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{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"id": "9e931031-4384-4c42-8398-20cfb25540c2",
"metadata": {},
"outputs": [],
"source": [
"import sys,os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import warnings\n",
"\n",
"import tensorflow as tf\n",
"from sklearn.utils import shuffle\n",
"from joblib import load, dump\n",
"from itertools import chain\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from skimage.metrics import structural_similarity as ssim\n",
"\n",
"from aggmap import AggMap\n",
"from aggmap.utils.matrixopt import smartpadding\n",
"import aggmap\n",
"\n",
"sns.set(style = 'white', font_scale=1.5)\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"from scipy.spatial.distance import cdist, squareform\n",
"from scipy.stats import entropy, truncnorm\n",
"\n",
"def get_graph_v(embd):\n",
" '''\n",
" Get the graph in low dimension\n",
" '''\n",
" distances = cdist(embd.embedding_, embd.embedding_)\n",
" a = embd._a\n",
" b = embd._b\n",
" graph_v = 1.0 / (1.0 + a * distances ** (2 * b)) \n",
" return graph_v\n",
"\n",
"def get_graph_u(embd):\n",
" '''\n",
" Get the graph in high dimension\n",
" '''\n",
" return embd.graph_.toarray()\n",
"\n",
"def cross_entropy_via_scipy(gu, gv):\n",
" ''' SEE: https://en.wikipedia.org/wiki/Cross_entropy'''\n",
" return entropy(gu) + entropy(gu, gv)\n",
" \n",
"def get_loss_pcc_from_embd(embd):\n",
" gv = get_graph_v(embd)\n",
" np.fill_diagonal(gv,0)\n",
" gv = squareform(gv)\n",
" gu = get_graph_u(embd)\n",
" gu = squareform(gu)\n",
" l1 = pd.Series(gu).to_frame(name = 'y_true')\n",
" l2 = pd.Series(gv).to_frame(name = 'y_pred')\n",
" pcc = l1.join(l2).corr().iloc[0].iloc[1]\n",
" loss = cross_entropy_via_scipy(gu, gv)\n",
" return loss, pcc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1230aa0e-5d90-48da-b829-f89eb7b37b38",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 197,
"id": "09c27d79-5656-451a-a464-7079e67f5641",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"label\n",
"0 [1, 21, 34, 37, 51, 56, 63, 68, 69, 75, 81, 88...\n",
"1 [3, 6, 8, 14, 23, 24, 40, 59, 67, 70, 72, 77, ...\n",
"2 [5, 16, 25, 28, 76, 82, 109, 117, 120, 122, 14...\n",
"3 [7, 10, 12, 27, 30, 44, 49, 50, 74, 86, 98, 10...\n",
"4 [2, 9, 20, 26, 53, 58, 60, 61, 64, 89, 92, 115...\n",
"5 [0, 11, 35, 47, 65, 100, 132, 138, 145, 173, 1...\n",
"6 [13, 18, 32, 36, 39, 62, 66, 73, 83, 90, 93, 1...\n",
"7 [15, 29, 38, 42, 52, 71, 79, 84, 91, 96, 101, ...\n",
"8 [17, 31, 41, 46, 55, 85, 94, 97, 125, 137, 144...\n",
"9 [4, 19, 22, 33, 43, 45, 48, 54, 57, 80, 87, 11...\n",
"dtype: object"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() #\n",
"label_dict = {'0': 'T-shirt/top',\n",
" '1': 'Trouser',\n",
" '2': 'Pullover', \n",
" '3': 'Dress', \n",
" '4': 'Coat', \n",
" '5': 'Sandal', \n",
" '6': 'Shirt', \n",
" '7': 'Sneaker', \n",
" '8': 'Bag', \n",
" '9': 'Ankle boot'}\n",
"\n",
"label_idx = pd.Series(y_train).to_frame(name='label').groupby('label').apply(lambda x:x.index.tolist())\n",
"\n",
"select_class = 3\n",
"\n",
"x_train = x_train[label_idx.loc[select_class]]\n",
"\n",
"n, w, h = x_train.shape\n",
"orignal_cols = ['p-%s' % str((i+1)).zfill(len(str(w*h))) for i in range(w*h)]\n",
"x_train_df = pd.DataFrame(x_train.reshape(x_train.shape[0], w*h), columns = orignal_cols)\n",
"\n",
"\n",
"label_idx"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ed83047-a366-4ef7-a074-60f3d83aa02f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 198,
"id": "d1539cbb-877f-498f-9df2-a788958f8b91",
"metadata": {},
"outputs": [],
"source": [
"shuffled_cols = shuffle(orignal_cols, random_state=111)\n",
"x_train_df_shuffled = x_train_df[shuffled_cols]\n",
"x_shuffled = x_train_df_shuffled.values.reshape(-1, 28,28)"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "5bac7160-a413-49ae-95ab-1ab627e00e2c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-12 12:28:36,953 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - Calculating distance ...\u001b[0m\n",
"2021-09-12 12:28:36,986 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - the number of process is 16\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|##########| 306936/306936 [00:09<00:00, 34017.10it/s]\n",
"100%|##########| 306936/306936 [00:00<00:00, 2713204.32it/s]\n",
"100%|##########| 784/784 [00:00<00:00, 1114.48it/s]\n"
]
}
],
"source": [
"mp = AggMap(x_train_df_shuffled, metric='correlation')"
]
},
{
"cell_type": "code",
"execution_count": 232,
"id": "da0b71a4-947b-4d27-bcb7-326b3c24ee4b",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-12 12:31:36,677 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - applying hierarchical clustering to obtain group information ...\u001b[0m\n",
"UMAP(a=None, angular_rp_forest=False, b=None, dens_frac=0.0, dens_lambda=0.0,\n",
" dens_var_shift=0.1, densmap=False, disconnection_distance=None,\n",
" force_approximation_algorithm=False, init='random', learning_rate=0.1,\n",
" local_connectivity=1.0, low_memory=True, metric='precomputed',\n",
" metric_kwds=None, min_dist=0.1, n_components=2, n_epochs=None, n_jobs=-1,\n",
" n_neighbors=15, negative_sample_rate=5, output_dens=False,\n",
" output_metric='euclidean', output_metric_kwds=None, random_state=32,\n",
" repulsion_strength=1.0, set_op_mix_ratio=1.0, spread=1.0,\n",
" target_metric='categorical', target_metric_kwds=None, ...)\n",
"Construct fuzzy simplicial set\n",
"Sun Sep 12 12:31:36 2021 Finding Nearest Neighbors\n",
"Sun Sep 12 12:31:36 2021 Finished Nearest Neighbor Search\n",
"Sun Sep 12 12:31:36 2021 Construct embedding\n",
"\tcompleted 0 / 500 epochs\n",
"\tcompleted 50 / 500 epochs\n",
"\tcompleted 100 / 500 epochs\n",
"\tcompleted 150 / 500 epochs\n",
"\tcompleted 200 / 500 epochs\n",
"\tcompleted 250 / 500 epochs\n",
"\tcompleted 300 / 500 epochs\n",
"\tcompleted 350 / 500 epochs\n",
"\tcompleted 400 / 500 epochs\n",
"\tcompleted 450 / 500 epochs\n",
"Sun Sep 12 12:31:38 2021 Finished embedding\n",
"2021-09-12 12:31:38,070 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - Applying grid feature map(assignment), this may take several minutes(1~30 min)\u001b[0m\n",
"2021-09-12 12:31:38,387 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - Finished\u001b[0m\n"
]
}
],
"source": [
"mp = mp.fit(emb_method='umap', cluster_channels=1, var_thr=1, learning_rate=0.1, init= 'random') "
]
},
{
"cell_type": "code",
"execution_count": 233,
"id": "35f596a6-54e9-4f4c-bab3-2273b60918df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-12 12:31:38,391 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - generate file: ./feature points_555_correlation_umap_scatter\u001b[0m\n",
"2021-09-12 12:31:38,403 - \u001b[32mINFO\u001b[0m - [bidd-aggmap]\u001b[0m - save html file to ./feature points_555_correlation_umap_scatter\u001b[0m\n"
]
},
{
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"p-269", "Subtypes": "cluster_01", "colors": "#00fff6"}, {"x": 3.7795298099517822, "y": 4.72201681137085, "IDs": "p-119", "Subtypes": "cluster_01", "colors": "#00fff6"}, {"x": 0.8338680863380432, "y": 6.70074462890625, "IDs": "p-323", "Subtypes": "cluster_01", "colors": "#00fff6"}, {"x": 7.323197364807129, "y": 7.3239874839782715, "IDs": "p-682", "Subtypes": "cluster_01", "colors": "#00fff6"}, {"x": 0.3848287761211395, "y": 2.9714083671569824, "IDs": "p-276", "Subtypes": "cluster_01", "colors": "#00fff6"}], "type": "scatter", "color": "#00fff6", "name": "cluster_01"}]; var dataLen = data.length; for (var ix = 0; ix < dataLen; ix++) { chart.addSeries(data[ix]); } }); </script> </body> </html>\" height=850 width=1000></iframe>"
],
"text/plain": [
"<highcharts.highcharts.highcharts.Highchart at 0x7fa87f3a5240>"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mp.plot_scatter()"
]
},
{
"cell_type": "code",
"execution_count": 234,
"id": "9f920583-3b73-4cac-99b8-e511e69b747e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|##########| 6131/6131 [00:01<00:00, 3093.30it/s]\n"
]
}
],
"source": [
"x_res = mp.batch_transform(x_train_df_shuffled.values, scale=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a2bec36-d953-416b-8a4b-33f66e331f70",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 235,
"id": "cd419a2c-fe93-4453-a1fe-50f043e50a97",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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MTej1eklbvV6PxkZeOUzegcWe6F9qamqwZs0ajBkzBjNnzgQAGI1GAJBM9wBAYGCgdTuRp2OxJ0LHCH7x4sUIDQ1FQUEBdLqOt0ZQUBAAwGw2S/YxmUzW7USezrNX4/joOv51Ef+rjxWb196pd3ZGXqmu6e+y8bvZCxX3WXw6VDb+m37yKzESav/seGIeoqmpCYsWLUJTUxN27txpM2XT+d+d0zldGQwGhIcrLzn1VHJ/2+0KS239n35BEtv85WzZtq0XD0tibWWH5I+7cLkkpvvOQNm2sr61JBsAWj7fI9v0h3/7iyR2zyzeJzKO7EloJpMJS5YswbVr1/Dee+9h6NChNtujo6Ph5+eHiooKm7jZbEZlZSViY2NdmS5Rj7HYk7Da2tqwbNkyXLx4EQUFBUhISJC06dOnD8aNG4fS0lI0Nzdb46Wlpbh37x5SU1NdmTJRj3n2NA6RE23cuBHHjh1DcnIybt++jdLSUuu24OBgpKSkAACWL1+Op59+GllZWcjIyEB9fT22bduGJ598EuPHj3dX+kQOYbEnYXVeOHX8+HEcP25764zIyEhrsR85ciS2bduG/Px85ObmIiQkBJmZmcjJyXF5zkQ9xWJPwlq3bh0GDrTvS8GxY8di165dTs7INfbe+EIS+81G+S/ev/jNf0tic8rXyLb1S/ixXTE1tNVJFwT0ebrQKefSCo8u9n/NWA0T/G1i2b0eV2z/IlfjOOS7JVcd3mfvDSckQkROxy9oiYgEwGJPRCQAFnsiIgF0O2dfXl6OkpISPs2HiMiLdVvst2zZgvPnz/NpPkQaFnO1Qjbeyz9QEvtu3K/tPu73k+XvChr87ha79m9rrJWNz57OlTeO6rbYL1iwAPn5+W55ms9j/7sKkRG29x55MWqKw8chIhJdt3P2SUlJfJoPEZGX69EXtHyaDxGRd+lRsefTfIiIvIvDV9DyaT5E4rjfYpLEUm99bv8B9irFn+hZQtRjDo3s+TQfIiLvZPfIXrSn+RARaYldxb7r03y2b9/+wKf5TJnyzdLIzqf5TJ8+vUfJ+fgHwSfA9oKspg/+Q7F9n8zf9eg8RERa1+00Dp/mQ0Tk/bod2b/55pt8mg8RkZfrtthfuXIFAJ/mQ0Tkzbot9kVFRXYfTEtP8yEi0hLe4piISAAe/VjC9vtNaL9nu0Z/yr+XuSkbIiLvxZE9EZEAWOyJiATAYk9EJAAWeyIiAbDYExEJgMWeiEgAHr30cv3cTQi22D7w+FTjFTdlQ0TkvTiyJyISAIs9EZEAWOyJiATAYk9EJACP/IK2ra0NAHAPZsDHdpufn4/MHkT28/Pt6EOd/cxVOs/XeX4iNXXXrz2y2Hc+y/ZY4FXJtqhBIa5OhzTKYDBg8ODBLj0fAAyMDHbZOUk8Sv3ax2KxWNyQzwMZjUZUVFRAr9fD19fX3emQxrS1tcFgMCAuLg5BQUHd76AS9mtypu76tUcWeyIiUhe/oCUiEgCLPRGRAFjsiYgEwGJPRCQAFnsiIgGw2BMRCYDFnohIACz2REQC8NhibzabkZeXh4kTJyI+Ph6ZmZk4deqUu9NSXWNjI/Lz85GVlYXExETExMTgzJkzsm2PHj2KWbNmYdSoUZg8eTIKCwvR2trq4ozVUV5ejtdeew1paWlISEjA5MmTsXz5cnz11VeStufPn8fPfvYzjB49GhMmTMAbb7yB+/fvuyHrh6eFfq3FPitCf/TYYr9y5Urs2LEDM2bMwKpVq6DT6bBo0SJcuHDB3ampqra2Fps3b0ZDQwNiYmIU2504cQLZ2dkIDQ3F6tWrkZKSgvXr1yM3N9eF2apny5YtOHz4MMaPH49Vq1YhMzMTZ8+eRXp6OmpqaqztKisrsWDBAphMJqxcuRJz5szB7t27sXz5cjdm33Na6Nda7LNC9EeLB7p06ZIlOjrasm3bNmvMaDRaUlJSLPPmzXNfYk7Q1NRkuXXrlsVisVgOHz5siY6Otpw+fVrSLi0tzTJr1ixLa2urNbZu3TrLiBEjLLW1ta5KVzXnzp2zmEwmm1htba0lLi7OsmLFCmvs2WeftTzxxBOWu3fvWmMffPCBJTo62nLy5EmX5asGrfRrLfZZEfqjR47sDx06BH9/f2RkZFhjgYGBmDNnDs6dO4fGxkY3ZqeukJAQhIWFPbBNdXU1qqurMXfuXJsbaM2bNw/t7e0oKytzdpqqS0pKQkBAgE0sKioKw4cPt46k7t69i5MnTyI9PR3Bwd/cKXLmzJno3bs3Dh486NKcH5ZW+rUW+6wI/dEji31lZSWGDBli8wsFgPj4eFgsFlRWVropM/e4fPkyACAuLs4m3r9/f0RERFi3ezuLxYKbN29aC0lVVRVaW1slrzsgIACxsbFe1w9E6tda6LNa648eWewNBgPCw8Mlcb1eDwBeMwJSS+d90Dtff1d6vV4zv4/9+/ejoaEBU6dOBaC91y1Sv9bC305r/dEji73RaIS/v78kHhgYCAAwmUyuTsmtjEYjAEg+ZgIdv5PO7d6spqYGa9aswZgxYzBz5kwA2nvdIvVrb//babE/emSxDwoKQktLiyTe+WbofHOIovNBBGazWbLNZDK59AEczmAwGLB48WKEhoaioKAAOl1Ht9Ta6xapX3vz306r/dEji73SR6LOj1FyH4W1rPNjY+fr70ppasBbNDU1YdGiRWhqasKWLVtsPiJr7XWL1K+99W+n5f7okcV+xIgRqK2tRXNzs0380qVL1u0iiY2NBQBUVFTYxBsaGlBfX2/d7m1MJhOWLFmCa9eu4b333sPQoUNttkdHR8PPz0/yus1mMyorK73udYvUr72xz2q9P3pksU9NTUVLSwv27NljjZnNZuzbtw9JSUno37+/G7NzveHDh2Po0KHYvXu3zZPjd+7cCZ1OhylTprgxu55pa2vDsmXLcPHiRRQUFCAhIUHSpk+fPhg3bhxKS0ttCmRpaSnu3buH1NRUV6b80ETq197WZ0Xoj76vvvrqq+5O4tsiIiJQXV2N4uJiNDc34/r168jNzUVNTQ3y8vIwYMAAd6eoqg0bNuCLL77A2bNncfXqVeh0OlRVVaGqqgrx8fEAgMjISGzfvh3nz5+H2WxGSUkJtm3bhrlz52LWrFlufgWOy83NxUcffYRJkybhscces77eqqoqXL9+3TqqGjZsGIqKinDixAm0t7fjyJEjKCgowIQJE5Cdne3mV+EYLfVrrfVZEfqjxz5w3GQy4Z133sGBAwdw584dxMTEICcnB+PHj3d3aqpTuuQ8MjISx44ds/585MgRFBYWoqamBv369cPs2bOxdOlS+Pn5uSpV1WRlZeHs2bOy2779ur/88kvk5+fj8uXLCAkJQVpaGnJyctC7d29XpasarfRrrfVZEfqjxxZ7IiJSj0fO2RMRkbpY7ImIBMBiT0QkABZ7IiIBsNgTEQmAxZ6ISAAs9kREAmCxJyISAIs9EZEAWOyJiATw/1gZXEOOwZ6eAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ZtEdHR8PPzw+1tbU2cYvFAqPRiNjYWGemSzRgLPYkrZ6eHqxcuRIXLlxASUkJpk2bpuozfPhwzJgxA5WVlejo6LDGKysrcfv2baSkpDgzZaIBc+vTOESDaevWrfj000+RlJSEr776CpWVlda24OBgJCcnAwBWrVqFF154ATk5OcjKykJLSwvKysrw9NNPY+bMma5Kn8ghLPYkrb4bp44fP47jx4/btEVGRlqL/dSpU1FWVobi4mIUFhYiJCQEixYtQl5entNzJhooFnuS1qZNmzB27Fi7+j7xxBPYs2fPIGdE9qpZMFoV80tbKux7a/nLqtiTLX/WPSd359bFfn7rH9HdzctxiIgeFL+gJSKSAIs9EZEEWOyJiCTQ7zn7mpoa7N+/n0/zISLyYP0W+x07dqC6uppP8yEipzOlRQnjQet/qYrd+fmrwr6Gg/JdeSPSb7FfsmQJiouL+TQfIiIP1u85+8TERD7Nh4jIww3oC1o+zYeIyLMMqNjzaT5ERJ7F4Tto+TQfIrJX5ainhfGn026oYk1HAlWxkC3Fwu2/fO7fVbF/vtbqYHZycWhmz6f5EBF5Jrtn9nyaDxGR57Kr2N/7NJ+dO3fe92k+c+fOtcb7nuaTlpY2oOQqRibiYcX21NAzfz05oH0REcms39M4fJoPEZHn63dmv3HjRj7Nh4jIw/Vb7Ovq6gDwaT5E5LhtAe3C+LO5uapYeN1mVWzVt9cIt996rVYYJ239Fvtdu3bZvTM+zYeIyD1xiWMiIgm49WMJs7+q5mMJiYh0wJk9EZEE3HpmT0Se7eOW88J4yGxxXO2yfslIjjN7IiIJsNgTEUmAxZ6ISAIs9kREEmCxJyKSAIs9EZEEWOyJiCTAYk9EJAEWeyIiCbjlHbQ9PT0AAD/fIS7OhLxR37jqG2fOwnFNg6m/ce2Wxb7vWbZjI4NdnAl5M5PJhPHjxzv19QCOaxpcWuN6iKIobresZGdnJ2pra2EwGODr6+vqdMjL9PT0wGQyIS4uDkFBQU57XY5rGkz9jWu3LPZERKQvfkFLRCQBFnsiIgmw2BMRSYDFnohIAiz2REQSYLEnIpIAiz0RkQRY7ImIJOC2xd5isaCoqAizZ89GfHw8Fi1ahJMnT7o6Ld21tbWhuLgYOTk5SEhIQExMDE6fPi3se+zYMWRmZuKxxx7DnDlzUFpaiu7ubidnrI+amhqsW7cOqampmDZtGubMmYNVq1bhiy++UPWtrq7Giy++iMcffxyzZs3Chg0bcOfOHRdk/eC8YVx745iVYTy6bbEvKCjABx98gOeffx6rV6+Gj48PcnNzcf78eVenpqumpiZs374dra2tiImJ0exXVVWFFStWIDQ0FGvWrEFycjI2b96MwsJCJ2arnx07duDIkSOYOXMmVq9ejUWLFuHMmTPIyMhAY2OjtZ/RaMSSJUtgNptRUFCAhQsXoqKiAqtWrXJh9gPnDePaG8esFONRcUMXL15UoqOjlbKyMmuss7NTSU5OVhYvXuy6xAZBe3u7cuPGDUVRFOXIkSNKdHS0curUKVW/1NRUJTMzU+nu7rbGNm3apEyZMkVpampyVrq6OXfunGI2m21iTU1NSlxcnJKfn2+N/fCHP1Seeuop5datW9bYhx9+qERHRysnTpxwWr568JZx7Y1jVobx6JYz+0OHDsHf3x9ZWVnWWGBgIBYuXIhz586hra3NhdnpKyQkBGFhYfft09DQgIaGBmRnZ9ssoLV48WL09vbi8OHDg52m7hITExEQEGATmzBhAqKioqwzqVu3buHEiRPIyMhAcPA/VopMT0/HsGHD8Mknnzg15wflLePaG8esDOPRLYu90WjExIkTbX6gABAfHw9FUWA0Gl2UmWtcunQJABAXF2cTj4iIwOjRo63tnk5RFFy/ft1aSOrr69Hd3a163wEBAYiNjfW4cSDTuPaGMett49Eti73JZEJ4eLgqbjAYAMBjZkB66VsHve/938tgMHjNz+PAgQNobW3FvHnzAHjf+5ZpXHvD787bxqNbFvvOzk74+/ur4oGBgQAAs9ns7JRcqrOzEwBUHzOBuz+TvnZP1tjYiPXr12P69OlIT08H4H3vW6Zx7em/O28cj25Z7IOCgtDV1aWK9x0MfQeHLPoeRGCxWFRtZrPZqQ/gGAwmkwlLly5FaGgoSkpK4ONzd1h62/uWaVx78u/OW8ejWxZ7rY9EfR+jRB+FvVnfx8a+938vrVMDnqK9vR25ublob2/Hjh07bD4ie9v7lmlce+rvzpvHo1sW+ylTpqCpqQkdHR028YsXL1rbZRIbGwsAqK2ttYm3traipaXF2u5pzGYzli1bhitXrmDbtm2YNGmSTXt0dDT8/PxU79tiscBoNHrc+5ZpXHvimPX28eiWxT4lJQVdXV346KOPrDGLxYJ9+/YhMTERERERLszO+aKiojBp0iRUVFTYPDl+9+7d8PHxwdy5c12Y3cD09PRg5cqVuHDhAkpKSjBt2jRVn+HDh2PGjBmorKy0KZCVlZW4ffs2UlJSnJnyA5NpXHvamJVhPPquXbt2rauT+LrRo0ejoaEB5eXl6OjowNWrV1FYWIjGxkYUFRVhzJgxrk5RV1u2bMHZs2dx5swZXL58GT4+Pqivr0d9fT3i4+MBAJGRkdi5cyeqq6thsViwf/9+lJWVITs7G5mZmS5+B44rLCzEb37zGzzzzDMYN26c9f3W19fj6tWr1lnV5MmTsWvXLlRVVaG3txdHjx5FSUkJZs2ahRUrVrj4XTjGm8a1t41ZGcaj2z5w3Gw2491338XBgwdx8+ZNxMTEIC8vDzNnznR1arrTuuU8MjISn376qfX/R48eRWlpKRobGzFq1CgsWLAAy5cvh5+fn7NS1U1OTg7OnDkjbPv6+/78889RXFyMS5cuISQkBKmpqcjLy8OwYcOcla5uvGVce9uYlWE8um2xJyIi/bjlOXsiItIXiz0RkQRY7ImIJMBiT0QkARZ7IiIJsNgTEUmAxZ6ISAIs9kREEmCxJyKSAIs9EZEE/g/PBym8c13wNQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(10):\n",
" fig, axes = plt.subplots(ncols=2)\n",
" \n",
" ax1, ax2 = axes\n",
" ax1.imshow(x_res[i].reshape(mp.fmap_shape))\n",
" \n",
" ax2.imshow(x_train[i])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2bb8210d-9ee6-444b-8fde-9b8866695a02",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b758d983-f605-45ba-81e8-922932633887",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b2bb030-bb6e-4c5f-ae42-00360cd340e7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "00c85410-17f8-4da2-b4be-4267209b9de0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "01df7ae0-861d-4859-a49c-048b4688f2e4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ab3322b-ab34-481a-97f2-e85c5224c600",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "44b9086f-ea0e-404e-90a9-57ba41625ea3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d79a53e-a95d-4e80-aa0d-456dbd487c79",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "651faf33-99c2-4914-989c-33dae4446a14",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "16f6d117-b34c-4e6a-afcf-2fea27a6b147",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d82bfea-eb4a-49dc-b27c-89f3d99b2013",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "79fe63f5-0855-49ce-bf40-fee9ec237ad7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b40ee053-95e2-4649-a6c6-db88a00d4dec",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e42fd11f-3d42-40b3-b112-144c37707605",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c64ac5ee-c7e1-4be7-b35f-5f04ff53d81a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1c7116e-dc01-4647-84bf-dbc7801bca50",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c70e85c0-804d-49ce-b939-e22442c1f396",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}