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@jshtaway
jshtaway committed Oct 23, 2018
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156 changes: 106 additions & 50 deletions word_based.ipynb
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"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -13,7 +13,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -117,40 +117,6 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Whosever room this is should be ashamed!\n",
"His underwear is hanging on the lamp.\n",
"His raincoat is there in the overstuffed chair,\n",
"And the chair is becoming quite mucky and damp.\n",
"His workbook is wedged in the window,\n",
"His sweater's been thrown on the floor.\n",
"His scarf and one ski are\n",
"\n"
]
}
],
"source": [
"# select a seed text\n",
"# seed_text = lines[randint(0,len(lines))]\n",
"seed_text = '''Whosever room this is should be ashamed!\n",
"His underwear is hanging on the lamp.\n",
"His raincoat is there in the overstuffed chair,\n",
"And the chair is becoming quite mucky and damp.\n",
"His workbook is wedged in the window,\n",
"His sweater's been thrown on the floor.\n",
"His scarf and one ski are'''\n",
"\n",
"print(seed_text + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# define model\n",
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},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
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"# {'model':'Flatten'},\n",
"# {'model': 'Dense','units':vocab_size,'activation':'softmax'},\n",
"# ]\n",
" modelList = [{'model':'Embedding', 'input_dim':vocab_size, 'output_dim': 256, 'input_length': seq_length},\n",
" {'model': 'LSTM', 'units':256, 'use_bias':True, 'dropout':.2, 'recurrent_dropout': 0, 'return_sequences': True}, \n",
" modelList = [{'model':'Embedding', 'input_dim':vocab_size, 'output_dim': 512, 'input_length': seq_length},\n",
" {'model': 'LSTM', 'units':512, 'use_bias':True, 'dropout':.2, 'recurrent_dropout': 0, 'return_sequences': True}, \n",
" {'model': 'Dense','units':100,'activation':'relu'}, \n",
" {'model': 'LSTM', 'units':256, 'use_bias':True, 'dropout':.2, 'recurrent_dropout': 0, 'return_sequences': True}, \n",
" {'model': 'Dense','units':100,'activation':'relu'}, \n",
" {'model': 'LSTM', 'units':256, 'use_bias':True, 'dropout':.2, 'recurrent_dropout': 0, 'return_sequences': True}, \n",
" {'model': 'LSTM', 'units':512, 'use_bias':True, 'dropout':.2, 'recurrent_dropout': 0, 'return_sequences': True}, \n",
" {'model': 'Dense','units':100,'activation':'relu'}, \n",
" {'model':'Flatten'},\n",
" {'model': 'Dense','units':vocab_size,'activation':'softmax'},\n",
Expand All @@ -295,7 +259,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -358,12 +322,104 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['yertle', 'the', 'turtle', 'on', 'the', 'far', 'away', 'island', 'of', 'sala', 'ma', 'sond', 'yertle', 'the', 'turtle', 'was', 'king', 'of', 'the', 'pond.', 'a', 'nice', 'little', 'pond.', 'it', 'was', 'clean.', 'it', 'was', 'neat.', 'the', 'water', 'was', 'warm.', 'there', 'was', 'plenty', 'to', 'eat.', 'the', 'turtles', 'had', 'everything', 'turtles', 'might', 'need.', 'and', 'they', 'were', 'all', 'happy.', 'quite', 'happy', 'indeed.', 'they', 'were.', 'untill', 'yertle', 'the', 'king', 'of', 'them', 'all', 'decided', 'the', 'kingdom', 'he', 'ruled', 'was', 'too', 'small.', 'im', 'ruler', 'said', 'yertle', 'of', 'all', 'that', 'i', 'see.', 'but', 'i', 'dont', 'see', 'enough.', 'thats', 'the', 'trouble', 'with', 'me.', 'with', 'this', 'stone', 'for', 'a', 'throne', 'i', 'look', 'down', 'on']\n",
"Total Tokens: 16226\n",
"Unique Tokens: 2829\n",
"Total Sequences: 16175\n",
"sequences[0][0]: yertle\n",
"sequences:\n",
" 0 1 2 3 4 5 6 7 8 9 ... 41 42 \\\n",
"0 162 1 161 12 1 237 425 2828 9 876 ... 47 1360 \n",
"1 1 161 12 1 237 425 2828 9 876 502 ... 1360 214 \n",
"2 161 12 1 237 425 2828 9 876 502 1362 ... 214 873 \n",
"3 12 1 237 425 2828 9 876 502 1362 162 ... 873 641 \n",
"4 1 237 425 2828 9 876 502 1362 162 1 ... 641 2 \n",
"\n",
" 43 44 45 46 47 48 49 50 \n",
"0 214 873 641 2 15 78 16 1363 \n",
"1 873 641 2 15 78 16 1363 95 \n",
"2 641 2 15 78 16 1363 95 642 \n",
"3 2 15 78 16 1363 95 642 877 \n",
"4 15 78 16 1363 95 642 877 15 \n",
"\n",
"[5 rows x 51 columns]\n",
"seq_length: 50\n",
"shape of X: (16175, 50)\n",
"shape of y: (16175, 2830)\n",
"drseuss_text: 'data/combinedText.txt'\n",
"seed_length: 50\n",
"epochs: 50\n",
"batch_size: 128\n",
"modelList: [{'model': 'Embedding', 'input_dim': 2830, 'output_dim': 512, 'input_length': 50}, {'model': 'LSTM', 'units': 512, 'use_bias': True, 'dropout': 0.2, 'recurrent_dropout': 0, 'return_sequences': True}, {'model': 'Dense', 'units': 100, 'activation': 'relu'}, {'model': 'LSTM', 'units': 512, 'use_bias': True, 'dropout': 0.2, 'recurrent_dropout': 0, 'return_sequences': True}, {'model': 'Dense', 'units': 100, 'activation': 'relu'}, {'model': 'Flatten'}, {'model': 'Dense', 'units': 2830, 'activation': 'softmax'}]\n",
"model.add(Embedding(input_dim= 2830, output_dim=512, input_length=50))\n",
"model.add(LSTM(units=512, use_bias=True, dropout=0.2, recurrent_dropout=0 ))\n",
"model.add(Dense(units=100, activation=relu))\n",
"model.add(LSTM(units=512, use_bias=True, dropout=0.2, recurrent_dropout=0 ))\n",
"model.add(Dense(units=100, activation=relu))\n",
"model.add(Flatten())\n",
"model.add(Dense(units=2830, activation=softmax))\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"embedding_1 (Embedding) (None, 50, 512) 1448960 \n",
"_________________________________________________________________\n",
"lstm_1 (LSTM) (None, 50, 512) 2099200 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 50, 100) 51300 \n",
"_________________________________________________________________\n",
"lstm_2 (LSTM) (None, 50, 512) 1255424 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 50, 100) 51300 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 5000) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 2830) 14152830 \n",
"=================================================================\n",
"Total params: 19,059,014\n",
"Trainable params: 19,059,014\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"Epoch 1/50\n",
" 128/16175 [..............................] - ETA: 14:44 - loss: 7.9478 - acc: 0.0000e+00"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-51c1a4b958c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainModelComplete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-7-1f952b296c8c>\u001b[0m in \u001b[0;36mtrainModelComplete\u001b[0;34m(results_path)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'token_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmodelName\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;31m#-- Fit model -- ---- --- --- --- ---- --- --- ---- --- --- --- --- --- --- --- ---\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0mhistory_callback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelFit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodelName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseq_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;31m#-- Save history and final model --- -\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mwriteFiles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodelName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory_callback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodelList\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseq_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-4-b4547fb07441>\u001b[0m in \u001b[0;36mmodelFit\u001b[0;34m(model, modelName, X, y, seq_length, batch_size, epochs, results_path)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# fit model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mhistory_callback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mhistory_callback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1036\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1037\u001b[0;31m validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 1038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1039\u001b[0m def evaluate(self, x=None, y=None,\n",
"\u001b[0;32m/anaconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 200\u001b[0m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2664\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2666\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2667\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2668\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2634\u001b[0m \u001b[0msymbol_vals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2635\u001b[0m session)\n\u001b[0;32m-> 2636\u001b[0;31m \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2637\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1397\u001b[0m ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m 1398\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 1400\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1401\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"if __name__ == '__main__':\n",
" trainModelComplete()"
" trainModelComplete('.')"
]
},
{
Expand All @@ -377,7 +433,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -423,7 +479,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down
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