CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdeep-learning
AliY
AliY

Reputation: 557

Tensorflow model accuracy low

So my main goal is to use data from 2018 and try to predict data for 2019. I'm using a GRU model and I have the following code. I have a few issues, I'm not sure if the code is actually correct or if I am missing something, and also for model.fit should I use validation_split=0.1 or validation_data=X_test,y_test since I'm using a different dataframe for tesing.

Regarding the accuracy, it is very small and doesn't make any sense and I have no idea why.

enter image description here

import pandas as pd
import tensorflow as tf
from keras.layers.core import Dense
from keras.layers.recurrent import GRU
from keras.models import Sequential
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorboardcolab import TensorBoardColab, TensorBoardColabCallback


df = pd.read_csv('IF 10 PERCENT.csv',index_col=None)



#Loading Second Dataframe


df2 = pd.read_csv('2019 10minutes IF 10 PERCENT.csv',index_col=None)


tbc=TensorBoardColab() # Tensorboard




X_train= df[['WindSpeed_mps','AmbTemp_DegC','RotorSpeed_rpm','RotorSpeedAve','NacelleOrientation_Deg','MeasuredYawError','Pitch_Deg','WindSpeed1','WindSpeed2','WindSpeed3','GeneratorTemperature_DegC','GearBoxTemperature_DegC']]
X_train=X_train.values

y_train= df['Power_kW']
y_train=y_train.values

X_test= df2[['WindSpeed_mps','AmbTemp_DegC','RotorSpeed_rpm','RotorSpeedAve','NacelleOrientation_Deg','MeasuredYawError','Pitch_Deg','WindSpeed1','WindSpeed2','WindSpeed3','GeneratorTemperature_DegC','GearBoxTemperature_DegC']]
X_test=X_test.values

y_test= df2['Power_kW']
y_test=y_test.values

# conversion to numpy array



# scaling values for model


x_scale = MinMaxScaler()
y_scale = MinMaxScaler()

X_train= x_scale.fit_transform(X_train)
y_train= y_scale.fit_transform(y_train.reshape(-1,1))
X_test=x_scale.fit_transform(X_test)
y_test=y_scale.fit_transform(y_test.reshape(-1,1))



X_train = X_train.reshape((-1,1,12)) 
X_test = X_test.reshape((-1,1,12))


# splitting train and test




# creating model using Keras
model = Sequential()
model.add(GRU(units=512, return_sequences=True, input_shape=(1,12)))
model.add(GRU(units=256, return_sequences=True))
model.add(GRU(units=256))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss=['mse'], optimizer='adam',metrics=['accuracy']) 
model.summary() 

#model.fit(X_train, y_train, batch_size=250, epochs=10, validation_split=0.1, verbose=1, callbacks=[TensorBoardColabCallback(tbc)])  
model.fit(X_train, y_train, batch_size=250, epochs=10, validation_data=(X_test,y_test), verbose=1, callbacks=[TensorBoardColabCallback(tbc)])

score = model.evaluate(X_test, y_test)
print('Score: {}'.format(score))
print('Accuracy: {}'.format(acc))
y_predicted = model.predict(X_test)
y_predicted = y_scale.inverse_transform(y_predicted)
y_t
est = y_scale.inverse_transform(y_test)
plt.plot(y_predicted, label='Predicted')
plt.plot(y_test, label='Measurements')
plt.legend()
plt.show()

Thank you

Upvotes: 0

Views: 176

Answers (1)

alift
alift

Reputation: 1928

It sounds to me that you are trying to solve a regression problem here. if it is so, It does not make sense to measure accuracy as a metric, since accuracy is about to measure the exact label matching. MSE should be pretty good for the regression

Upvotes: 3

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