DL – Pretrained Model Example 01

import tensorflow as tf  #pip install tensorflow 

import numpy as np  #pip install numpy

filename = 'Pictures/Saved Pictures/office03.jpg'

Lets load the Deep Learning Model – Pre trained models using mobilenet

mobile = tf.keras.applications.mobilenet.MobileNet() #DL model weights - pretrained

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf.h5
17227776/17225924 [==============================] - 9s 1us/step
  1. Creating a model
  2. Training a model
  3. Test or validate – provide labels
  4. Predict classification alogorithm

Prep-processing of the image Adding a step to use mobilenet_v2

—-mobile=tf.keras.applications.mobilenet_v2.mobilenet_v2()

from tensorflow.keras.preprocessing import image

img = image.load_img(filename, target_size=(224, 224))

import matplotlib.pyplot as plt

plt.imshow(img)

<matplotlib.image.AxesImage at 0x29e5a16d0d0>
resized_img = image.img_to_array(img)

final_image = np.expand_dims(resized_img, axis=0)  #need fourth dimension

final_image = tf.keras.applications.mobilenet.preprocess_input(final_image)

final_image.shape

(1, 224, 224, 3)
predictions = mobile.predict(final_image)

print(predictions)  #can be deleted / eliminate after you see output from the script

[[5.16344812e-07 4.29362586e-07 8.73986323e-07 2.88791171e-07
  2.14516149e-06 1.48731287e-05 5.28806140e-06 7.34447326e-07
  1.36056121e-06 8.73420163e-08 5.56154987e-07 2.73330625e-06
  1.20713446e-06 1.37200601e-07 2.19754088e-06 2.08699089e-06
  1.43476723e-06 4.89133129e-07 1.03431444e-06 7.70895326e-07
  4.43114368e-06 3.10400566e-07 2.39896053e-07 8.02754130e-07
  4.56352645e-08 5.40576686e-08 1.17779948e-07 7.08913944e-07
  1.20364149e-07 2.10305438e-06 5.88034368e-07 1.16747302e-07
  1.13033920e-07 1.30712888e-05 4.11793008e-06 4.07797188e-07
  3.35411528e-07 1.81322442e-08 1.21324021e-07 4.15668774e-06
  1.26525009e-07 1.00281078e-07 2.66568293e-07 3.05088577e-07
  9.52711900e-08 2.90141793e-06 1.20561583e-06 1.25241479e-08
  2.49361790e-07 5.33998627e-06 4.91935043e-06 4.97004839e-06
  1.65215681e-06 1.32060464e-07 2.00471035e-07 2.11161066e-07
  1.14497176e-07 1.14666008e-07 2.05196301e-07 1.24695168e-06
  7.95229244e-06 1.15702967e-07 1.63893759e-07 2.19689764e-06
  1.05349602e-06 4.17304619e-07 1.17150876e-06 9.87205794e-07
  4.34937789e-07 9.16358331e-08 5.90005470e-07 2.33659790e-07
  6.01964871e-07 2.09702603e-06 2.00201455e-07 2.88092195e-07
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  7.60463706e-07 2.67302482e-07 1.56794806e-06 3.63888944e-07
  3.42667192e-07 2.53022847e-07 8.43923658e-07 8.55267615e-07
  3.27400016e-07 1.82778834e-07 1.37009363e-06 3.85945128e-07
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  7.33703610e-06 8.50109154e-06 5.54093185e-06 5.16194632e-07
  1.36296052e-07 2.79240510e-07 2.72499934e-09 2.55877012e-07
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  7.42209249e-06 3.90323839e-06 8.95595440e-06 7.48884850e-06
  1.56308988e-06 8.04278272e-07 7.93761283e-05 1.29477039e-05
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  4.35374648e-04 1.42353874e-05 1.65165202e-05 2.09524187e-05
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  2.20275815e-05 1.38686346e-05 7.09892920e-05 7.90177510e-05
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  1.15355151e-05 1.02976492e-05 2.17709101e-07 1.08196673e-05
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  7.95802464e-07 4.80341669e-07 2.06541358e-07 1.43554871e-06
  8.67090648e-07 1.93624237e-06 3.92109257e-07 5.20820493e-07
  5.38101347e-07 4.54696583e-08 4.90116520e-07 7.65667920e-08
  2.00126902e-08 2.23698248e-06 3.96166485e-07 1.00844034e-07
  1.87409228e-07 3.19874474e-08 9.71137979e-07 2.93434653e-07
  1.40507140e-07 6.84756515e-07 1.68402778e-06 3.53886008e-07
  4.16099866e-08 6.45615410e-06 3.00583132e-08 2.90957104e-07
  2.12443709e-07 2.35576501e-08 2.07528771e-07 1.46371315e-06
  5.23173696e-07 6.86022645e-07 6.41992642e-07 1.72498142e-06
  2.39568863e-07 3.74083164e-08 1.81653121e-07 1.28729266e-06
  3.05950452e-05 3.88989065e-06 3.02107566e-07 5.00086912e-07
  3.79233097e-06 3.77692982e-06 2.40933514e-05 5.72932151e-07
  9.11874395e-07 1.11994177e-05 1.21186361e-06 1.52705127e-06
  1.64549442e-06 3.08244495e-07 1.84877464e-07 1.34209290e-06
  1.32420382e-07 5.14172598e-08 2.35048802e-07 2.18185392e-06
  2.03617606e-06 3.51576688e-07 1.45922172e-07 5.05325943e-07
  1.43444922e-05 3.29818988e-07 9.98585506e-07 9.40833434e-06
  5.33253342e-07 6.17610908e-07 1.09228240e-05 2.20271704e-06
  1.14104431e-07 2.56511015e-07 1.89579112e-07 7.84036911e-06
  2.04219305e-06 1.23781422e-06 4.42459168e-07 1.44750345e-06
  1.84219857e-06 3.98835073e-06 6.73718716e-07 1.87329888e-07
  1.63788070e-06 7.86183818e-07 6.22517177e-07 3.20085206e-07
  1.26657631e-06 1.42982046e-07 5.84684756e-07 1.47738660e-06
  3.13715667e-08 9.18282149e-06 6.83336020e-06 1.37111513e-07
  1.58616126e-06 1.56962221e-06 2.83724353e-06 2.30219939e-06
  1.14812906e-06 4.11201206e-08 5.02107150e-06 4.14011708e-07
  5.84890820e-08 3.85059273e-08 3.15288089e-05 1.68994046e-03
  1.02560688e-03 3.39394719e-05 5.79358766e-06 2.69027197e-07
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  7.04910781e-05 6.44066949e-06 6.74951252e-06 5.62113302e-04
  2.45173851e-05 2.80887275e-06 2.47074524e-03 2.12062969e-05
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  8.37977666e-07 3.11848526e-05 1.05355994e-03 2.76364462e-05
  2.87467847e-05 3.43974243e-05 1.09033308e-05 1.72730324e-07
  2.04025423e-06 5.04376512e-05 1.05555875e-04 5.29283949e-04
  1.06308801e-06 1.56394648e-07 5.22964683e-07 8.61070930e-07
  3.12302000e-04 2.03274772e-06 2.46450072e-05 1.72024352e-06
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  1.65946176e-06 1.48500054e-04 1.01562882e-05 3.40065220e-08
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  5.83619624e-07 1.02774472e-07 5.36921361e-05 4.75273100e-06]]

from tensorflow.keras.applications import imagenet_utils

results = imagenet_utils.decode_predictions(predictions)

Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json
40960/35363 [==================================] - 0s 4us/step

print(results)

[[('n03642806', 'laptop', 0.48010135), ('n03832673', 'notebook', 0.25662512), ('n03924679', 'photocopier', 0.05953988), ('n03179701', 'desk', 0.041483484), ('n03337140', 'file', 0.015770826)]]

plt.imshow(img)

<matplotlib.image.AxesImage at 0x29e5a5c7280>

Predict results again

predictions = mobile.predict(final_image)

results = imagenet_utils.decode_predictions(predictions)

print(results)

[[('n03642806', 'laptop', 0.48010135), ('n03832673', 'notebook', 0.25662512), ('n03924679', 'photocopier', 0.05953988), ('n03179701', 'desk', 0.041483484), ('n03337140', 'file', 0.015770826)]]
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