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keras_resnet/data/notebooks/CIFAR-10/ResNet-50.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"import keras\n",
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"import keras.datasets\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"deletable": true,
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},
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"cell_type": "code",
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"execution_count": 5,
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "__init__() takes 2 positional arguments but 3 were given",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-7-38c2a62f02a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras_resnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResNet50\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"categorical_crossentropy\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: __init__() takes 2 positional arguments but 3 were given"
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],
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"output_type": "error"
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}
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],
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"source": [
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"model = keras_resnet.ResNet50(keras.layers.Input(shape), classes)\n",
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"\n",
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"version": 3.0
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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}

setup.py

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]
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),
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url="https://github.com/broadinstitute/keras-resnet",
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version="0.0.1"
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version="0.0.2"
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)

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