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update dog vision images
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@mrdbourke
mrdbourke committed Apr 18, 2024
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39 changes: 2 additions & 37 deletions section-4-unstructured-data-projects/end-to-end-dog-vision-v2.ipynb
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"id": "6nBMeHrVa1-y"
},
"source": [
"Truncated output of above: \n",
"Truncated output of `base_model.summary()`: \n",
"\n",
"```\n",
"Model: \"efficientnetv2-b0\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Params Connected to \n",
"==================================================================================================\n",
" input_1 (InputLayer) [(None, 224, 224, 3)] 0 [] \n",
" \n",
" rescaling (Rescaling) (None, 224, 224, 3) 0 ['input_1[0][0]'] \n",
" \n",
" normalization (Normalizati (None, 224, 224, 3) 0 ['rescaling[0][0]'] \n",
" on) \n",
" \n",
" stem_conv (Conv2D) (None, 112, 112, 32) 864 ['normalization[0][0]'] \n",
" \n",
" stem_bn (BatchNormalizatio (None, 112, 112, 32) 128 ['stem_conv[0][0]'] \n",
" n) \n",
" \n",
" stem_activation (Activatio (None, 112, 112, 32) 0 ['stem_bn[0][0]'] \n",
" n) \n",
" \n",
" >>> [Lots more layers here, removed 90% of them for brevity] <<< \n",
" \n",
" \n",
" avg_pool (GlobalAveragePoo (None, 1280) 0 ['top_activation[0][0]'] \n",
" ling2D) \n",
" \n",
" top_dropout (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] \n",
" \n",
" predictions (Dense) (None, 1000) 1281000 ['top_dropout[0][0]'] \n",
" \n",
"==================================================================================================\n",
"Total params: 7200312 (27.47 MB)\n",
"Trainable params: 7139704 (27.24 MB)\n",
"Non-trainable params: 60608 (236.75 KB)\n",
"__________________________________________________________________________________________________\n",
"```\n",
"<img src=\"https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/images/unstructured-data-effnetv2b0-model-summary.png?raw=true\" width=750 alt=\"Screenshot of a text-based neural network model summary for 'efficientnetv2-b0'. The display lists layers, output shapes, parameter counts, and connectivity in a structured table with dashed lines separating columns. Visible layers include InputLayer, Rescaling, Normalization, Conv2D, BatchNormalization, Activation, GlobalAveragePooling2D, Dropout, and Dense, along with their corresponding specifications. A note indicates 90% of the layers are omitted for brevity.\"/>\n",
"\n",
"Woah! Look at all those layers... this is what the \"deep\" in deep learning means! A *deep* number of layers.\n",
"\n",
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