Fore-Back_subtraction_experiments.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Fore-Back_subtraction_experiments.ipynb","provenance":[],"collapsed_sections":[]},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.7"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"KtbfdaaTpfWL","colab_type":"code","outputId":"426f49f5-339f-4064-c249-817a8ae1ca48","executionInfo":{"status":"ok","timestamp":1564817517356,"user_tz":-330,"elapsed":36856,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":138}},"source":["from google.colab import drive\n","drive.mount('/gdrive')\n","%cd /gdrive"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /gdrive\n","/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"u7Po-UiJpOB2","colab_type":"code","outputId":"476290e3-7fd2-47e1-f821-a0c7f355e47b","executionInfo":{"status":"ok","timestamp":1563804365073,"user_tz":-330,"elapsed":2253499,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":370}},"source":["import numpy as np \n","\n","def Thresh(M,thresh):\n","    return np.array(M>thresh).astype('float')*M\n","\n","\n","def altproj(M,r,beta,eps=0.005):\n","\n","    u,s,v = np.linalg.svd(M,full_matrices=False)\n","    thresh = beta*s[0]\n","    \n","    L = np.zeros(M.shape)\n","    S = Thresh(M-L,thresh)\n","    #print np.linalg.norm(M-S,ord=None)\n","    #print M.shape[1]*beta*np.linalg.norm(M-S,ord=None)/eps\n","    T = 10*np.log2(M.shape[1]*beta*np.linalg.norm(M-S,ord=None)/eps)\n","    for k in range(1,r+1):\n","        for t in range(0,int(np.ceil(T+1))):\n","            #print 'hi'\n","            u,s,v = np.linalg.svd(M-S,full_matrices=False)\n","            thresh = beta*(s[k] + s[k-1]*(0.5)**t)\n","            L = np.dot(np.dot(u,np.diag(np.concatenate((s[:k],np.zeros(s[k:].shape))))),v)\n","            S = Thresh(M-L,thresh)\n","            u,s,v = np.linalg.svd(L,full_matrices=False)\n","        if beta*s[k]<eps/(2*M.shape[1]): return L,S\n","    return L,S\n","\n","\n","#def main():\n","\n","temp = range(-9,-8)\n","beta_range = [2**i for i in temp]\n","r_range = [1]\n","dmdt_o = np.load('/gdrive/My Drive/periodic_tf/data/all/X_2d.npy')\n","labels = np.load('/gdrive/My Drive/periodic_tf/data/all/y.npy')\n","classes = [1,2,4,5,6,8,13]\n","for classnum in classes:\n","  mask = np.in1d(labels,classnum)\n","  dmdt = dmdt_o[mask]\n","  print(dmdt.shape,labels.shape)\n","  m,n = dmdt.shape[1]*dmdt.shape[2],dmdt.shape[0]\n","  M = np.zeros((dmdt.shape[1]*dmdt.shape[2],dmdt.shape[0]))\n","  for i in range(dmdt.shape[0]):\n","    M[:,i] = dmdt[i].flatten().astype('float32')#/np.sum(dmdt[i])\n","  if m<n: M = M.transpose()\n","  print(\"the frobenius norm of the matrix M is %f\"%(np.linalg.norm(M,'fro')))\n","  for beta in beta_range:\n","    for r in r_range:\n","      #print('hi')            \n","      L,S = altproj(M,r,beta)\n","      err = np.linalg.norm(M-L-S,ord='fro')\n","      if r_range.index(r)==0 and beta_range.index(beta)==0:err_best = err\n","      print(\"error for beta:%f and rank:%f is %f\"%(beta,r,err))\n","      if err<err_best or err==err_best: \n","        L_ = L\n","        S_ = S\n","  if m<n:\n","    L_ = L_.transpose()\n","    S_ = S_.transpose()\n","  np.save('/gdrive/My Drive/periodic_tf/data/all/L_'+str(classnum)+'_trainonly.npy',L_)\n","  np.save('/gdrive/My Drive/periodic_tf/data/all/S_'+str(classnum)+'_trainonly.npy',S_)\n","    "],"execution_count":0,"outputs":[{"output_type":"stream","text":["(30593, 22, 24) (46821,)\n","the frobenius norm of the matrix M is 9049.283836\n","error for beta:0.001953 and rank:1.000000 is 12.020220\n","(4658, 22, 24) (46821,)\n","the frobenius norm of the matrix M is 3875.052629\n","error for beta:0.001953 and rank:1.000000 is 1.854667\n","(2420, 22, 24) (46821,)\n","the frobenius norm of the matrix M is 2076.084902\n","error for beta:0.001953 and rank:1.000000 is 1.114171\n","(5433, 22, 24) (46821,)\n","the frobenius norm of the matrix M is 3358.748340\n","error for beta:0.001953 and rank:1.000000 is 2.379772\n","(500, 22, 24) (46821,)\n","the frobenius norm of the matrix M is 1005.445382\n","error for beta:0.001953 and rank:1.000000 is 0.290144\n","(1514, 22, 24) (46821,)\n","the frobenius norm of the matrix M is 2411.495188\n","error for beta:0.001953 and rank:1.000000 is 0.518247\n","(512, 22, 24) (46821,)\n","the frobenius norm of the matrix M is 869.969104\n","error for beta:0.001953 and rank:1.000000 is 0.213678\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vyYqhz4BSkQd","colab_type":"code","outputId":"672e5962-8b9f-4a84-c26f-2979676ecc35","executionInfo":{"status":"ok","timestamp":1563804418941,"user_tz":-330,"elapsed":1258,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["np.where(labels==13)[0].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(512,)"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"V-y0fmFw9Y5P","colab_type":"code","colab":{}},"source":["L_all = np.zeros((22*24,0))\n","for classnum in classes:\n","  L_all = np.concatenate((L_all,np.load('/gdrive/My Drive/periodic_tf/data/all/L_'+str(classnum)+'_trainonly.npy')), axis=1)\n","np.save(\"/gdrive/My Drive/periodic_tf/data/all/L_all.npy\",L_all)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vy_pjke5pOCT","colab_type":"code","outputId":"36556f48-3c47-418c-f0f9-2faf16332cb7","executionInfo":{"status":"ok","timestamp":1563804468800,"user_tz":-330,"elapsed":1890,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":302}},"source":["L = np.load('/gdrive/My Drive/periodic_tf/data/all/L_all.npy')\n","X = np.load('/gdrive/My Drive/periodic_tf/data/all/X_2d.npy')\n","y = np.load('/gdrive/My Drive/periodic_tf/data/all/y.npy')\n","max_ = 0\n","print(L.shape)\n","print(X.shape)\n","max_back = np.zeros((1,22,24))\n","for i in range(L.shape[1]):\n","    if np.sum(L[:,i]) > max_:\n","        max_ = np.sum(L[:,i])\n","        max_back = L[:,i].reshape((1,22,24))\n","\n","for i in range(X.shape[0]):\n","    X[i] = (X[i] - max_back).clip(min=0)\n","    \n","np.save('/gdrive/My Drive/periodic_tf/data/all/X_2d_cad_trainonly.npy',X)\n","np.save('/gdrive/My Drive/periodic_tf/data/all/y_cad_trainonly.npy',y)\n","np.save('/gdrive/My Drive/periodic_tf/data/all/max_cad_back.npy',max_back)\n","\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","plt.imshow(max_back[0])\n","plt.savefig('/gdrive/My Drive/periodic_tf/data/all/max_cad_back.png')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(528, 45630)\n","(46821, 22, 24)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARwAAAD8CAYAAAClxxvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nne0qnsLkAgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"fAxwO0boXoGA","colab_type":"code","outputId":"045fc471-7e74-4bf4-e37d-ca373f38d9fb","executionInfo":{"status":"ok","timestamp":1563804541507,"user_tz":-330,"elapsed":2069,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":386}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","%matplotlib inline\n","\n","classes = [1,2,4,5,6,8,13]\n","\n","for classnum in classes:\n","  L = np.load('/gdrive/My Drive/periodic_tf/data/all/L_'+str(classnum)+'_trainonly.npy')\n","  # X = np.load('/gdrive/My Drive/periodic_tf/data/all/X_2d.npy')\n","  # y = np.load('/gdrive/My Drive/periodic_tf/data/all/y.npy')\n","  max_ = 0\n","  print(L.shape)\n","  # print(X.shape)\n","  max_back = np.zeros((1,22,24))\n","  for i in range(L.shape[1]):\n","    if np.sum(L[:,i]) > max_:\n","      max_ = np.sum(L[:,i])\n","      max_back = L[:,i].reshape((1,22,24))\n","  \n","  plt.imshow(max_back[0])\n","  plt.savefig('/gdrive/My Drive/periodic_tf/data/all/max_cad_back_'+str(classnum)+'.png')\n","  np.save('/gdrive/My Drive/periodic_tf/data/all/max_cad_back_'+str(classnum)+'.npy',max_back)\n","\n","  # for i in range(X.shape[0]):\n","  #     X[i] = (X[i] - max_back).clip(min=0)\n","\n","  # np.save('/gdrive/My Drive/periodic_tf/data/all/X_2d_cad_trainonly.npy',X)\n","  # np.save('/gdrive/My Drive/periodic_tf/data/all/y_cad_trainonly.npy',y)\n","  # np.save('/gdrive/My Drive/periodic_tf/data/all/max_cad_back_'+str(classnum)+'.npy',max_back)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(528, 30593)\n","(528, 4658)\n","(528, 2420)\n","(528, 5433)\n","(528, 500)\n","(528, 1514)\n","(528, 512)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARwAAAD8CAYAAAClxxvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"nufRhe_vSWZx","colab_type":"code","outputId":"0b24c14a-33cc-44c9-cdb5-95e43d6fab24","executionInfo":{"status":"ok","timestamp":1563804607602,"user_tz":-330,"elapsed":1053,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","plt.imshow(X[2])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"kkFA7LHdpOCp","colab_type":"code","colab":{}},"source":["main()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TUGM1BIHXpWc","colab_type":"code","colab":{}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","% matplotlib inline\n","max_back = np.load('/gdrive/My Drive/periodic_tf/data/all/max_cad_back.npy')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"KnoClEpgXzfh","colab_type":"code","outputId":"24dc5b98-b72b-44ec-895d-534322e45ec3","executionInfo":{"status":"ok","timestamp":1563804714186,"user_tz":-330,"elapsed":1302,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(max_back[0])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7efe06563710>"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARwAAAD8CAYAAAClxxvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nne0qnsLkAgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"aECbzHI2bAMe","colab_type":"code","outputId":"d3d0a987-c77f-4d9c-d7dc-67cd649b53d7","executionInfo":{"status":"ok","timestamp":1563526387958,"user_tz":-330,"elapsed":869,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["plt.savefig(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back.png\")"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"Cu6KdBgRpODD","colab_type":"text"},"source":["## Classes done - 1,2,4,5,6,8,13"]},{"cell_type":"markdown","metadata":{"id":"Hdp0fDxGpODQ","colab_type":"text"},"source":["## Section for storing random objects of each class"]},{"cell_type":"code","metadata":{"id":"5jo3XeTHpODZ","colab_type":"code","colab":{}},"source":["import matplotlib.pyplot as plt\n","import os\n","\n","from pylab import * \n","%matplotlib inline"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"YrzXNxRgpOD5","colab_type":"code","colab":{}},"source":["L = np.load('L_%i_id.npy'%(1))\n","zeros = 0\n","for i in range(L.shape[1]):\n","    if np.linalg.norm(L[:,i])<=0.5:zeros+=1\n","        \n","print zeros\n","print L.shape[1]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uCbuZ-xSpOEX","colab_type":"code","colab":{}},"source":["back= np.load('L_cad_trainonly.npy')\n","max_ = 0\n","max_back = np.zeros((1,23,24))\n","for i in range(back.shape[1]):\n","    if np.sum(back[:,i])>max_:\n","        max_back = back[:,i].reshape((1,23,24))\n","        max_ = np.sum(back[:,i])\n","        \n","imsave('cadence_back.png',max_back.reshape((23,24)),vmin=0,vmax=3)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Cd4u5PTJpOEr","colab_type":"code","colab":{}},"source":["dmdt_orig = np.load('X_2d_normalized.npy')\n","labels = np.load('y_normalized.npy')\n","ids = np.load('ids.npy')\n","classes_done = [1,2,4,5,6,8,13]\n","for i in classes_done:\n","    \n","    mask = np.in1d(labels,i)\n","    dmdt = dmdt_orig[mask]\n","    #ids  = ids[mask]\n","    total_objects = dmdt.shape[0]\n","    \n","    \n","    \n","    \n","    path = 'samples_normalized%i'%(i)\n","    if os.path.exists(path)==False:os.makedirs(path)\n","    \n","    \n","    objects = np.random.randint(total_objects,size = 10)\n","    for id in objects:\n","        \n","        imsave(path+'/%i_orig.png'%(id),dmdt[id],vmin=0,vmax=0.025)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"u3vYM9WXpOFD","colab_type":"code","outputId":"ff6ac723-870e-465c-a378-579f50349c62","executionInfo":{"status":"ok","timestamp":1563804776067,"user_tz":-330,"elapsed":2220,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from sklearn.cluster import KMeans\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","from sklearn.cluster import MiniBatchKMeans\n","\n","\n","# dmdt = np.load('X_2d_id.npy')\n","# labels = np.load('y_id.npy')\n","# ids = np.load('ids.npy')\n","\n","dmdt = np.load('/gdrive/My Drive/periodic_tf/data/all/X_2d_cad_trainonly.npy')\n","labels = np.load('/gdrive/My Drive/periodic_tf/data/all/y_cad_trainonly.npy')\n","\n","print(set(labels))\n","\n","class_ = 2\n","\n","mask = np.in1d(labels,class_)\n","temp = dmdt[mask]\n","X = np.zeros((temp.shape[0],22*24))\n","for i in range(temp.shape[0]):\n","  X[i,:] = temp[i].reshape((22*24))\n","\n","kmeans = KMeans(n_clusters=2,random_state=0).fit(X)\n","minibatchkmeans = MiniBatchKMeans(n_clusters=2,random_state=0).fit(X)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"6XpXA0Lk0yYK","colab_type":"code","outputId":"6d95e9ef-ac8e-442c-d9c5-970398d9ed90","executionInfo":{"status":"ok","timestamp":1563804779323,"user_tz":-330,"elapsed":1285,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["kmeans.inertia_/temp.shape[0]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["167.74767532283082"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"eL2LBeno3C1E","colab_type":"code","outputId":"75225613-bed0-42b6-f84a-677b677ffabd","executionInfo":{"status":"ok","timestamp":1563804783639,"user_tz":-330,"elapsed":2230,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["kmeans.inertia_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["781368.6716537459"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"zQ8zoRgQ1ALJ","colab_type":"code","outputId":"a1074444-7060-4ce2-93ae-7457b7bd356e","executionInfo":{"status":"ok","timestamp":1563804787604,"user_tz":-330,"elapsed":1010,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["minibatchkmeans.inertia_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["783022.7622752588"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"6Nk97U78so8c","colab_type":"code","outputId":"579748e2-7cae-4cd6-b7a7-770f42cae626","executionInfo":{"status":"ok","timestamp":1563804790331,"user_tz":-330,"elapsed":873,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(kmeans.cluster_centers_[0].reshape((22,24)))\n","#plt.savefig('/gdrive/My Drive/periodic_tf/data/all/kmeans/EW/cluster_center_1.png')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"wvM4ug21tn64","colab_type":"code","outputId":"452adeb3-1a92-4f07-b7a5-cf530daa2b19","executionInfo":{"status":"ok","timestamp":1563804794515,"user_tz":-330,"elapsed":905,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(kmeans.cluster_centers_[1].reshape((22,24)))\n","#plt.savefig('/gdrive/My Drive/periodic_tf/data/all/kmeans/EW/cluster_center_2.png')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7efdf737c7b8>"]},"metadata":{"tags":[]},"execution_count":21},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARwAAAD8CAYAAAClxxvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZtk4cMwsm/8HsuWFnfQn36MAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"1rPh_KbK0gbH","colab_type":"code","outputId":"f7eafe3d-497d-417a-a923-f0388918ee59","executionInfo":{"status":"ok","timestamp":1563633538248,"user_tz":-330,"elapsed":1746,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(kmeans.cluster_centers_[2].reshape((22,24)))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fab84104710>"]},"metadata":{"tags":[]},"execution_count":34},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARwAAAD8CAYAAAClxxvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"VsJq_XLNpOFb","colab_type":"code","colab":{}},"source":["X = np.load('X_2d.npy')\n","y = np.load('y.npy')\n","for i in range(X.shape[0]):\n","    X[i,10:12,:] = np.zeros((2,24))\n","np.save('X_2d_blanked_2.npy',X)\n","np.save('y_blanked_2',y)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"joerXa61pOF8","colab_type":"code","colab":{}},"source":["from matplotlib import pyplot\n","\n","X = np.load('X_2d_test.npy')\n","y = np.load('y_test.npy')\n","X_miss = np.load('X_2d_test_fabian_normal_misclassified.npy')\n","y_miss = np.load('y_test_fabian_normal_misclassified.npy')\n","\n","classes = [1,2,4,5,6,8,13]\n","\n","for i in range(7):\n","    mask = np.in1d(y,i)\n","    temp = X[mask]\n","    \n","    mask_miss = np.in1d(y_miss,float(i))\n","    temp_miss = X_miss[mask_miss]\n","    \n","    hist = []\n","    for j in range(temp.shape[0]):\n","        hist.append(np.sum(temp[j]))\n","    \n","    hist_miss = []\n","    for j in range(temp_miss.shape[0]):\n","        hist_miss.append(np.sum(temp_miss[j]))\n","    pyplot.figure(figsize=(10,8))\n","    pyplot.hist(hist,bins=20,color='white',label='Total')\n","    pyplot.hist(hist_miss,bins=20,label='Misclassified')\n","    pyplot.suptitle('Class %i'%(classes[i]),fontsize=25,fontweight='bold')\n","    pyplot.xlabel('Number of dmdt pairs',fontsize=25,fontweight='bold')\n","    pyplot.ylabel('Number',fontsize=25,fontweight='bold')\n","    pyplot.tick_params(labelsize=25)\n","    pyplot.legend(loc='upper right')\n","    pyplot.savefig('class%i'%(classes[i]))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wYHS6c0_pOGW","colab_type":"code","colab":{}},"source":["temp1 = np.load('X_2d.npy')\n","y = np.load('y.npy')\n","classes = [1,2,4,5,6,8,13]\n","for i in range(7):\n","    mask = np.in1d(y,classes[i])\n","    temp = temp1[mask]\n","    print temp.shape\n","    sum_ = np.zeros((1,temp.shape[1],temp.shape[2]))\n","    for j in range(temp.shape[0]):sum_+=temp[j]\n","    if classes[i]>=6:imsave('stacked_class%i.png'%(classes[i]),sum_.reshape((23,24)),vmin=0,vmax=4*temp.shape[0])\n","    else:imsave('stacked_class%i.png'%(classes[i]),sum_.reshape((23,24)),vmin=0,vmax=4*temp.shape[0])\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Cfin6RUh3uep","colab_type":"code","outputId":"914219e9-fcd5-4df9-b67a-87df4ab35c59","executionInfo":{"status":"ok","timestamp":1564817551981,"user_tz":-330,"elapsed":20117,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":356}},"source":["!pip3 install scipy==1.1.0\n","!pip install texttable"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Collecting scipy==1.1.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/0b/f163da98d3a01b3e0ef1cab8dd2123c34aee2bafbb1c5bffa354cc8a1730/scipy-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (31.2MB)\n","\u001b[K     |████████████████████████████████| 31.2MB 64.7MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scipy==1.1.0) (1.16.4)\n","\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n","Installing collected packages: scipy\n","  Found existing installation: scipy 1.3.0\n","    Uninstalling scipy-1.3.0:\n","      Successfully uninstalled scipy-1.3.0\n","Successfully installed scipy-1.1.0\n","Collecting texttable\n","  Downloading https://files.pythonhosted.org/packages/82/a8/60df592e3a100a1f83928795aca210414d72cebdc6e4e0c95a6d8ac632fe/texttable-1.6.2.tar.gz\n","Building wheels for collected packages: texttable\n","  Building wheel for texttable (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for texttable: filename=texttable-1.6.2-cp36-none-any.whl size=10654 sha256=0a7139d0c307fed43b75b8fb49babf73bebefce12c73a80842ba3f8ef5a72cf9\n","  Stored in directory: /root/.cache/pip/wheels/51/d1/d6/dfbe4eb3c468832f7fbe4bd27f9875fa97277cabed8fb6715c\n","Successfully built texttable\n","Installing collected packages: texttable\n","Successfully installed texttable-1.6.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3shvkdXTxioY","colab_type":"code","outputId":"308cc68c-2675-4246-8ba9-4879ef481ac2","executionInfo":{"status":"ok","timestamp":1564817559492,"user_tz":-330,"elapsed":5334,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import configparser\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from numpy import genfromtxt\n","from six.moves import cPickle\n","import os\n","import matplotlib.colors as mcolors\n","from mpl_toolkits.axes_grid1 import make_axes_locatable\n","import statistics as stats\n","import glob\n","from texttable import Texttable\n","\n","#import matplotlib\n","#matplotlib.use('Agg')\n","\n","import copy\n","from sklearn import metrics\n","import seaborn\n","import tensorflow as tf\n","import keras\n","import keras.backend.tensorflow_backend as K\n","from keras.models import Sequential\n","from keras.layers import Conv2D\n","from keras.layers import MaxPooling2D\n","from keras.layers import Dropout\n","from keras.layers import Dense, Activation\n","from keras.layers import Flatten\n","from keras.engine.topology import Input\n","from keras.optimizers import Adam\n","from keras import regularizers\n","from keras.models import load_model\n","from sklearn.metrics import accuracy_score\n","from sklearn.model_selection import train_test_split\n","from sklearn import preprocessing\n","\n","from vis.visualization import visualize_activation, visualize_saliency, visualize_cam\n","from vis.utils import utils\n","from keras import activations"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"N2Orq6BZx3hk","colab_type":"code","outputId":"06202ac5-559e-4c4f-bb59-42d239a89cd0","executionInfo":{"status":"ok","timestamp":1564817567035,"user_tz":-330,"elapsed":6180,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":406}},"source":["model = tf.keras.models.Sequential()\n","\n","model.add(tf.keras.layers.InputLayer(input_shape=(22,24,1)))\n","model.add(tf.keras.layers.Conv2D(filters=16, kernel_size=(5, 5), activation=\"relu\", \n","                 #data_format = \"channels_first\",\n","                 kernel_regularizer=regularizers.l2(0.01), \n","                 #input_shape=(23,24,1),\n","                 padding='valid',\n","                 name=\"conv2d1\"))\n","model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='valid', \n","                 #data_format = \"channels_first\",\n","                 name=\"maxpool2d1\",\n","                 strides=(2,2)))\n","#model.add(Dropout(rate=0.1))\n","model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(5, 5), activation=\"relu\", \n","                 kernel_regularizer=regularizers.l2(0.01),\n","                 padding='valid', \n","                 #data_format = \"channels_first\",\n","                 name=\"conv2d2\"))\n","#model.add(Conv2D(filters=256, kernel_size=(5, 5), activation=\"relu\", kernel_regularizer=regularizers.l2(0.01)))\n","model.add(tf.keras.layers.Flatten(\n","    #data_format = \"channels_first\", \n","    name=\"flatten\"))\n","#model.add(Dense(units=512, kernel_regularizer=regularizers.l2(0.01)))\n","#model.add(Dropout(rate=0.5))\n","#model.add(Dense(units=512, kernel_regularizer=regularizers.l2(0.01)))\n","model.add(tf.keras.layers.Dense(units=7, activation=\"softmax\", name=\"preds\"))\n","\n","model.compile(optimizer='adam',\n","              loss='sparse_categorical_crossentropy',\n","              metrics=['accuracy'])\n","\n","model.load_weights('/gdrive/My Drive/periodic_tf/code/experiments/cnn_22_w o_back_subtraction_float_bins/model.h5')\n","#model.load_weights('model.h5')\n","model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING: Logging before flag parsing goes to stderr.\n","W0803 07:32:42.213408 140488809736064 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Call initializer instance with the dtype argument instead of passing it to the constructor\n"],"name":"stderr"},{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d1 (Conv2D)             (None, 18, 20, 16)        416       \n","_________________________________________________________________\n","maxpool2d1 (MaxPooling2D)    (None, 9, 10, 16)         0         \n","_________________________________________________________________\n","conv2d2 (Conv2D)             (None, 5, 6, 64)          25664     \n","_________________________________________________________________\n","flatten (Flatten)            (None, 1920)              0         \n","_________________________________________________________________\n","preds (Dense)                (None, 7)                 13447     \n","=================================================================\n","Total params: 39,527\n","Trainable params: 39,527\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"uXtWiWMbyVFI","colab_type":"code","colab":{}},"source":["# X = np.load(\"/gdrive/My Drive/periodic_tf/data/all/X_2d.npy\")\n","# X = X.reshape((X.shape[0], X.shape[1], X.shape[2], 1))\n","# y = np.load(\"/gdrive/My Drive/periodic_tf/data/all/y.npy\")\n","# mask = np.in1d(y, np.array([1,2,4,5,6,8,13]))\n","# y = y[mask]\n","# X = X[mask]\n","\n","X = np.load(\"/gdrive/My Drive/periodic_tf/code/experiments/cnn_22_w o_back_subtraction_float_bins/X_test.npy\")\n","y = np.load(\"/gdrive/My Drive/periodic_tf/code/experiments/cnn_22_w o_back_subtraction_float_bins/y_test.npy\")\n","\n","b1 = np.load(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back_1.npy\")[0]\n","b2 = np.load(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back_2.npy\")[0]\n","b4 = np.load(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back_4.npy\")[0]\n","b5 = np.load(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back_5.npy\")[0]\n","b6 = np.load(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back_6.npy\")[0]\n","b8 = np.load(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back_8.npy\")[0]\n","b13 = np.load(\"/gdrive/My Drive/periodic_tf/data/all/max_cad_back_13.npy\")[0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7v1f3eqG4IFL","colab_type":"code","outputId":"ec3d513e-fe98-4a4c-da00-0227f605a78b","executionInfo":{"status":"ok","timestamp":1564819365264,"user_tz":-330,"elapsed":716,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(9126, 22, 24, 1)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"CnF9vkQ2zBjO","colab_type":"code","outputId":"47865463-4e99-425d-88f6-4bb1a89dec31","executionInfo":{"status":"ok","timestamp":1564783271849,"user_tz":-330,"elapsed":3563,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":319}},"source":["import random \n","i = random.randint(0, X.shape[0])\n","print(\"id:\",i)\n","print(\"class:\",y[i])\n","print(\"original probability:\", model.predict(X)[i])\n","print(\"b1 probability:\", model.predict((X[i,:,:,0]-b1).reshape(1, 22, 24, 1))[0])\n","print(\"b2 probability:\", model.predict((X[i,:,:,0]-b2).reshape(1, 22, 24, 1))[0])\n","print(\"b4 probability:\", model.predict((X[i,:,:,0]-b4).reshape(1, 22, 24, 1))[0])\n","print(\"b5 probability:\", model.predict((X[i,:,:,0]-b5).reshape(1, 22, 24, 1))[0])\n","print(\"b6 probability:\", model.predict((X[i,:,:,0]-b6).reshape(1, 22, 24, 1))[0])\n","print(\"b8 probability:\", model.predict((X[i,:,:,0]-b8).reshape(1, 22, 24, 1))[0])\n","print(\"b13 probability:\", model.predict((X[i,:,:,0]-b13).reshape(1, 22, 24, 1))[0])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["id: 25389\n","class: 5\n","original probability: [1.4081006e-01 3.1128522e-05 5.8445606e-02 7.7623147e-01 1.0013879e-02\n"," 1.4459295e-02 8.5892752e-06]\n","b1 probability: [5.7529800e-37 0.0000000e+00 2.0680070e-15 7.6106021e-35 0.0000000e+00\n"," 2.6740075e-29 1.0000000e+00]\n","b2 probability: [1.3204959e-01 8.2088780e-01 1.0057952e-02 3.6956146e-02 1.3754397e-05\n"," 3.4650675e-05 4.3011184e-08]\n","b4 probability: [6.0336769e-01 4.8279013e-03 2.4809856e-02 3.5929039e-01 1.5785071e-03\n"," 6.1195320e-03 6.0874891e-06]\n","b5 probability: [2.1821710e-01 7.3895192e-01 1.5436496e-02 2.7152048e-02 2.6186388e-05\n"," 2.1607871e-04 1.6109934e-07]\n","b6 probability: [1.4422817e-02 1.3768260e-04 3.5603538e-02 8.9921772e-01 2.0023763e-04\n"," 5.0399013e-02 1.8942546e-05]\n","b8 probability: [1.2624075e-05 6.6476218e-07 2.6672666e-05 1.3290472e-04 4.0039051e-08\n"," 9.9982697e-01 2.4896323e-07]\n","b13 probability: [9.0573519e-01 6.5300272e-05 1.3076155e-03 9.2844658e-02 6.2227555e-06\n"," 4.0950155e-05 2.4496098e-09]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LoKvSZyNJN-K","colab_type":"code","outputId":"7559befb-4310-4862-dc7f-882e8d0bcf39","executionInfo":{"status":"ok","timestamp":1564849227112,"user_tz":-330,"elapsed":3246705,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["pd={1:0,2:1,4:2,5:3,6:4,8:5,13:6}\n","cls = 1\n","mis_cls = 8\n","y_cls = np.where(y==cls)[0]\n","res = []\n","for i in y_cls:\n","  og = model.predict(X)[i]\n","  b1p = model.predict((X[i,:,:,0]-b1).reshape(1, 22, 24, 1))[0]\n","  b2p = model.predict((X[i,:,:,0]-b2).reshape(1, 22, 24, 1))[0]\n","  b4p = model.predict((X[i,:,:,0]-b4).reshape(1, 22, 24, 1))[0]\n","  b5p = model.predict((X[i,:,:,0]-b5).reshape(1, 22, 24, 1))[0]\n","  b6p = model.predict((X[i,:,:,0]-b6).reshape(1, 22, 24, 1))[0]\n","  b8p = model.predict((X[i,:,:,0]-b8).reshape(1, 22, 24, 1))[0]\n","  b13p = model.predict((X[i,:,:,0]-b13).reshape(1, 22, 24, 1))[0]\n","  if np.argmax(og)!=pd[cls] and max(b1p[pd[cls]], b2p[pd[cls]], b4p[pd[cls]], b5p[pd[cls]], b6p[pd[cls]], b8p[pd[cls]], b13p[pd[cls]]) == b8p[pd[cls]]:\n","    print(i)\n","    res.append(i)\n","res = np.array(res)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["489\n","5708\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jI2H5tg-d9w_","colab_type":"code","outputId":"41fbe4e0-61aa-419d-e52f-1348806f2987","executionInfo":{"status":"ok","timestamp":1564849258888,"user_tz":-330,"elapsed":1228,"user":{"displayName":"Meet Gandhi 15110049","photoUrl":"https://lh6.googleusercontent.com/-5eaxj6GKBiI/AAAAAAAAAAI/AAAAAAAAAqg/CNTOmCExKzg/s64/photo.jpg","userId":"04375499159180660557"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["res.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2,)"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"markdown","metadata":{"id":"DBYryL-rUoyz","colab_type":"text"},"source":["true class = 5, Prediction = 1: 10/228\\\n","true class = 1, Prediction = 5: 211/226\\\n","true class = 2, Prediction = 1: 43/218\\\n","true class = 8, Prediction = 1: 43/216\\\n","true class = 1, Prediction = 2: 1/116\\\n","true class = 4, Prediction = 5: 35/92\\\n","true class = 4, Prediction = 1: 21/84\\\n","true class = 1, Prediction = 4: 27/69\\\n","true class = 6, Prediction = 5: 0/60\\\n","true class = 1, Prediction = 8: 2/56\\\n","true class = 5, Prediction = 4: 12/46\\\n","true class = 8, Prediction = 5: 0/23\\\n","true class = 6, Prediction = 1: 0/12\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"YH1JOYuBiiuU","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}