def fit(self, X, y):
X_dict = numpy_to_dict(X)
self.model = self.model_creator(X_dict, y)
self.model.fit(X_dict, y, epochs=self.epochs, batch_size=self.batch_size, verbose=1)
return self
def fit(self, X, y):
X_dict = numpy_to_dict(X)
y = to_categorical(y)
self.model = self.model_creator(X_dict, y)
for (epochs, batch_size) in self.fit_params:
self.model.fit(X_dict, y, epochs=epochs, batch_size=batch_size, verbose=1)
return self
def fit(self, X, y):
X_dict = numpy_to_dict(X)
self._le = LabelEncoder().fit(y)
y = to_categorical(self._le.transform(y))
self.model = self.model_creator(X_dict, y)
self.model.fit(X_dict,
y,
epochs=self.epochs,
batch_size=self.batch_size,
verbose=1)
return self
def predict_proba(self, X):
return self.model.predict(numpy_to_dict(X))