Why doesn't loss go down during Neural Net training?
0
$begingroup$
I am working on a Kaggle competition and have tried 2 different code approaches and have the same issue: the loss is large (18247478709991652.0000) and does not go down or is nan.
I'm not sure if there is something wrong with the code or with the data. I tried both scaled and non-scaled data and got the same results. I tried it with the full data set (3,000 examples) and an abbreviated data set.
Here is the abbreviated data.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
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
dataframe = pandas.read_csv('data/tmdb/train_processed.csv')
dataframe.drop('id', axis=1, inplace=True)
Y = dataframe['revenue'].values
dataframe.drop(columns=['revenue'], inplace=True)
X = dataframe.values
def baseline_model():
model = Sequential()
model.add(Dense(13, input_dim=3, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
seed = 7
numpy.random.seed(seed)
estimators =
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=5, verbose=1)))
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()))
neural-network keras tensorflow
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0
$begingroup$
I am working on a Kaggle competition and have tried 2 different code approaches and have the same issue: the loss is large (18247478709991652.0000) and does not go down or is nan.
I'm not sure if there is something wrong with the code or with the data. I tried both scaled and non-scaled data and got the same results. I tried it with the full data set (3,000 examples) and an abbreviated data set.
Here is the abbreviated data.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
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
dataframe = pandas.read_csv('data/tmdb/train_processed.csv')
dataframe.drop('id', axis=1, inplace=True)
Y = dataframe['revenue'].values
dataframe.drop(columns=['revenue'], inplace=True)
X = dataframe.values
def baseline_model():
model = Sequential()
model.add(Dense(13, input_dim=3, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
seed = 7
numpy.random.seed(seed)
estimators =
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=5, verbose=1)))
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()))
neural-network keras tensorflow
asked 3 mins ago
B SevenB Seven
1215
New contributor
B Seven 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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add a comment |
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0
0
$begingroup$
I am working on a Kaggle competition and have tried 2 different code approaches and have the same issue: the loss is large (18247478709991652.0000) and does not go down or is nan.
I'm not sure if there is something wrong with the code or with the data. I tried both scaled and non-scaled data and got the same results. I tried it with the full data set (3,000 examples) and an abbreviated data set.
Here is the abbreviated data.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
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
dataframe = pandas.read_csv('data/tmdb/train_processed.csv')
dataframe.drop('id', axis=1, inplace=True)
Y = dataframe['revenue'].values
dataframe.drop(columns=['revenue'], inplace=True)
X = dataframe.values
def baseline_model():
model = Sequential()
model.add(Dense(13, input_dim=3, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
seed = 7
numpy.random.seed(seed)
estimators =
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=5, verbose=1)))
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()))
neural-network keras tensorflow
asked 3 mins ago
B SevenB Seven
1215
New contributor
B Seven is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am working on a Kaggle competition and have tried 2 different code approaches and have the same issue: the loss is large (18247478709991652.0000) and does not go down or is nan.
I'm not sure if there is something wrong with the code or with the data. I tried both scaled and non-scaled data and got the same results. I tried it with the full data set (3,000 examples) and an abbreviated data set.
Here is the abbreviated data.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
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
dataframe = pandas.read_csv('data/tmdb/train_processed.csv')
dataframe.drop('id', axis=1, inplace=True)
Y = dataframe['revenue'].values
dataframe.drop(columns=['revenue'], inplace=True)
X = dataframe.values
def baseline_model():
model = Sequential()
model.add(Dense(13, input_dim=3, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
seed = 7
numpy.random.seed(seed)
estimators =
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=5, verbose=1)))
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()))
neural-network keras tensorflow
neural-network keras tensorflow
asked 3 mins ago
B SevenB Seven
1215
New contributor
B Seven is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
B SevenB Seven
1215
New contributor
B Seven is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
B SevenB Seven
1215
New contributor
B Seven is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
B SevenB Seven
1215
asked 3 mins ago
B SevenB Seven
1215
1215
New contributor
B Seven is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
B Seven is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
B Seven 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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