Reshape values back to original values
I am busy with a Recurrent Neural Network for predicting Cryptocurrencies prices. So the reason I do this project is because of school. I am pretty far with the project, but I ran against a problem. So, in my code I have a dataframe (df). In the dataframe the values are pretty big, so I shaped it to smaller values using this:
for col in df.columns:
if col != "target":
df[col] = df[col].pct_change()
df.dropna(inplace=True)
df[col] = preprocessing.scale(df[col].values)
But after I have put it into the model, I need the values shaped back to original. So, I have tried everything on the internet, but I couldn't find my solution. Can someone help me with this?
EDIT:
I want to scale the values after the model.fit! So when I train the model with this:
# Train model
model.fit(
train_x, train_y,
batch_size=64,
epochs=EPOCHS,
validation_split=0.05,
callbacks=[tensorboard])
How can I do that?
python scikit-learn artificial-intelligence
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
add a comment |
I am busy with a Recurrent Neural Network for predicting Cryptocurrencies prices. So the reason I do this project is because of school. I am pretty far with the project, but I ran against a problem. So, in my code I have a dataframe (df). In the dataframe the values are pretty big, so I shaped it to smaller values using this:
for col in df.columns:
if col != "target":
df[col] = df[col].pct_change()
df.dropna(inplace=True)
df[col] = preprocessing.scale(df[col].values)
But after I have put it into the model, I need the values shaped back to original. So, I have tried everything on the internet, but I couldn't find my solution. Can someone help me with this?
EDIT:
I want to scale the values after the model.fit! So when I train the model with this:
# Train model
model.fit(
train_x, train_y,
batch_size=64,
epochs=EPOCHS,
validation_split=0.05,
callbacks=[tensorboard])
How can I do that?
python scikit-learn artificial-intelligence
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
add a comment |
I am busy with a Recurrent Neural Network for predicting Cryptocurrencies prices. So the reason I do this project is because of school. I am pretty far with the project, but I ran against a problem. So, in my code I have a dataframe (df). In the dataframe the values are pretty big, so I shaped it to smaller values using this:
for col in df.columns:
if col != "target":
df[col] = df[col].pct_change()
df.dropna(inplace=True)
df[col] = preprocessing.scale(df[col].values)
But after I have put it into the model, I need the values shaped back to original. So, I have tried everything on the internet, but I couldn't find my solution. Can someone help me with this?
EDIT:
I want to scale the values after the model.fit! So when I train the model with this:
# Train model
model.fit(
train_x, train_y,
batch_size=64,
epochs=EPOCHS,
validation_split=0.05,
callbacks=[tensorboard])
How can I do that?
python scikit-learn artificial-intelligence
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
I am busy with a Recurrent Neural Network for predicting Cryptocurrencies prices. So the reason I do this project is because of school. I am pretty far with the project, but I ran against a problem. So, in my code I have a dataframe (df). In the dataframe the values are pretty big, so I shaped it to smaller values using this:
for col in df.columns:
if col != "target":
df[col] = df[col].pct_change()
df.dropna(inplace=True)
df[col] = preprocessing.scale(df[col].values)
But after I have put it into the model, I need the values shaped back to original. So, I have tried everything on the internet, but I couldn't find my solution. Can someone help me with this?
EDIT:
I want to scale the values after the model.fit! So when I train the model with this:
# Train model
model.fit(
train_x, train_y,
batch_size=64,
epochs=EPOCHS,
validation_split=0.05,
callbacks=[tensorboard])
How can I do that?
python scikit-learn artificial-intelligence
python scikit-learn artificial-intelligence
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
edited Nov 18 '18 at 21:52
Vreesie
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
asked Nov 18 '18 at 17:46
VreesieVreesie
6212
6212
add a comment |
add a comment |
1 Answer
1
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oldest
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If you want to go back to the original data, you have the prescaler and you can multiply by the standard deviation and add the mean (the opposite of what it does), and then the same for pct_change.
But this will add noise to your data.
The best solution here is to keep your original data, and process it in another dataframe for the network.
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
add a comment |
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1 Answer
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1 Answer
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active
oldest
votes
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oldest
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oldest
votes
If you want to go back to the original data, you have the prescaler and you can multiply by the standard deviation and add the mean (the opposite of what it does), and then the same for pct_change.
But this will add noise to your data.
The best solution here is to keep your original data, and process it in another dataframe for the network.
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
add a comment |
If you want to go back to the original data, you have the prescaler and you can multiply by the standard deviation and add the mean (the opposite of what it does), and then the same for pct_change.
But this will add noise to your data.
The best solution here is to keep your original data, and process it in another dataframe for the network.
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
add a comment |
If you want to go back to the original data, you have the prescaler and you can multiply by the standard deviation and add the mean (the opposite of what it does), and then the same for pct_change.
But this will add noise to your data.
The best solution here is to keep your original data, and process it in another dataframe for the network.
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
If you want to go back to the original data, you have the prescaler and you can multiply by the standard deviation and add the mean (the opposite of what it does), and then the same for pct_change.
But this will add noise to your data.
The best solution here is to keep your original data, and process it in another dataframe for the network.
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
answered Nov 18 '18 at 20:45
Matthieu BrucherMatthieu Brucher
15.6k32140
15.6k32140
add a comment |
add a comment |
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