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softmax-as-intermediate-layer-cnn's Introduction

Softmax for intermediate layers

Link to the notebook: CNN

Context: Usually Softmax activation function will not be used in intermediate layer as it might lead to vanishing gradients problem as it squashes the input between 0 to 1.

Visualizing the CNN output while softmax is used in intermediate layers

  • Usually Softmax will be used for spatial dimension in the sense the height and width of an image

Softmax for height dimension

F1

Softmax for width dimension

F1

Softmax for both height and width dimension

F1

The output of Intel Image Classification for softmax intermediate layers

  • Epoch [1 / 50], Training Loss: 1.884, Training Accuracy: 16.895
  • Epoch [2 / 50], Training Loss: 1.801, Training Accuracy: 17.510
  • Epoch [3 / 50], Training Loss: 1.815, Training Accuracy: 16.815
  • Epoch [4 / 50], Training Loss: 1.819, Training Accuracy: 16.441
  • Epoch [5 / 50], Training Loss: 1.820, Training Accuracy: 16.984
  • Epoch [6 / 50], Training Loss: 1.823, Training Accuracy: 16.762
  • Epoch [7 / 50], Training Loss: 1.812, Training Accuracy: 17.082
  • Epoch [8 / 50], Training Loss: 1.826, Training Accuracy: 16.708
  • Epoch [9 / 50], Training Loss: 1.821, Training Accuracy: 17.091
  • Epoch [10 / 50], Training Loss: 1.800, Training Accuracy: 16.842
  • Epoch [11 / 50], Training Loss: 1.814, Training Accuracy: 16.842
  • Epoch [12 / 50], Training Loss: 1.811, Training Accuracy: 17.127
  • Epoch [13 / 50], Training Loss: 1.826, Training Accuracy: 16.388
  • Epoch [14 / 50], Training Loss: 1.845, Training Accuracy: 16.637
  • Epoch [15 / 50], Training Loss: 1.814, Training Accuracy: 16.227
  • Epoch [16 / 50], Training Loss: 1.822, Training Accuracy: 16.655
  • Epoch [17 / 50], Training Loss: 1.817, Training Accuracy: 16.486
  • Epoch [18 / 50], Training Loss: 1.817, Training Accuracy: 17.073
  • Epoch [19 / 50], Training Loss: 1.812, Training Accuracy: 16.477
  • Epoch [20 / 50], Training Loss: 1.816, Training Accuracy: 17.127
  • Epoch [21 / 50], Training Loss: 1.819, Training Accuracy: 16.682
  • Epoch [22 / 50], Training Loss: 1.813, Training Accuracy: 16.877
  • Epoch [23 / 50], Training Loss: 1.823, Training Accuracy: 16.833
  • Epoch [24 / 50], Training Loss: 1.804, Training Accuracy: 19.015
  • Epoch [25 / 50], Training Loss: 1.739, Training Accuracy: 24.947
  • Epoch [26 / 50], Training Loss: 1.650, Training Accuracy: 33.488
  • Epoch [27 / 50], Training Loss: 1.530, Training Accuracy: 39.464
  • Epoch [28 / 50], Training Loss: 1.440, Training Accuracy: 41.788
  • Epoch [29 / 50], Training Loss: 1.389, Training Accuracy: 43.445
  • Epoch [30 / 50], Training Loss: 1.345, Training Accuracy: 44.549
  • Epoch [31 / 50], Training Loss: 1.293, Training Accuracy: 47.756
  • Epoch [32 / 50], Training Loss: 1.266, Training Accuracy: 48.219
  • Epoch [33 / 50], Training Loss: 1.251, Training Accuracy: 48.281
  • Epoch [34 / 50], Training Loss: 1.204, Training Accuracy: 50.588
  • Epoch [35 / 50], Training Loss: 1.176, Training Accuracy: 52.066
  • Epoch [36 / 50], Training Loss: 1.167, Training Accuracy: 52.387
  • Epoch [37 / 50], Training Loss: 1.155, Training Accuracy: 52.503
  • Epoch [38 / 50], Training Loss: 1.133, Training Accuracy: 53.919
  • Epoch [39 / 50], Training Loss: 1.114, Training Accuracy: 54.783
  • Epoch [40 / 50], Training Loss: 1.098, Training Accuracy: 54.925
  • Epoch [41 / 50], Training Loss: 1.093, Training Accuracy: 55.273
  • Epoch [42 / 50], Training Loss: 1.092, Training Accuracy: 54.542
  • Epoch [43 / 50], Training Loss: 1.073, Training Accuracy: 56.448
  • Epoch [44 / 50], Training Loss: 1.074, Training Accuracy: 55.504
  • Epoch [45 / 50], Training Loss: 1.065, Training Accuracy: 55.994
  • Epoch [46 / 50], Training Loss: 1.068, Training Accuracy: 55.851
  • Epoch [47 / 50], Training Loss: 1.056, Training Accuracy: 56.573
  • Epoch [48 / 50], Training Loss: 1.029, Training Accuracy: 59.236
  • Epoch [49 / 50], Training Loss: 1.003, Training Accuracy: 59.966
  • Epoch [50 / 50], Training Loss: 1.019, Training Accuracy: 58.452

The output of Intel Image Classification for ReLU intermediate layers

  • Epoch [1 / 50], Training Loss: 1.571, Training Accuracy: 44.968
  • Epoch [2 / 50], Training Loss: 1.038, Training Accuracy: 59.503
  • Epoch [3 / 50], Training Loss: 0.952, Training Accuracy: 63.529
  • Epoch [4 / 50], Training Loss: 0.848, Training Accuracy: 68.899
  • Epoch [5 / 50], Training Loss: 0.788, Training Accuracy: 70.315
  • Epoch [6 / 50], Training Loss: 0.778, Training Accuracy: 71.170
  • Epoch [7 / 50], Training Loss: 0.723, Training Accuracy: 73.156
  • Epoch [8 / 50], Training Loss: 0.722, Training Accuracy: 73.254
  • Epoch [9 / 50], Training Loss: 0.668, Training Accuracy: 74.840
  • Epoch [10 / 50], Training Loss: 0.658, Training Accuracy: 75.472
  • Epoch [11 / 50], Training Loss: 0.653, Training Accuracy: 76.006
  • Epoch [12 / 50], Training Loss: 0.617, Training Accuracy: 77.048
  • Epoch [13 / 50], Training Loss: 0.631, Training Accuracy: 76.354
  • Epoch [14 / 50], Training Loss: 0.610, Training Accuracy: 77.752
  • Epoch [15 / 50], Training Loss: 0.585, Training Accuracy: 78.295
  • Epoch [16 / 50], Training Loss: 0.572, Training Accuracy: 78.750
  • Epoch [17 / 50], Training Loss: 0.575, Training Accuracy: 78.928
  • Epoch [18 / 50], Training Loss: 0.561, Training Accuracy: 79.382
  • Epoch [19 / 50], Training Loss: 0.562, Training Accuracy: 79.364
  • Epoch [20 / 50], Training Loss: 0.558, Training Accuracy: 79.489
  • Epoch [21 / 50], Training Loss: 0.556, Training Accuracy: 79.507
  • Epoch [22 / 50], Training Loss: 0.538, Training Accuracy: 80.397
  • Epoch [23 / 50], Training Loss: 0.536, Training Accuracy: 80.166
  • Epoch [24 / 50], Training Loss: 0.528, Training Accuracy: 80.549
  • Epoch [25 / 50], Training Loss: 0.537, Training Accuracy: 80.700
  • Epoch [26 / 50], Training Loss: 0.516, Training Accuracy: 80.807
  • Epoch [27 / 50], Training Loss: 0.517, Training Accuracy: 81.038
  • Epoch [28 / 50], Training Loss: 0.504, Training Accuracy: 80.932
  • Epoch [29 / 50], Training Loss: 0.504, Training Accuracy: 81.083
  • Epoch [30 / 50], Training Loss: 0.516, Training Accuracy: 80.718
  • Epoch [31 / 50], Training Loss: 0.499, Training Accuracy: 81.341
  • Epoch [32 / 50], Training Loss: 0.507, Training Accuracy: 81.715
  • Epoch [33 / 50], Training Loss: 0.485, Training Accuracy: 82.089
  • Epoch [34 / 50], Training Loss: 0.484, Training Accuracy: 81.822
  • Epoch [35 / 50], Training Loss: 0.479, Training Accuracy: 82.348
  • Epoch [36 / 50], Training Loss: 0.492, Training Accuracy: 81.769
  • Epoch [37 / 50], Training Loss: 0.489, Training Accuracy: 82.054
  • Epoch [38 / 50], Training Loss: 0.488, Training Accuracy: 82.553
  • Epoch [39 / 50], Training Loss: 0.485, Training Accuracy: 82.134
  • Epoch [40 / 50], Training Loss: 0.496, Training Accuracy: 82.098
  • Epoch [41 / 50], Training Loss: 0.487, Training Accuracy: 82.419
  • Epoch [42 / 50], Training Loss: 0.485, Training Accuracy: 82.481
  • Epoch [43 / 50], Training Loss: 0.475, Training Accuracy: 82.561
  • Epoch [44 / 50], Training Loss: 0.470, Training Accuracy: 82.615
  • Epoch [45 / 50], Training Loss: 0.477, Training Accuracy: 82.268
  • Epoch [46 / 50], Training Loss: 0.474, Training Accuracy: 82.508
  • Epoch [47 / 50], Training Loss: 0.464, Training Accuracy: 82.864
  • Epoch [48 / 50], Training Loss: 0.460, Training Accuracy: 82.998
  • Epoch [49 / 50], Training Loss: 0.480, Training Accuracy: 82.063
  • Epoch [50 / 50], Training Loss: 0.466, Training Accuracy: 82.588

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