I am new to machine learning, and I am trying to handle Keras to perform regression tasks. I have implemented this code, based on this example.
X = df[['full_sq','floor','build_year','num_room','sub_area_2','sub_area_3','state_2.0','state_3.0','state_4.0']]
y = df['price_doc']
X = np.asarray(X)
y = np.asarray(y)
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=.2)
def baseline_model():
model = Sequential()
model.add(Dense(13, input_dim=9, kernel_initializer='normal',
activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=100, verbose=False)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(estimator, X_train, Y_train, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
prediction = estimator.predict(X_test)
accuracy_score(Y_test, prediction)
When I run the code I get this error:
AttributeError: 'KerasRegressor' object has no attribute 'model'
How could I correctly 'insert' the model in KerasRegressor?
you have to fit the estimator again after cross_val_score
to evaluate on the new data:
estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=100, verbose=False)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(estimator, X_train, Y_train, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
estimator.fit(X, y)
prediction = estimator.predict(X_test)
accuracy_score(Y_test, prediction)
Working Test version:
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score, KFold
from keras.models import Sequential
from sklearn.metrics import accuracy_score
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
seed = 1
diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
def baseline_model():
model = Sequential()
model.add(Dense(10, input_dim=10, activation='relu'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=100, verbose=False)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(estimator, X, y, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
estimator.fit(X, y)
prediction = estimator.predict(X)
accuracy_score(y, prediction)