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estamos
estamos
  • 157
  • 1
  • 13

How can I write some code so it can take as input hex addresses and convert them to int ? 

eg the following .xlsx file containing 400.000 samples

dataset

0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c

My config .json file is the following

sp.csv

{
    "data": {
        "filename": "sp500.csv",
        "columns": [
            "Close",
            "Volume"
        ],
        "sequence_length": 50,
        "train_test_split": 0.85,
        "normalise": true
    },
    "training": {
        "epochs": 2,
        "batch_size": 32
    },
    "model": {
        "loss": "mse",
        "optimizer": "adam",
        "save_dir": "saved_models",
        "layers": [
            {
                "type": "lstm",
                "neurons": 100,
                "input_timesteps": 49,
                "input_dim": 2,
                "return_seq": true
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "lstm",
                "neurons": 100,
                "return_seq": true
            },
            {
                "type": "lstm",
                "neurons": 100,
                "return_seq": false
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "dense",
                "neurons": 1,
                "activation": "linear"
            }
        ]
    }
}

Length is 999 and I want to predict the 1000th .

How can I write some code so it can take as input hex addresses and convert them to int ? eg the following .xlsx file containing 400.000 samples

dataset

0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c

My config .json file is the following

sp.csv

{
    "data": {
        "filename": "sp500.csv",
        "columns": [
            "Close",
            "Volume"
        ],
        "sequence_length": 50,
        "train_test_split": 0.85,
        "normalise": true
    },
    "training": {
        "epochs": 2,
        "batch_size": 32
    },
    "model": {
        "loss": "mse",
        "optimizer": "adam",
        "save_dir": "saved_models",
        "layers": [
            {
                "type": "lstm",
                "neurons": 100,
                "input_timesteps": 49,
                "input_dim": 2,
                "return_seq": true
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "lstm",
                "neurons": 100,
                "return_seq": true
            },
            {
                "type": "lstm",
                "neurons": 100,
                "return_seq": false
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "dense",
                "neurons": 1,
                "activation": "linear"
            }
        ]
    }
}

Length is 999 and I want to predict the 1000th .

How can I write some code so it can take as input hex addresses and convert them to int ? 

eg the following .xlsx file containing 400.000 samples

0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
Source Link
estamos
estamos
  • 157
  • 1
  • 13

Convert input dataset given in hex addresses to int

I have created an LSTM Neural Network which take as input the following format in an .csv file

sinewave
0.841470985
0.873736397
0.90255357
0.927808777
0.949402346
0.967249058
0.98127848
0.991435244

How can I write some code so it can take as input hex addresses and convert them to int ? eg the following .xlsx file containing 400.000 samples

dataset

0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
0xbfb22b10
0xbfb22b0c

My config .json file is the following

sp.csv

{
    "data": {
        "filename": "sp500.csv",
        "columns": [
            "Close",
            "Volume"
        ],
        "sequence_length": 50,
        "train_test_split": 0.85,
        "normalise": true
    },
    "training": {
        "epochs": 2,
        "batch_size": 32
    },
    "model": {
        "loss": "mse",
        "optimizer": "adam",
        "save_dir": "saved_models",
        "layers": [
            {
                "type": "lstm",
                "neurons": 100,
                "input_timesteps": 49,
                "input_dim": 2,
                "return_seq": true
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "lstm",
                "neurons": 100,
                "return_seq": true
            },
            {
                "type": "lstm",
                "neurons": 100,
                "return_seq": false
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "dense",
                "neurons": 1,
                "activation": "linear"
            }
        ]
    }
}

Length is 999 and I want to predict the 1000th .