Regression loss function is nan
1
$begingroup$
I'm beginner with ANN and DL in general. I have a regression task with target of 2 dimensions, my dataset has only 46 samples (small dataset I think).
I tried the code (below) for a regression with only 1 output, it works normally.
When I change to 2-dimensions Regression, I get a loss function = NaN
I tried to change the optimizer and fix the dropout rate, but nothing changed, any solution?
thank you for your help in advance.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
#load data
dataframe = pandas.read_excel("data1.xlsx")
dataframe.isnull().any()
dataset = dataframe.values
#print (dataset)
X = dataset[:, 0:5]
Y = dataset[:,5:7]
def baseline_model():
#create model
model = Sequential()
model.add(Dense(10, input_dim=5, kernel_initializer='normal', activation
='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, kernel_initializer='normal'))
#compile model
#model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
model.compile(loss='mean_squared_error', optimizer='adam')
return model
#fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
estimators=
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50,
batch_size=32, verbose=0)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10, random_state = seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print ("Result: %.2f (%.2f) MSE" %(results.mean(), results.std()))
python neural-network keras regression
asked 15 mins ago
Kikio.1Kikio.1
62
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add a comment |
1
$begingroup$
I'm beginner with ANN and DL in general. I have a regression task with target of 2 dimensions, my dataset has only 46 samples (small dataset I think).
I tried the code (below) for a regression with only 1 output, it works normally.
When I change to 2-dimensions Regression, I get a loss function = NaN
I tried to change the optimizer and fix the dropout rate, but nothing changed, any solution?
thank you for your help in advance.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
#load data
dataframe = pandas.read_excel("data1.xlsx")
dataframe.isnull().any()
dataset = dataframe.values
#print (dataset)
X = dataset[:, 0:5]
Y = dataset[:,5:7]
def baseline_model():
#create model
model = Sequential()
model.add(Dense(10, input_dim=5, kernel_initializer='normal', activation
='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, kernel_initializer='normal'))
#compile model
#model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
model.compile(loss='mean_squared_error', optimizer='adam')
return model
#fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
estimators=
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50,
batch_size=32, verbose=0)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10, random_state = seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print ("Result: %.2f (%.2f) MSE" %(results.mean(), results.std()))
python neural-network keras regression
asked 15 mins ago
Kikio.1Kikio.1
62
New contributor
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
1
1
1
$begingroup$
I'm beginner with ANN and DL in general. I have a regression task with target of 2 dimensions, my dataset has only 46 samples (small dataset I think).
I tried the code (below) for a regression with only 1 output, it works normally.
When I change to 2-dimensions Regression, I get a loss function = NaN
I tried to change the optimizer and fix the dropout rate, but nothing changed, any solution?
thank you for your help in advance.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
#load data
dataframe = pandas.read_excel("data1.xlsx")
dataframe.isnull().any()
dataset = dataframe.values
#print (dataset)
X = dataset[:, 0:5]
Y = dataset[:,5:7]
def baseline_model():
#create model
model = Sequential()
model.add(Dense(10, input_dim=5, kernel_initializer='normal', activation
='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, kernel_initializer='normal'))
#compile model
#model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
model.compile(loss='mean_squared_error', optimizer='adam')
return model
#fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
estimators=
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50,
batch_size=32, verbose=0)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10, random_state = seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print ("Result: %.2f (%.2f) MSE" %(results.mean(), results.std()))
python neural-network keras regression
asked 15 mins ago
Kikio.1Kikio.1
62
New contributor
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm beginner with ANN and DL in general. I have a regression task with target of 2 dimensions, my dataset has only 46 samples (small dataset I think).
I tried the code (below) for a regression with only 1 output, it works normally.
When I change to 2-dimensions Regression, I get a loss function = NaN
I tried to change the optimizer and fix the dropout rate, but nothing changed, any solution?
thank you for your help in advance.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
#load data
dataframe = pandas.read_excel("data1.xlsx")
dataframe.isnull().any()
dataset = dataframe.values
#print (dataset)
X = dataset[:, 0:5]
Y = dataset[:,5:7]
def baseline_model():
#create model
model = Sequential()
model.add(Dense(10, input_dim=5, kernel_initializer='normal', activation
='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, kernel_initializer='normal'))
#compile model
#model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
model.compile(loss='mean_squared_error', optimizer='adam')
return model
#fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
estimators=
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50,
batch_size=32, verbose=0)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10, random_state = seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print ("Result: %.2f (%.2f) MSE" %(results.mean(), results.std()))
python neural-network keras regression
python neural-network keras regression
asked 15 mins ago
Kikio.1Kikio.1
62
New contributor
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 15 mins ago
Kikio.1Kikio.1
62
New contributor
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 15 mins ago
Kikio.1Kikio.1
62
New contributor
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 15 mins ago
Kikio.1Kikio.1
62
asked 15 mins ago
Kikio.1Kikio.1
62
62
New contributor
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Kikio.1 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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