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updated stock price prediction tutorial

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machine-learning/stock-prediction
- data
  - AAPL_2020-04-28.csv
  - TSLA_2020-01-08.csv
- logs
  - 2020-01-08_AAPL-mse-LSTM-seq-50-step-1-layers-3-units-256
    - train
      - events.out.tfevents.1578493736.DESKTOP-JCAH48A.15628.1854.v2
      - events.out.tfevents.1578493742.DESKTOP-JCAH48A.profile-empty
      - plugins/profile/2020-01-08_15-29-02
        local.trace
    - validation
      - events.out.tfevents.1578493759.DESKTOP-JCAH48A.15628.30250.v2
  - 2020-01-08_AAPL-mse-LSTM-seq-50-step-10-layers-3-units-256
    - train
      - events.out.tfevents.1578498454.DESKTOP-JCAH48A.16656.1854.v2
      - events.out.tfevents.1578498461.DESKTOP-JCAH48A.profile-empty
      - plugins/profile/2020-01-08_16-47-41
        local.trace
    - validation
      - events.out.tfevents.1578498479.DESKTOP-JCAH48A.16656.30250.v2
  - 2020-01-08_AAPL-mse-LSTM-seq-50-step-30-layers-3-units-256
    - train
      - events.out.tfevents.1578500555.DESKTOP-JCAH48A.10452.1854.v2
      - events.out.tfevents.1578500561.DESKTOP-JCAH48A.profile-empty
      - plugins/profile/2020-01-08_17-22-41
        local.trace
    - validation
      - events.out.tfevents.1578500577.DESKTOP-JCAH48A.10452.30250.v2
  - 2020-04-28_AAPL-huber_loss-adam-LSTM-seq-50-step-1-layers-3-units-256
    - train
      - events.out.tfevents.1588085522.DESKTOP-JCAH48A.20284.17166.v2
      - events.out.tfevents.1588085526.DESKTOP-JCAH48A.profile-empty
      - plugins/profile/2020-04-28_16-52-06
        local.trace
    - validation
      - events.out.tfevents.1588085529.DESKTOP-JCAH48A.20284.26053.v2
  - 2020-04-28_AAPL-huber_loss-adam-LSTM-seq-70-step-1-layers-3-units-256
    - train
      - events.out.tfevents.1588088001.DESKTOP-JCAH48A.17916.222502.v2
      - events.out.tfevents.1588088006.DESKTOP-JCAH48A.profile-empty
      - plugins/profile/2020-04-28_17-33-26
        local.trace
    - validation
      - events.out.tfevents.1588088009.DESKTOP-JCAH48A.17916.231389.v2
  - 2020-04-28_AAPL-huber_loss-adam-LSTM-seq-70-step-1-layers-3-units-256-b
    - train
      - events.out.tfevents.1588086485.DESKTOP-JCAH48A.17916.3462.v2
      - events.out.tfevents.1588086499.DESKTOP-JCAH48A.profile-empty
      - plugins/profile/2020-04-28_17-08-19
        local.trace
    - validation
      - events.out.tfevents.1588086505.DESKTOP-JCAH48A.17916.20196.v2
- parameters.py
- results
  - 2020-01-08_AAPL-mse-LSTM-seq-50-step-1-layers-3-units-256
    - saved_model.pb
    - variables
  - 2020-01-08_AAPL-mse-LSTM-seq-50-step-1-layers-3-units-256.h5
  - 2020-04-28_AAPL-huber_loss-adam-LSTM-seq-70-step-1-layers-3-units-256.h5
- stock_prediction.ipynb
- stock_prediction.py
- test.py
- train.py

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`‎machine-learning/stock-prediction/data/AAPL_2020-01-08.csvrenamed to‎machine-learning/stock-prediction/data/AAPL_2020-04-28.csv`

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`‎machine-learning/stock-prediction/data/TSLA_2020-01-08.csv`

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`‎machine-learning/stock-prediction/parameters.py`

Lines changed: 14 additions & 8 deletions

Original file line number	Diff line number	Diff line change
`@@ -4,9 +4,9 @@`
`4`	`4`
`5`	`5`
`6`	`6`	`# Window size or the sequence length`
`7`		`-N_STEPS=100`
	`7`	`+N_STEPS=70`
`8`	`8`	`# Lookup step, 1 is the next day`
`9`		`-LOOKUP_STEP=90`
	`9`	`+LOOKUP_STEP=1`
`10`	`10`
`11`	`11`	`# test ratio size, 0.2 is 20%`
`12`	`12`	`TEST_SIZE=0.2`
`@@ -24,17 +24,23 @@`
`24`	`24`	`UNITS=256`
`25`	`25`	`# 40% dropout`
`26`	`26`	`DROPOUT=0.4`
	`27`	`+# whether to use bidirectional RNNs`
	`28`	`+BIDIRECTIONAL=False`
`27`	`29`
`28`	`30`	`### training parameters`
`29`	`31`
`30`		`-# mean squared error loss`
`31`		`-LOSS="mse"`
`32`		`-OPTIMIZER="rmsprop"`
	`32`	`+# mean absolute error loss`
	`33`	`+# LOSS = "mae"`
	`34`	`+# huber loss`
	`35`	`+LOSS="huber_loss"`
	`36`	`+OPTIMIZER="adam"`
`33`	`37`	`BATCH_SIZE=64`
`34`		`-EPOCHS=300`
	`38`	`+EPOCHS=400`
`35`	`39`
`36`	`40`	`# Apple stock market`
`37`	`41`	`ticker="AAPL"`
`38`	`42`	`ticker_data_filename=os.path.join("data",f"{ticker}_{date_now}.csv")`
`39`		`-# model name to save`
`40`		`-model_name=f"{date_now}_{ticker}-{LOSS}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"`
	`43`	`+# model name to save, making it as unique as possible based on parameters`
	`44`	`+model_name=f"{date_now}_{ticker}-{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"`
	`45`	`+ifBIDIRECTIONAL:`
	`46`	`+model_name+="-b"`