Image Source: Pexels
We have already gone through one of the blogs. We don’t need to create a new architecture from the scratch for our projects, instead, we can use existing Convolutional Neural Networks(CNN) popular architectures like LeNet-5, ResNet, VGG16, etc. models to check how it predicts.
I am working on few kinds of research on the Enterprise Resource Planning side, how to use Convolutional Neural Networks(CNN) Image Recognition and computer vision.
Today, we will use Convolutional Neural Networks(CNN) ResNet50 architecture pre-trained model to predict “Humming Bird” and we will check how much accuracy shows.
I have used Google Colab IDE for this example.
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[156., 138., 138.]]]], dtype=float32)
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WARNING:tensorflow:7 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fcc0d198e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.
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1.51720803e-09, 1.62617197e-09, 2.65224231e-11, 1.00002978e-10,
3.64896058e-10, 2.36830346e-07, 6.43445353e-10, 1.65821287e-10,
1.07712228e-09, 4.52734183e-10, 3.67097130e-10, 1.46535076e-10,
1.11602894e-10, 2.00167868e-10, 7.97730593e-09, 9.00666070e-11,
7.35303507e-09, 2.46716181e-09, 6.75000500e-10, 1.35032555e-10,
1.11416362e-10, 5.51087509e-10, 3.89666667e-11, 9.16366716e-08,
4.28776653e-10, 3.13917887e-08, 3.03559677e-10, 1.02890552e-09,
5.88953719e-10, 2.48633975e-10, 1.94763228e-09, 1.02703890e-09,
7.18242466e-10, 1.49899160e-09, 1.15264860e-10, 1.44796952e-10,
1.62177882e-10, 1.35524855e-10, 6.54406418e-10, 1.54843416e-09,
3.73556652e-09, 2.08372042e-10, 9.41582992e-11, 1.29894273e-09,
5.48711909e-10, 6.08749995e-10, 1.27452493e-11, 1.07494624e-10,
2.26289695e-10, 2.63327082e-10, 5.71379521e-09, 2.96558977e-09,
2.57036809e-10, 1.06336336e-10, 1.05587539e-10, 1.79408877e-09,
1.64877348e-10, 5.98402994e-10, 3.32942007e-10, 1.53418417e-10,
7.01433078e-10, 1.18935098e-10, 2.68871925e-09, 7.36317465e-11,
1.57054914e-09, 1.49878565e-09, 1.21423246e-10, 3.53223811e-10,
9.78542913e-10, 2.97478153e-11, 3.44242213e-09, 2.79351209e-09,
1.91197702e-09, 2.42221632e-10, 1.20114994e-08, 5.48719470e-09,
2.34172859e-12, 3.12997891e-11, 2.18577864e-10, 8.10817871e-11,
7.59460717e-10, 2.94102728e-11, 3.45375228e-10, 7.10937031e-10,
1.72042491e-10, 4.45330856e-11, 8.71705971e-11, 1.13826851e-10,
6.41734332e-10, 4.18738412e-11, 1.43479884e-09, 8.72246941e-10,
5.30907485e-10, 5.17538845e-10, 5.36272027e-10, 4.89504340e-11,
3.02799813e-10, 1.65061831e-09, 8.50385318e-10, 5.09957576e-10,
2.73556844e-10, 6.88583235e-09, 1.02916315e-08, 5.24792015e-11,
3.65993034e-11, 6.30870334e-09, 2.61588001e-10, 4.36884440e-09,
2.45080733e-09, 1.07535841e-10, 2.35659448e-09, 1.79679844e-08,
5.84361240e-11, 1.95823593e-10, 1.25204999e-10, 1.81936077e-10,
2.49454318e-10, 3.89524191e-10, 3.31739058e-09, 5.18273069e-09,
3.68117646e-12, 5.86857576e-11, 1.57516760e-11, 6.84533610e-11,
3.22084315e-09, 1.27603594e-10, 8.80814657e-11, 5.73067649e-10,
2.42359223e-11, 7.99812508e-11, 2.90456157e-12, 5.47763446e-10,
5.51299895e-10, 2.04960840e-10, 3.32021043e-11, 5.88256821e-09,
2.00111860e-09, 4.70350814e-10, 3.23994220e-10, 3.06423065e-09,
2.57242179e-11, 1.48506984e-09, 1.39046830e-10, 4.68582340e-10,
1.44357044e-11, 6.97515726e-11, 9.04613814e-11, 5.78741950e-11,
5.37000977e-09, 6.27642660e-10, 2.14364096e-10, 5.22388754e-09,
1.17904075e-09, 1.86969551e-10, 7.85119969e-10, 2.01477945e-09,
1.31704092e-11, 9.50232115e-11, 3.45408119e-10, 6.60249189e-09,
8.54556337e-09, 3.88321908e-09, 2.10622586e-09, 1.55526869e-09,
8.62866845e-09, 2.24274890e-10, 1.25108562e-10, 5.25125721e-10,
7.04329217e-09, 1.24622715e-10, 1.28346318e-11, 1.96937550e-10,
9.39158529e-10, 4.71559902e-10, 2.61603095e-09, 1.75755910e-10,
1.70761694e-09, 5.03089792e-09, 2.60379385e-10, 4.36194192e-09,
8.23822510e-10, 6.50450416e-10, 1.45287098e-11, 3.61412705e-10,
6.67088518e-09, 2.39426701e-09, 8.59890562e-11, 1.29428959e-11,
5.40029479e-11, 1.68725103e-11, 1.22000812e-10, 6.23605473e-11,
5.88795054e-11, 8.65460703e-10, 2.79952228e-10, 1.54658941e-09,
1.38641126e-10, 3.74374587e-10, 5.15920529e-10, 3.30133254e-09,
4.01399669e-09, 2.29777530e-09, 1.53080109e-08, 9.88460647e-10,
1.17540777e-09, 1.67178431e-08, 5.32561950e-09, 9.68967306e-09,
4.12301926e-09, 4.86658287e-08, 2.48336587e-08, 2.49211790e-10,
1.18651711e-09, 1.58793299e-08, 1.10010057e-09, 1.14921087e-10,
1.53731264e-10, 5.99789871e-11, 1.01513499e-10, 3.07138537e-10,
4.26061908e-10, 2.45830106e-10, 5.32892896e-10, 1.59279090e-10,
3.52382168e-09, 2.66979966e-10, 2.93478505e-11, 9.18117138e-10,
8.75378603e-10, 1.15013084e-08, 1.27722364e-11, 9.49502965e-09,
1.65667646e-10, 6.11075768e-09, 8.24292468e-10, 7.50526117e-12,
1.07757386e-10, 7.10572323e-10, 6.29930941e-10, 3.90737265e-09,
4.92377694e-09, 5.90737450e-08, 5.04222353e-09, 2.76875078e-09,
1.15951631e-08, 4.24760067e-08, 1.60372027e-09, 3.82138765e-10,
2.71942580e-10, 1.55857827e-11, 1.30140094e-08, 1.73178083e-09,
1.02055135e-10, 6.04035655e-10, 2.03674144e-09, 3.44230894e-10]],
dtype=float32)
Copy to Clipboard
[[('n01833805', 'hummingbird', 0.9997278),
('n01828970', 'bee_eater', 0.0001296032),
('n01843065', 'jacamar', 8.915818e-05),
('n01847000', 'drake', 1.0703447e-05),
('n01531178', 'goldfinch', 7.0216606e-06)]]
Wow! Correctly predicted as “Humming Bird” and 0.99 prediction accuracy.
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