3. Visualization

3.3 Display the test set and predictions

In [15]:
# set up the figure
fig = plt.figure(figsize=(15, 7))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

# plot the digits: each image is 8x8 pixels
for i in range(120):
    ax = fig.add_subplot(6, 20, i + 1, xticks=[], yticks=[])
    ax.imshow(X_test[i,:].reshape((8,8)),cmap=plt.cm.gray_r, interpolation='nearest')
    
    # label the image with the target value
    ax.text(0, 7, str(prediction_values[i]))

3.2 Display the weights and biases of our model

In [16]:
# Input layer
weights0 = model.layers[0].get_weights()[0]
biases0 = model.layers[0].get_weights()[1]
print("Input layer weights",weights0.shape,":\n",weights0)
print("Input layer biases",biases0.shape,":\n",biases0)


# Output layer
weights1 = model.layers[1].get_weights()[0]
biases1 = model.layers[1].get_weights()[1]
print("\nOutput layer weights",weights1.shape,":\n",weights1)
print("Output layer biases",biases1.shape,":\n",biases1)
Input layer weights (64, 64) :
 [[-2.1028808e-09 -5.3369298e-10  1.9550455e-09 ...  1.6600994e-09
  -2.1651485e-09 -8.7832142e-10]
 [ 2.7757486e-02 -1.3005386e-02  3.9379567e-02 ... -1.7543668e-02
  -3.3369772e-03 -1.6613516e-03]
 [ 3.7941642e-02 -9.4091576e-03  5.3184878e-02 ... -2.3927499e-02
   2.5960287e-02  3.0849278e-02]
 ...
 [-4.7890082e-02 -4.7605634e-03 -8.6482704e-02 ... -6.2531151e-02
   4.8489153e-02 -5.9909574e-03]
 [-3.9722331e-02 -3.9657583e-03 -6.0278095e-02 ... -5.0083987e-02
   2.3547366e-02  4.1136377e-02]
 [-5.2029476e-02  5.7484183e-02 -1.2379432e-02 ... -1.6499287e-03
   1.8882798e-02  1.8333012e-02]]
Input layer biases (64,) :
 [-0.03881287 -0.1402899   0.18381447 -0.03819283  0.01619301  0.01251267
  0.01010677 -0.03594004  0.03503983 -0.08878161  0.0450852  -0.04533898
  0.00848197 -0.00829253  0.08814015 -0.03260835 -0.01184387 -0.04768205
  0.00207892 -0.06210351 -0.0594545  -0.12247932  0.00155093  0.07375896
 -0.00626616 -0.07855942  0.10352132 -0.02821794 -0.14823328 -0.03630821
  0.07926352  0.00883728  0.03490223  0.01206386  0.06858793 -0.1117122
 -0.02231753 -0.06526469 -0.07552753  0.06490603 -0.02215363  0.07126758
  0.06361867  0.07557367 -0.00990338 -0.0344195   0.01694137  0.03400782
 -0.00696678 -0.06455978 -0.04860882  0.08262052  0.08750709  0.02970159
  0.05849162  0.03771083 -0.13368174 -0.10498384 -0.06281371 -0.00114712
  0.08035284  0.01309204 -0.00401347 -0.02066582]

Output layer weights (64, 10) :
 [[-2.65090734e-01 -6.01535499e-01 -4.54511881e-01  5.14958739e-01
  -4.82944936e-01  7.14010835e-01 -5.45012057e-01  7.14143932e-01
  -3.30482572e-01  4.23351079e-01]
 [-2.54181772e-01  6.71864152e-01 -5.09683862e-02 -2.77923465e-01
  -1.77550375e-01 -8.57776031e-02 -5.68282492e-02 -2.70106614e-01
   1.99825346e-01 -2.66463310e-02]
 [-3.73399913e-01 -4.39940870e-01 -4.24822599e-01  3.89005065e-01
   6.49582386e-01  4.88269925e-01 -5.86201489e-01  4.55343992e-01
  -3.01884055e-01 -1.07966915e-01]
 [-2.12109491e-01 -1.24209620e-01 -1.04344018e-01 -1.46536216e-01
  -7.52012879e-02  1.64258182e-01  4.18669954e-02 -2.23789573e-01
  -1.32602826e-01  2.10466385e-01]
 [ 3.02054733e-01 -2.88701892e-01  3.49720895e-01 -5.56491375e-01
   3.06912094e-01 -1.11288466e-01  1.48771256e-01  4.98234451e-01
   8.46636519e-02  4.93705720e-01]
 [-3.13310683e-01  4.04633224e-01  1.69898584e-01 -5.17215542e-02
   3.59504372e-01 -6.48287654e-01  9.51069966e-02 -2.65142083e-01
   4.99872304e-02  3.10349286e-01]
 [-2.69175619e-01 -2.02272087e-01 -2.73697555e-01  1.03308797e-01
   2.79351771e-01  8.50676075e-02 -1.88558057e-01  2.96333939e-01
  -1.60621420e-01 -1.42063826e-01]
 [ 2.40434244e-01  2.04004407e-01  7.45643303e-02 -3.60678077e-01
   2.51082003e-01 -1.70194656e-01 -9.43351090e-02 -2.58570999e-01
   1.77522108e-01 -5.64589165e-02]
 [ 8.97786468e-02  2.43767858e-01  3.54914814e-02  1.08622327e-01
  -4.15431380e-01  1.72060847e-01 -1.28356919e-01  1.79006875e-01
   1.18448585e-01  1.61212578e-01]
 [ 2.90321819e-02  1.25411034e-01 -1.76016182e-01  1.56518474e-01
  -1.07226953e-01 -3.17889810e-01  1.75660670e-01  6.27567768e-02
   2.64196366e-01  1.27651617e-01]
 [-3.11937898e-01 -4.41837996e-01  1.88951313e-01  3.87401849e-01
   5.28571680e-02 -4.85613734e-01 -1.97926000e-01  9.88691971e-02
   4.08104390e-01 -1.13315701e-01]
 [-3.69152039e-01  1.65363058e-01 -2.24642992e-01  3.06987256e-01
   1.76562145e-01 -2.49301314e-01  2.12277144e-01 -3.17201674e-01
   1.19928032e-01  2.68435806e-01]
 [-4.73656267e-01 -1.33675143e-01  3.39787900e-01 -4.87425655e-01
  -1.42906860e-01  5.83704650e-01 -4.54574943e-01 -4.49821800e-01
   1.42403811e-01  2.75043011e-01]
 [-4.46240604e-03 -3.15916300e-01  3.06521773e-01  1.73225015e-01
  -6.93217039e-01  1.66016951e-01 -8.46123789e-03 -1.19386002e-01
   3.63065481e-01  4.63489965e-02]
 [-1.96026519e-01  2.52662331e-01  8.92736986e-02 -1.22146487e-01
   2.40576908e-01 -3.66436765e-02 -1.58838511e-01 -1.49111331e-01
  -1.08498044e-01 -2.03065619e-01]
 [-1.33418322e-01 -1.19949557e-01 -5.37450135e-01 -4.87319529e-01
  -7.77656734e-02  1.73473045e-01 -1.49083689e-01 -4.67740595e-01
   4.74498302e-01 -1.07506722e-01]
 [ 3.79630737e-02  3.41981351e-01  3.31728766e-03 -6.98043585e-01
   4.26943451e-01 -5.19249916e-01 -8.30766112e-02  3.43820632e-01
  -4.16297615e-01 -3.86550762e-02]
 [ 1.57236144e-01  2.75449574e-01  6.59114867e-02  2.83097997e-02
  -9.66273844e-02 -1.34563863e-01  1.70362994e-01 -1.54699668e-01
  -1.28254086e-01 -2.08144605e-01]
 [ 3.61964494e-01  2.88404524e-02 -3.79346848e-01  1.76500291e-01
  -4.17214215e-01  4.15114850e-01 -4.08264935e-01 -4.05044079e-01
  -1.73660800e-01  5.54608047e-01]
 [ 2.27539942e-01  1.31953567e-01 -1.73807759e-02 -1.34725183e-01
  -3.09706926e-01  2.99315810e-01  3.71459052e-02  2.63046712e-01
  -1.31728381e-01  5.61344326e-01]
 [ 1.26668319e-01 -3.82044166e-02 -1.12570569e-01 -2.41870567e-01
  -1.35042183e-02  1.40394285e-01  2.45447159e-01  2.75065303e-01
   3.06555629e-01 -1.63506582e-01]
 [-4.53936428e-01  4.29224849e-01 -4.53400791e-01  2.86042154e-01
  -3.69619697e-01  9.18682516e-02 -1.55414566e-01  1.38048157e-01
  -6.67719021e-02  7.84865394e-02]
 [ 6.18363976e-01 -1.15560487e-01 -2.42079616e-01 -3.77685159e-01
   3.58662575e-01 -3.30828547e-01 -6.19741023e-01  3.31198543e-01
  -3.16765279e-01 -1.32879198e-01]
 [ 1.94131002e-01 -6.10352457e-01 -4.20985281e-01  3.99625562e-02
   4.66239065e-01  9.14557055e-02  3.54423851e-01 -5.87681174e-01
   5.59869349e-01  4.94835913e-01]
 [ 5.08886099e-01 -2.48286247e-01  3.63162398e-01 -6.20039642e-01
  -6.71519458e-01  4.94383544e-01  1.54289931e-01 -2.66753882e-01
  -5.35539329e-01 -5.02743125e-01]
 [-3.17382485e-01  6.85701370e-01 -5.86549461e-01 -3.84330153e-01
   1.91239491e-01  1.05455220e-01  6.87375784e-01 -2.93262243e-01
  -3.47594656e-02 -3.83509248e-01]
 [ 2.45656207e-01 -3.20866019e-01 -2.59807050e-01 -3.50523174e-01
   1.81344762e-01 -1.92762583e-01  3.18332836e-02  2.75682181e-01
  -4.03940380e-02  5.52198291e-01]
 [ 3.47675622e-01  8.35438445e-02 -1.41443804e-01 -5.43996394e-01
   2.36149594e-01 -3.02985698e-01 -3.84737730e-01  2.25295663e-01
   3.02650541e-01  2.40382388e-01]
 [-3.88012350e-01  4.52696174e-01  5.33323810e-02 -3.42214137e-01
  -4.15918499e-01 -2.71192312e-01  2.10015685e-03  5.51977873e-01
  -1.57526895e-01 -6.22311592e-01]
 [-5.52968569e-02  4.97825116e-01 -2.30656698e-01 -5.95988184e-02
   4.96002436e-01  2.63834924e-01  4.24231619e-01  1.18593156e-01
  -5.82884550e-01 -2.32382789e-01]
 [ 2.27475926e-01 -2.96971440e-01  4.02708381e-01  2.53356189e-01
   4.10915852e-01 -6.06288351e-02  3.79332066e-01 -4.62717503e-01
  -2.99207509e-01 -3.19914430e-01]
 [-2.40762562e-01 -3.56623471e-01  5.79085425e-02  3.28329951e-01
  -2.05284268e-01 -2.09175721e-01  9.67958421e-02 -1.94893390e-01
  -4.25403804e-01  6.99282527e-01]
 [-9.65062827e-02  2.04985932e-01 -4.65749018e-03 -9.10849683e-03
   2.17300534e-01  1.30828217e-01  9.47233364e-02  1.85376659e-01
   8.19356218e-02  9.84469578e-02]
 [-5.89795224e-03 -5.11246741e-01  2.14916468e-02  2.44196713e-01
   4.32802409e-01  1.43542349e-01 -2.49522567e-01  1.29019275e-01
  -7.32926905e-01  7.68109620e-01]
 [-8.97122696e-02  1.66562676e-01 -1.48657396e-01 -9.00924504e-02
  -1.22612175e-02 -7.25013018e-02  1.94008976e-01  4.58735600e-03
  -1.29362956e-01  5.71717545e-02]
 [-1.43447611e-02  4.89602953e-01 -1.33147120e-01 -7.27303207e-01
   5.87245338e-02  3.57011966e-02 -3.60675037e-01  5.82522713e-02
   3.22566539e-01  1.39855266e-01]
 [-1.47999763e-01  3.26785654e-01  3.56223315e-01  5.05816221e-01
  -5.40055811e-01 -3.63686740e-01 -4.56856132e-01  1.41201820e-02
  -2.42634490e-01  5.57023644e-01]
 [-3.34293157e-01  2.27799535e-01 -1.88546926e-02 -8.01627219e-01
  -6.04952723e-02 -2.19879553e-01  5.14897108e-01 -1.81547582e-01
   4.83327210e-01 -5.61830461e-01]
 [-2.37584367e-01  5.44673204e-01  4.61030722e-01 -1.46324537e-03
  -6.70906246e-01 -5.24245560e-01  5.83666801e-01  9.06509459e-02
  -6.41790390e-01 -7.72740990e-02]
 [ 2.82270581e-01 -6.44361317e-01 -1.89929739e-01 -2.55403757e-01
   2.46125944e-02 -2.59001285e-01  4.92050111e-01 -4.56080198e-01
  -4.39315051e-01 -2.64725089e-01]
 [-1.77095860e-01 -1.24612175e-01  1.05146423e-01  1.42698467e-01
   2.32962087e-01 -4.30558203e-03  1.91399410e-01  1.05541877e-01
  -1.65201694e-01  1.52164832e-01]
 [ 2.12680131e-01 -2.41428137e-01 -2.05755323e-01  6.88338950e-02
  -2.73683697e-01  6.95124315e-03  1.60866797e-01  8.84860083e-02
   7.42844939e-02 -1.46100745e-01]
 [-2.64327288e-01 -2.25170389e-01  5.65476418e-02 -5.23217440e-01
   4.07214493e-01  4.79103684e-01 -1.26955779e-02  1.85295179e-01
  -2.35636353e-01 -5.07213831e-01]
 [ 3.40306908e-01 -3.16653252e-01  3.01561415e-01  1.70619115e-01
   4.35577214e-01 -5.89655697e-01  3.64301771e-01  5.65689981e-01
   4.75101084e-01 -6.40695035e-01]
 [-3.98784727e-01  5.21308035e-02 -5.68345964e-01 -1.15927473e-01
   4.83518153e-01 -1.59721017e-01 -3.18385303e-01  5.55218220e-01
  -7.32412219e-01  1.99367240e-01]
 [-4.65776352e-03  7.28881180e-01  7.50982314e-02 -1.14648409e-01
  -1.56990089e-03 -4.17313695e-01  5.01125157e-01 -3.53284001e-01
   3.90315115e-01 -4.56592530e-01]
 [ 2.97739148e-01  3.99202436e-01  1.22507587e-01 -1.71239659e-01
   1.16912432e-01  1.85821325e-01  1.04618417e-02 -4.71393049e-01
  -5.49355805e-01 -1.75597817e-01]
 [-2.20719725e-01  3.64678055e-02  3.43214571e-02 -1.57718718e-01
  -1.94619551e-01 -1.50586173e-01 -1.47221103e-01  1.49601027e-01
  -9.30423439e-02 -2.13038370e-01]
 [ 2.86432624e-01  2.57496927e-02 -3.37619185e-01 -5.63179612e-01
   2.68560737e-01  2.06990555e-01 -4.24228385e-02  2.78121382e-01
  -1.46085173e-01  1.96760654e-01]
 [ 7.80165419e-02  4.71286088e-01 -1.64977580e-01  1.22991661e-02
  -3.86211500e-02 -2.10363388e-01  5.27653806e-02 -1.61026523e-01
   4.35574532e-01  5.40746629e-01]
 [ 2.68884867e-01  3.94384295e-01 -3.12512547e-01 -3.56872678e-01
   5.83566129e-01 -2.26437733e-01  7.49505907e-02 -4.72056776e-01
   1.43054007e-02  3.67874980e-01]
 [ 3.78119409e-01 -8.13410521e-01 -2.67726511e-01 -3.26450765e-01
   4.20300305e-01 -3.83435041e-01  5.77706635e-01  6.21149182e-01
  -3.28779936e-01 -2.74426371e-01]
 [-1.19022094e-01 -1.08767435e-01 -1.71834096e-01 -2.19942138e-01
  -4.61870804e-02 -1.24967597e-01 -6.88656718e-02  6.71627745e-02
  -2.98520207e-01 -1.71969578e-01]
 [ 1.05906660e-02 -3.51249352e-02  1.62654296e-01 -2.37843081e-01
   5.18677175e-01  4.63030994e-01  2.08019048e-01 -4.55996186e-01
  -4.06295434e-02 -1.18819356e-01]
 [ 9.62857231e-02 -6.68023750e-02  1.09700494e-01  1.46077365e-01
  -3.33222240e-01 -5.63282192e-01 -3.85978132e-01 -1.12683058e-01
   2.91864932e-01  2.65779734e-01]
 [-1.27192542e-01 -5.48940480e-01  7.07132965e-02  6.00070596e-01
  -1.44990966e-01 -6.36783242e-01 -2.45468408e-01 -1.70052037e-01
   6.19255185e-01 -6.11817718e-01]
 [-3.05926293e-01  3.95834506e-01  6.99731186e-02 -3.22109938e-01
   2.19492763e-01  3.38688135e-01 -2.98560321e-01 -4.11475480e-01
  -1.32546768e-01 -4.00142729e-01]
 [-2.81069905e-01  6.13887787e-01  6.41016245e-01 -4.68535513e-01
   2.56699711e-01  1.20609347e-03 -5.10993659e-01  4.26912189e-01
  -4.87648696e-01 -4.69665229e-01]
 [-2.21514091e-01  5.59850395e-01 -2.69003034e-01 -4.02590573e-01
   2.77725849e-02 -5.24676740e-01 -2.83268601e-01  4.51035678e-01
   6.16234839e-01  4.91719157e-01]
 [-2.31868729e-01  4.00175124e-01 -1.42834842e-01  2.67122298e-01
   5.52073419e-01  1.48163185e-01 -5.24266362e-01  1.28581360e-01
  -5.66961884e-01  2.86506787e-02]
 [-2.61825584e-02 -1.09530184e-02 -1.62613615e-01 -1.25600368e-01
   1.11469463e-01  3.35416287e-01  4.46890473e-01  2.48559430e-01
   1.58933625e-02 -3.24401766e-01]
 [-5.66769131e-02 -3.77700627e-02 -3.94968241e-01 -2.02299878e-01
   3.02203536e-01 -2.60779798e-01 -4.57925171e-01  2.93677509e-01
  -3.25312577e-02 -1.27941936e-01]
 [-3.51725668e-01 -6.48105459e-04  3.59032512e-01 -3.56894940e-01
   3.87860924e-01  2.82485068e-01 -2.95887627e-02 -6.21434569e-01
   4.66537833e-01 -4.39308494e-01]
 [-2.97538787e-01 -2.97167510e-01  7.38695204e-01 -1.95738032e-01
  -5.14048815e-01  2.06863899e-02 -2.44879782e-01  2.47701436e-01
   1.54624388e-01 -9.75212604e-02]]
Output layer biases (10,) :
 [ 0.02123602  0.00257758  0.01063514  0.05597897  0.01978655  0.09104188
 -0.02678998  0.00161227 -0.06452856 -0.00490647]