Keras - How to perform a prediction using KerasRegressor?

user_7387749 0
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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?

scikit-learn keras regression machine-learning neural-network
4 Answers

For evaluation of your system performance, you can calculate the error like following. You also do not need to call KFold and cross_val_score.

import numpy as np
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)
estimator.fit(X, y)
prediction = estimator.predict(X)

train_error =  np.abs(y - prediction)
mean_error = np.mean(train_error)
min_error = np.min(train_error)
max_error = np.max(train_error)
std_error = np.std(train_error)
user_68467460

Instead of kerasRegressor, you can directly use model itself. These two snippets of the code give the exact same results:

estimator = KerasRegressor(build_fn=baseline_model)
estimator.fit(X, y, nb_epoch=100, batch_size=100, verbose=False, shuffle=False)
prediction = estimator.predict(X)


model = baseline_model()
model.fit(X, y, nb_epoch=100, batch_size=100, verbose=False, shuffle=False)
prediction = model.predict(X)

Please note that the shuffle argument of fit() function for both kerasRegressor and model needs to be False. Moreover, for having the fixed initial state and obtain reproducible results, you need to add these lines of code at the beginning of your script:

session = K.get_session()
init_op = tf.group(tf.tables_initializer(),tf.global_variables_initializer(), tf.local_variables_initializer())
session.run(init_op)
np.random.seed(1)
tf.set_random_seed(1)
user_68467460

you should train model on X_train and y_train you can not train model on X and y unless you should have extra data for testing

train should be in Train then test/predict should be on X_test.

user_57206380
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