Ok now this should be the final one
Tuan-Dat Tran
parent
cf05c7cc1b
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "25366fc5",
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"id": "4f19089f",
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "0394ccfe",
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"id": "fb26acd0",
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "de4b0575",
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"id": "369eda91",
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"execution_count": 5,
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"id": "da88ad64",
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"name": "stderr",
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"text": [
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"100%|██████████| 26179/26179 [01:29<00:00, 291.28it/s]\n"
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" 19%|█▉ | 3717/19640 [00:00<00:00, 18600.96it/s]"
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" 18%|█▊ | 3624/19640 [00:00<00:00, 18199.99it/s]"
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"100%|██████████| 19640/19640 [00:01<00:00, 18565.30it/s]\n"
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "ba0c9798",
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"id": "224a0acc",
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"source": [
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@ -465,13 +465,15 @@
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" metrics=[\"acc\"],\n",
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" )\n",
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" \n",
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" model.summary()\n",
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" \n",
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" return model"
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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": 17,
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"id": "08d775aa",
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"id": "8b1920de",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -480,8 +482,6 @@
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"def train(X_train, y_train, X_test, y_test):\n",
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" model = build_model(X_train[0].shape, 52)\n",
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" \n",
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" model.summary()\n",
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" \n",
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" # Create a callback that saves the model's weights\n",
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" model_checkpoint = ModelCheckpoint(filepath=checkpoint_path, monitor='loss', \n",
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"\t\t\tsave_best_only=True)\n",
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "0666829a",
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"id": "830bc0c4",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "adfe695d",
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"id": "52c4202a",
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"metadata": {
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"tags": []
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},
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"output_type": "stream",
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"text": [
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"Loaded weights...\n",
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"CPU times: user 400 ms, sys: 145 ms, total: 545 ms\n",
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"Wall time: 573 ms\n"
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"Model: \"sequential\"\n",
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"flatten (Flatten) (None, 1050) 0 \n",
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"_________________________________________________________________\n",
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"dropout_10.0 (Dropout) (None, 1050) 0 \n",
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"_________________________________________________________________\n",
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"batchNorm (BatchNormalizatio (None, 1050) 4200 \n",
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"_________________________________________________________________\n",
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"HiddenDropout_10 (Dropout) (None, 1050) 0 \n",
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"_________________________________________________________________\n",
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"Hidden_1 (Dense) (None, 525) 551775 \n",
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"_________________________________________________________________\n",
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"HiddenDropout_20 (Dropout) (None, 525) 0 \n",
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"_________________________________________________________________\n",
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"Hidden_2 (Dense) (None, 262) 137812 \n",
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"_________________________________________________________________\n",
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"HiddenDropout_30 (Dropout) (None, 262) 0 \n",
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"_________________________________________________________________\n",
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"Hidden_3 (Dense) (None, 131) 34453 \n",
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"_________________________________________________________________\n",
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"Output (Dense) (None, 52) 6864 \n",
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"=================================================================\n",
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"Total params: 735,104\n",
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"Trainable params: 733,004\n",
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"Non-trainable params: 2,100\n",
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"_________________________________________________________________\n",
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"CPU times: user 338 ms, sys: 217 ms, total: 554 ms\n",
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"Wall time: 574 ms\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": "markdown",
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"id": "35a8741f",
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"id": "a9ac22d1",
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"metadata": {},
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"source": [
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"# Evaluation"
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "f0fa25f6",
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"id": "6c7a4243",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "fc983eae",
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"id": "81f43c20",
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"metadata": {},
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"outputs": [
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{
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" macro avg 0.79 0.78 0.78 3928\n",
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"weighted avg 0.77 0.77 0.76 3928\n",
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"\n",
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"CPU times: user 1.01 s, sys: 209 ms, total: 1.22 s\n",
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"Wall time: 966 ms\n"
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"CPU times: user 998 ms, sys: 195 ms, total: 1.19 s\n",
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"Wall time: 963 ms\n"
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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": 22,
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"id": "70716586",
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"id": "3fbedc43",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"id": "d28c3de8",
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"execution_count": 23,
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"id": "12fec3a9",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"exit()"
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]
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}
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],
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"metadata": {
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