While working with keras, I learned that using a wrapper comes with adverse effects for keras and scikit learn api requests. I am interested in a solution to have both.
Variant 1: scikit Wrapper
from keras.wrappers.scikit_learn import KerasClassifier
def model():
model = Sequential()
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
estimator = KerasClassifier(build_fn=model, epochs=100, batch_size=5)
model.fit(X, y)
-> This lets me print scikit commands such as accuracy_score() or classification_report(). However, model.summary() does not work:
AttributeError: 'KerasClassifier' object has no attribute 'summary'
Variant 2: No Wrapper
model = Sequential()
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=100, batch_size=5)
-> This lets me print model.summary() but not the scikit commands.
ValueError: Mix type of y not allowed, got types {'multiclass', 'multilabel-indicator'}
Is there a way that enables to use both?
KerasClassifier is just a wrapper over the actual Model in keras so that the actual methods of the keras api can be routed to the methods used in scikit, so it can be used in conjunction with scikit utilities. But internally it just uses the model which can be accessed by using estimator.model.
Example illustrating the above:
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.datasets import make_classification
def model():
model = Sequential()
model.add(Dense(10, input_dim=20, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
estimator = KerasClassifier(build_fn=model, epochs=100, batch_size=5)
X, y = make_classification()
estimator.fit(X, y)
# This is what you need
estimator.model.summary()
The output for this is:
Layer (type) Output Shape Param #
=================================================================
dense_9 (Dense) (None, 10) 210
_________________________________________________________________
dense_10 (Dense) (None, 2) 22
=================================================================
Total params: 232
Trainable params: 232
Non-trainable params: 0
_________________________________________________________________