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2.4 Transfer Learning & Fine-Tuning.ipynb

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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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},
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"outputs": [],
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"source": [
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"if K.image_dim_ordering() == 'th':\n",
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"if K.image_data_format() == 'channels_first':\n",
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" input_shape = (1, img_rows, img_cols)\n",
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"else:\n",
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" input_shape = (img_rows, img_cols, 1)"
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{
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"cell_type": "code",
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"execution_count": 8,
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"collapsed": false
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"cell_type": "code",
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"execution_count": 9,
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"collapsed": false
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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"version": "3.5.3"
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}
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},
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"nbformat": 4,

4. Conclusions.ipynb

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}
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},
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"source": [
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"<img src=\"imgs/keras_rank_1.jpg\" width=\"65%\" />"
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"<img src=\"imgs/keras_rank.jpg\" width=\"65%\" />"
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]
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}
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},
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"source": [
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"<img src=\"imgs/keras_rank_2.jpg\" width=\"65%\" />"
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"<img src=\"imgs/keras_rank_details.jpg\" width=\"65%\" />"
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]
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},
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{
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"source": [
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"#### Cutting Edge\n",
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"\n",
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"* Fractal Net Implementation with Keras: https://github.com/snf/keras-fractalnet -\n",
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"* Please check out: [https://github.com/fchollet/keras-resources]()"
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"* Fractal Net Implementation with Keras: [https://github.com/snf/keras-fractalnet](https://github.com/snf/keras-fractalnet) -\n",
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"* Please check out: [https://github.com/fchollet/keras-resources](https://github.com/fchollet/keras-resources)"
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]
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},
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{
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}
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],
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"metadata": {
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"celltoolbar": "Slideshow",
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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"version": "3.5.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}

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