Keras prediction from saved model

I created a regression ML model in Keras on a different PC, saved the model, and now I am attempting to create an addition pandas dataframe of 'modeled' data. And finally .sum() the modelled data.

There is not a lot of wisdom in this experiment, so my methods could most like be improved on... Feel free to give me any tips :)

This script below will take my .h5 model weights and I can recreate the model architecture to make some predictions in keras. Ultimately at the bottom I am attempting to create the additional pandas df, with a data['calcKwh'] = data['kWh'].apply(lambda x: method..

Opening up the file in Excel, this is how the data looks. One part I am not sure is correct is I am attempting utilize rows in the CSV `data.loc[: , "runTime":"vacation"] Runtime thru vacation, these are my input variables. And the target variable highlighted yellow is kWh.

enter image description here

Scipt.py file runs and calculates 'values' but it doesn't appear to calculate just one sum of the calcKwh df… I am not sure where I am going wrong..

from keras.models import load_model

from keras.models import Sequential

from keras.layers import Dense

import os

import numpy as np

import pandas as pd

import math





#path to saved model

weights_path = "C:/Users/bbartling/Desktop/EC/kwhFinal_backup.h5"



#load data

data = pd.read_csv("joinedRuntime2.csv", index_col='Date', parse_dates=True)



def create_model():

    model = Sequential()

    model.add(Dense(60, input_dim=7, kernel_initializer='normal', activation='relu'))

    model.add(Dense(55, kernel_initializer='normal', activation='relu'))

    model.add(Dense(50, kernel_initializer='normal', activation='relu'))

    model.add(Dense(45, kernel_initializer='normal', activation='relu'))

    model.add(Dense(30, kernel_initializer='normal', activation='relu'))

    model.add(Dense(20, kernel_initializer='normal', activation='relu'))

    model.add(Dense(1, kernel_initializer='normal'))

    return model





def load_trained_model(weights_path):

    model = create_model()

    model.load_weights(weights_path)

    return model





#select columns in the CSV runTime thru vacation

params = np.array(data.loc[: , "runTime":"vacation"])





if (params.ndim == 1):

    params = np.array([params])



data['calcKwh'] = data['kWh'].apply(lambda x: load_trained_model(weights_path).predict(params))



print(data['calcKwh'].sum())

asked 2 days ago

HenryHub

1567

add a comment |

I created a regression ML model in Keras on a different PC, saved the model, and now I am attempting to create an addition pandas dataframe of 'modeled' data. And finally .sum() the modelled data.

There is not a lot of wisdom in this experiment, so my methods could most like be improved on... Feel free to give me any tips :)

enter image description here

Scipt.py file runs and calculates 'values' but it doesn't appear to calculate just one sum of the calcKwh df… I am not sure where I am going wrong..

from keras.models import load_model

from keras.models import Sequential

from keras.layers import Dense

import os

import numpy as np

import pandas as pd

import math





#path to saved model

weights_path = "C:/Users/bbartling/Desktop/EC/kwhFinal_backup.h5"



#load data

data = pd.read_csv("joinedRuntime2.csv", index_col='Date', parse_dates=True)



def create_model():

    model = Sequential()

    model.add(Dense(60, input_dim=7, kernel_initializer='normal', activation='relu'))

    model.add(Dense(55, kernel_initializer='normal', activation='relu'))

    model.add(Dense(50, kernel_initializer='normal', activation='relu'))

    model.add(Dense(45, kernel_initializer='normal', activation='relu'))

    model.add(Dense(30, kernel_initializer='normal', activation='relu'))

    model.add(Dense(20, kernel_initializer='normal', activation='relu'))

    model.add(Dense(1, kernel_initializer='normal'))

    return model





def load_trained_model(weights_path):

    model = create_model()

    model.load_weights(weights_path)

    return model





#select columns in the CSV runTime thru vacation

params = np.array(data.loc[: , "runTime":"vacation"])





if (params.ndim == 1):

    params = np.array([params])



data['calcKwh'] = data['kWh'].apply(lambda x: load_trained_model(weights_path).predict(params))



print(data['calcKwh'].sum())

asked 2 days ago

HenryHub

1567

add a comment |

I created a regression ML model in Keras on a different PC, saved the model, and now I am attempting to create an addition pandas dataframe of 'modeled' data. And finally .sum() the modelled data.

There is not a lot of wisdom in this experiment, so my methods could most like be improved on... Feel free to give me any tips :)

enter image description here

Scipt.py file runs and calculates 'values' but it doesn't appear to calculate just one sum of the calcKwh df… I am not sure where I am going wrong..

from keras.models import load_model

from keras.models import Sequential

from keras.layers import Dense

import os

import numpy as np

import pandas as pd

import math





#path to saved model

weights_path = "C:/Users/bbartling/Desktop/EC/kwhFinal_backup.h5"



#load data

data = pd.read_csv("joinedRuntime2.csv", index_col='Date', parse_dates=True)



def create_model():

    model = Sequential()

    model.add(Dense(60, input_dim=7, kernel_initializer='normal', activation='relu'))

    model.add(Dense(55, kernel_initializer='normal', activation='relu'))

    model.add(Dense(50, kernel_initializer='normal', activation='relu'))

    model.add(Dense(45, kernel_initializer='normal', activation='relu'))

    model.add(Dense(30, kernel_initializer='normal', activation='relu'))

    model.add(Dense(20, kernel_initializer='normal', activation='relu'))

    model.add(Dense(1, kernel_initializer='normal'))

    return model





def load_trained_model(weights_path):

    model = create_model()

    model.load_weights(weights_path)

    return model





#select columns in the CSV runTime thru vacation

params = np.array(data.loc[: , "runTime":"vacation"])





if (params.ndim == 1):

    params = np.array([params])



data['calcKwh'] = data['kWh'].apply(lambda x: load_trained_model(weights_path).predict(params))



print(data['calcKwh'].sum())

asked 2 days ago

HenryHub

1567

I created a regression ML model in Keras on a different PC, saved the model, and now I am attempting to create an addition pandas dataframe of 'modeled' data. And finally .sum() the modelled data.

There is not a lot of wisdom in this experiment, so my methods could most like be improved on... Feel free to give me any tips :)

enter image description here

Scipt.py file runs and calculates 'values' but it doesn't appear to calculate just one sum of the calcKwh df… I am not sure where I am going wrong..

from keras.models import load_model

from keras.models import Sequential

from keras.layers import Dense

import os

import numpy as np

import pandas as pd

import math





#path to saved model

weights_path = "C:/Users/bbartling/Desktop/EC/kwhFinal_backup.h5"



#load data

data = pd.read_csv("joinedRuntime2.csv", index_col='Date', parse_dates=True)



def create_model():

    model = Sequential()

    model.add(Dense(60, input_dim=7, kernel_initializer='normal', activation='relu'))

    model.add(Dense(55, kernel_initializer='normal', activation='relu'))

    model.add(Dense(50, kernel_initializer='normal', activation='relu'))

    model.add(Dense(45, kernel_initializer='normal', activation='relu'))

    model.add(Dense(30, kernel_initializer='normal', activation='relu'))

    model.add(Dense(20, kernel_initializer='normal', activation='relu'))

    model.add(Dense(1, kernel_initializer='normal'))

    return model





def load_trained_model(weights_path):

    model = create_model()

    model.load_weights(weights_path)

    return model





#select columns in the CSV runTime thru vacation

params = np.array(data.loc[: , "runTime":"vacation"])





if (params.ndim == 1):

    params = np.array([params])



data['calcKwh'] = data['kWh'].apply(lambda x: load_trained_model(weights_path).predict(params))



print(data['calcKwh'].sum())

machine-learning python keras regression data-science-model

asked 2 days ago

HenryHub

1567

asked 2 days ago

HenryHub

1567

asked 2 days ago

HenryHub

1567

asked 2 days ago

HenryHub

1567

asked 2 days ago

HenryHub

1567

add a comment |

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