set_title('a sine wave')\n",
    "\n",
    "t = np.arange(0.0, 1.0, 0.01)\n",
    "s = np.sin(2*np.pi*t)\n",
    "line, = ax1.plot(t, s, color='blue', lw=2)\n",
    "\n",
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "ax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])\n",
    "n, bins, patches = ax2.hist(np.random.randn(1000), 50,\n",
    "                            facecolor='yellow', edgecolor='yellow')\n",
    "ax2.set_xlabel('time (s)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'set_theme'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-a89cf2b5f8d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_theme\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"whitegrid\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Load the example diamonds dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'set_theme'"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Load the example diamonds dataset\n",
    "diamonds = sns.load_dataset(\"diamonds\")\n",
    "\n",
    "# Draw a scatter plot while assigning point colors and sizes to different\n",
    "# variables in the dataset\n",
    "f, ax = plt.subplots(figsize=(6.5, 6.5))\n",
    "sns.despine(f, left=True, bottom=True)\n",
    "clarity_ranking = [\"I1\", \"SI2\", \"SI1\", \"VS2\", \"VS1\", \"VVS2\", \"VVS1\", \"IF\"]\n",
    "sns.scatterplot(x=\"carat\", y=\"price\",\n",
    "                hue=\"clarity\", size=\"depth\",\n",
    "                palette=\"ch:r=-.2,d=.3_r\",\n",
    "                hue_order=clarity_ranking,\n",
    "                sizes=(1, 8), linewidth=0,\n",
    "                data=diamonds, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/nix/store/4w1rxv6wfkv728jzmm6pqq3jsqd4yvi7-python3-3.7.3/bin:/nix/store/vcj6f6sv9v5gqjx27qf9sbkiiiap2v5p-python3.7-notebook-5.7.8/bin:/nix/store/zhhnxn9np7ly8gdycldvkdkzc7sgkjrq-python3.7-setuptools-41.0.1/bin:/nix/store/x3d33scgp0lk4b0qh3b18673f610bb2g-python3.7-jupyter_core-4.4.0/bin:/nix/store/g802a8fmwlidanw0j903xfzhh5q99qd5-python3.7-ipython-7.2.0/bin:/nix/store/k5jmrfn6q01xcggjd49rf78cvj21kbjl-python3.7-Pygments-2.3.1/bin:/nix/store/ww5qjqrcyzrdmdmj7wgvp4ycflg4rjh9-python3.7-docutils-0.14/bin:/nix/store/4hzr51hzdzx835vnhvri6bg18jx3w7sr-python3.7-jupyter_client-5.2.4/bin:/nix/store/g0bq93y20369j0275xzsdxin2idcpc0w-python3.7-nbformat-4.4.0/bin:/nix/store/84f6mzp4yw3f42l1bhwp7dyf4rwi6w6b-python3.7-jsonschema-2.6.0/bin:/nix/store/dnbxpvphxnmij8f5skxkym3a696y25m1-python3.7-nbconvert-5.4.1/bin:/nix/store/q3ida6v51vglk8l4v446481qgimnl7ap-python3.7-chardet-3.0.4/bin:/nix/store/x3d33scgp0lk4b0qh3b18673f610bb2g-python3.7-jupyter_core-4.4.0/bin:/nix/store/4w1rxv6wfkv728jzmm6pqq3jsqd4yvi7-python3-3.7.3/bin:/nix/store/x3d33scgp0lk4b0qh3b18673f610bb2g-python3.7-jupyter_core-4.4.0/bin:/nix/store/g802a8fmwlidanw0j903xfzhh5q99qd5-python3.7-ipython-7.2.0/bin:/nix/store/zhhnxn9np7ly8gdycldvkdkzc7sgkjrq-python3.7-setuptools-41.0.1/bin:/nix/store/k5jmrfn6q01xcggjd49rf78cvj21kbjl-python3.7-Pygments-2.3.1/bin:/nix/store/ww5qjqrcyzrdmdmj7wgvp4ycflg4rjh9-python3.7-docutils-0.14/bin:/nix/store/8kgsjv57icc18qhpmj588g9x1w34hi4j-bash-interactive-5.1-p12/bin:/nix/store/qpzwm6z4igakmqr4n4k6k3q0a4bqy3ws-patchelf-0.9/bin:/nix/store/1ap5d85630s3ksal6xgkjnbglmbng3kg-gcc-wrapper-7.4.0/bin:/nix/store/d8k7bv2w9g669dv7r9z4wrr9cnzdncdv-gcc-7.4.0/bin:/nix/store/2fxzw4ilrgc4klppk1nc50vgcwfphh1s-glibc-2.27-bin/bin:/nix/store/wpjdad5wpylnpqbjw4dbnih8f6q32l43-coreutils-8.31/bin:/nix/store/yzijh65sak6z06cdvrg9wi2d1964g6h3-binutils-wrapper-2.31.1/bin:/nix/store/c222w06ysx899n7r1jqaw96l6x6g8q9i-binutils-2.31.1/bin:/nix/store/2fxzw4ilrgc4klppk1nc50vgcwfphh1s-glibc-2.27-bin/bin:/nix/store/wpjdad5wpylnpqbjw4dbnih8f6q32l43-coreutils-8.31/bin:/nix/store/awzvfmszh60vfkajsg1mgvyq81vlm50m-chord-0.1.0/bin:/nix/store/r1az7lczwhzwz8hss7rvj0gnm7xlri5v-python3-3.7.3-env/bin:/nix/store/wpjdad5wpylnpqbjw4dbnih8f6q32l43-coreutils-8.31/bin:/nix/store/d9y878m6hk96mc05pz4vdzqrlqcfl7rs-findutils-4.6.0/bin:/nix/store/xnvj5phwyv5fj4idkrwass1243nn5n19-diffutils-3.7/bin:/nix/store/g92ybkzhiqcw7xsz5yq1dayjjlhll914-gnused-4.7/bin:/nix/store/da2jifip9xsab81yh34h887a1fwnz9gd-gnugrep-3.3/bin:/nix/store/vk9rvr21phkc2jfb765rnrlhb171ywh2-gawk-4.2.1/bin:/nix/store/d34nmar6fd15yf24rw8h5aiiwcywlzbq-gnutar-1.32/bin:/nix/store/f1689ai31ca0m7j6q8lpyf10z16rmrmw-gzip-1.10/bin:/nix/store/cr2fv6r0bic97wmakjdxcwbvyp4hqn2z-bzip2-1.0.6.0.1-bin/bin:/nix/store/cpn4i0g3s7m5l0i6v1i7k855ylfxfn0g-gnumake-4.2.1/bin:/nix/store/fyxcppddjg7abrand8n10gwzm5gknc48-bash-4.4-p23/bin:/nix/store/1nnnsl2l0hcnzcy4410pbpicsvkfkzj9-patch-2.7.6/bin:/nix/store/rshc59lkp5j44fjzg2a5hi2ril4dd7ka-xz-5.2.4-bin/bin:/home/dumball/.autojump/bin:/run/wrappers/bin:/home/dumball/.nix-profile/bin:/etc/profiles/per-user/dumball/bin:/nix/var/nix/profiles/default/bin:/run/current-system/sw/bin'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import os\n",
    "os.environ.get('MPLCONFIGDIR')\n",
    "os.environ.get('PATH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os    \n",
    "import tempfile\n",
    "os.environ['MPLCONFIGDIR'] = tempfile.mkdtemp()\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/run/user/1000/tmpc2r9f11o'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import os\n",
    "os.environ.get('MPLCONFIGDIR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9.0\n"
     ]
    }
   ],
   "source": [
    "print(sns.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 14s 1us/step\n",
      "WARNING:tensorflow:From /nix/store/49fyimyln41iyh8ghp35q1bxf6lnfj97-python3-3.7.3-env/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /nix/store/49fyimyln41iyh8ghp35q1bxf6lnfj97-python3-3.7.3-env/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 5s 75us/step - loss: 0.0763 - val_loss: 0.0529\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 4s 67us/step - loss: 0.0436 - val_loss: 0.0355\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0328 - val_loss: 0.0296\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0285 - val_loss: 0.0265\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0259 - val_loss: 0.0244\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0240 - val_loss: 0.0227\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0225 - val_loss: 0.0215\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0214 - val_loss: 0.0205\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 4s 71us/step - loss: 0.0205 - val_loss: 0.0197\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 4s 71us/step - loss: 0.0197 - val_loss: 0.0189\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Input\n",
    "from keras import optimizers\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "train_x = x_train.reshape(60000, 784) / 255\n",
    "val_x = x_test.reshape(10000, 784) / 255\n",
    "\n",
    "autoencoder = Sequential()\n",
    "autoencoder.add(Dense(512,  activation='elu', input_shape=(784,)))\n",
    "autoencoder.add(Dense(128,  activation='elu'))\n",
    "autoencoder.add(Dense(10,    activation='linear', name=\"bottleneck\"))\n",
    "autoencoder.add(Dense(128,  activation='elu'))\n",
    "autoencoder.add(Dense(512,  activation='elu'))\n",
    "autoencoder.add(Dense(784,  activation='sigmoid'))\n",
    "autoencoder.compile(loss='mean_squared_error', optimizer = Adam())\n",
    "trained_model = autoencoder.fit(train_x, train_x, batch_size=1024, epochs=10, verbose=1, validation_data=(val_x, val_x))\n",
    "encoder = Model(autoencoder.input, autoencoder.get_layer('bottleneck').output)\n",
    "encoded_data = encoder.predict(train_x)  # bottleneck representation\n",
    "decoded_output = autoencoder.predict(train_x)        # reconstruction\n",
    "encoding_dim = 10\n",
    "\n",
    "# return the decoder\n",
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "decoder = autoencoder.layers[-3](encoded_input)\n",
    "decoder = autoencoder.layers[-2](decoder)\n",
    "decoder = autoencoder.layers[-1](decoder)\n",
    "decoder = Model(encoded_input, decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is putting a huge load on my computer \n"
     ]
    }
   ],
   "source": [
    "print(\"this is putting a huge load on my computer \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5070.066]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from keras.preprocessing import image\n",
    "img = image.load_img(\"./hello.png\", target_size=(28, 28), color_mode = \"grayscale\")\n",
    "input_img = image.img_to_array(img)\n",
    "inputs = input_img.reshape(1,784)\n",
    "target_data = autoencoder.predict(inputs)\n",
    "dist = np.linalg.norm(inputs - target_data, axis=-1)\n",
    "print(dist)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6674.866]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from keras.preprocessing import image\n",
    "img = image.load_img(\"./1_nlfLUgHUEj5vW7WVJpxY-g.png\", target_size=(28, 28), color_mode = \"grayscale\")\n",
    "input_img = image.img_to_array(img)\n",
    "inputs = input_img.reshape(1,784)\n",
    "target_data = autoencoder.predict(inputs)\n",
    "dist = np.linalg.norm(inputs - target_data, axis=-1)\n",
    "print(dist)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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