Keras - How to make a forecast using KerasRegressor?

Question

I am new to machine learning and I am trying to process Keras to perform regression tasks. I 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 can I correctly insert a model into KerasRegressor?

Answer 1

you need to substitute the estimate after cross_val_scoreto evaluate 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)

Answer 2

, . KFold 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)

Answer 3

kerasRegressor . :

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)

, shuffle fit() kerasRegressor False. , , , script:

import random as rn 
rn.seed(1)
np.random.seed(1)    
tf.set_random_seed(1)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)