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ugik
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tflearn examples
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8 files changed

+192
-24
lines changed

‎.ipynb_checkpoints/tflearn toy ANN reload-checkpoint.ipynb‎

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‎.ipynb_checkpoints/tflearn toy ANN-checkpoint.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 32,
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"metadata": {
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"collapsed": false
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training Step: 999 | total loss: \u001b[1m\u001b[32m0.24585\u001b[0m\u001b[0m | time: 0.002s\n",
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"| Adam | epoch: 500 | loss: 0.24585 - acc: 0.9570 -- iter: 16/18\n",
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"Training Step: 1000 | total loss: \u001b[1m\u001b[32m0.22196\u001b[0m\u001b[0m | time: 0.005s\n",
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"| Adam | epoch: 500 | loss: 0.22196 - acc: 0.9613 -- iter: 18/18\n",
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"--\n"
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"Training Step: 999 | total loss: \u001b[1m\u001b[32m0.00214\u001b[0m\u001b[0m | time: 0.003s\n",
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"| Adam | epoch: 500 | loss: 0.00214 - acc: 1.0000 -- iter: 16/18\n",
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"Training Step: 1000 | total loss: \u001b[1m\u001b[32m0.00209\u001b[0m\u001b[0m | time: 0.006s\n",
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"| Adam | epoch: 500 | loss: 0.00209 - acc: 1.0000 -- iter: 18/18\n",
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"--\n",
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"INFO:tensorflow:/home/gk/gensim/notebooks/ANN.model is not in all_model_checkpoint_paths. Manually adding it.\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 33,
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"metadata": {
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"collapsed": false
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1, 1, 1, 1, 1] [[0.9917974472045898, 0.008202536962926388]]\n",
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"[1, 1, 1, 1, 0] [[0.0033489016350358725, 0.9966511130332947]]\n",
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"[1, 1, 0, 0, 1] [[0.9948439598083496, 0.0051560611464083195]]\n",
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"[1, 0, 0, 1, 1] [[0.9970082640647888, 0.002991726389154792]]\n"
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"[1, 1, 1, 1, 1] [[0.9939707517623901, 0.006029300391674042]]\n",
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"[1, 1, 1, 1, 0] [[0.0010782598983496428, 0.9989217519760132]]\n",
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"[1, 1, 0, 0, 1] [[0.997883141040802, 0.0021168675739318132]]\n",
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"[1, 0, 0, 1, 1] [[0.9975355863571167, 0.002464443678036332]]\n"
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]
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}
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],
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"source": [
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"for i,t in enumerate(test_x):\n",
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" print(test_x[i], model.predict([test_x[i]]))"
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]
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎ANN.model.data-00000-of-00001‎

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‎ANN.model.index‎

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‎ANN.model.meta‎

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‎checkpoint‎

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model_checkpoint_path: "model.ckpt"
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all_model_checkpoint_paths: "model.ckpt"
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model_checkpoint_path: "/home/gk/gensim/notebooks/ANN.model"
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all_model_checkpoint_paths: "/home/gk/gensim/notebooks/ANN.model"

‎tflearn toy ANN reload.ipynb‎

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‎tflearn toy ANN.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 32,
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"metadata": {
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"collapsed": false
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training Step: 999 | total loss: \u001b[1m\u001b[32m0.24585\u001b[0m\u001b[0m | time: 0.002s\n",
112-
"| Adam | epoch: 500 | loss: 0.24585 - acc: 0.9570 -- iter: 16/18\n",
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"Training Step: 1000 | total loss: \u001b[1m\u001b[32m0.22196\u001b[0m\u001b[0m | time: 0.005s\n",
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"| Adam | epoch: 500 | loss: 0.22196 - acc: 0.9613 -- iter: 18/18\n",
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"--\n"
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"Training Step: 999 | total loss: \u001b[1m\u001b[32m0.00214\u001b[0m\u001b[0m | time: 0.003s\n",
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"| Adam | epoch: 500 | loss: 0.00214 - acc: 1.0000 -- iter: 16/18\n",
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"Training Step: 1000 | total loss: \u001b[1m\u001b[32m0.00209\u001b[0m\u001b[0m | time: 0.006s\n",
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"| Adam | epoch: 500 | loss: 0.00209 - acc: 1.0000 -- iter: 18/18\n",
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"--\n",
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"INFO:tensorflow:/home/gk/gensim/notebooks/ANN.model is not in all_model_checkpoint_paths. Manually adding it.\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 33,
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"metadata": {
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"collapsed": false
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1, 1, 1, 1, 1] [[0.9917974472045898, 0.008202536962926388]]\n",
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"[1, 1, 1, 1, 0] [[0.0033489016350358725, 0.9966511130332947]]\n",
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"[1, 1, 0, 0, 1] [[0.9948439598083496, 0.0051560611464083195]]\n",
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"[1, 0, 0, 1, 1] [[0.9970082640647888, 0.002991726389154792]]\n"
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"[1, 1, 1, 1, 1] [[0.9939707517623901, 0.006029300391674042]]\n",
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"[1, 1, 1, 1, 0] [[0.0010782598983496428, 0.9989217519760132]]\n",
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"[1, 1, 0, 0, 1] [[0.997883141040802, 0.0021168675739318132]]\n",
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"[1, 0, 0, 1, 1] [[0.9975355863571167, 0.002464443678036332]]\n"
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]
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}
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],
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"source": [
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"for i,t in enumerate(test_x):\n",
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" print(test_x[i], model.predict([test_x[i]]))"
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]
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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