是否可以将Keras's scikit-learn API与 fit_generator()
方法一起使用?或者用另一种方式产生培训批次?我输入到Keras之前必须转换为NumPy数组的稀疏矩阵,但由于内存消耗很高,我无法同时转换它们 . 这是我产生批次的功能:
def batch_generator(X, y, batch_size):
n_splits = len(X) // (batch_size - 1)
X = np.array_split(X, n_splits)
y = np.array_split(y, n_splits)
while True:
for i in range(len(X)):
X_batch = []
y_batch = []
for ii in range(len(X[i])):
X_batch.append(X[i][ii].toarray().astype(np.int8)) # conversion sparse matrix -> np.array
y_batch.append(y[i][ii])
yield (np.array(X_batch), np.array(y_batch))
和交叉验证的示例代码:
from sklearn.model_selection import StratifiedKFold, GridSearchCV
from sklearn import datasets
from keras.models import Sequential
from keras.layers import Activation, Dense
from keras.wrappers.scikit_learn import KerasClassifier
import numpy as np
def build_model(n_hidden=32):
model = Sequential([
Dense(n_hidden, input_dim=4),
Activation("relu"),
Dense(n_hidden),
Activation("relu"),
Dense(3),
Activation("sigmoid")
])
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
iris = datasets.load_iris()
X = iris["data"]
y = iris["target"].flatten()
param_grid = {
"n_hidden": np.array([4, 8, 16]),
"nb_epoch": np.array(range(50, 61, 5))
}
model = KerasClassifier(build_fn=build_model, verbose=0)
skf = StratifiedKFold(n_splits=5).split(X, y) # this yields (train_indices, test_indices)
grid = GridSearchCV(model, param_grid, cv=skf, verbose=2, n_jobs=4)
grid.fit(X, y)
print(grid.best_score_)
print(grid.cv_results_["params"][grid.best_index_])
为了进一步解释,它使用 param_grid
中所有可能的超参数组合来构建模型 . 然后在 StratifiedKFold
提供的列车测试数据分割(折叠)上逐个训练和测试每个模型 . 然后给定模型的最终得分是来自所有折叠的平均得分 .
So is it somehow possible to insert some preprocessing substep to the code above to convert data (sparse matrices) before the actual fitting?
我知道我可以编写自己的交叉验证生成器,但它必须产生索引,而不是真实的数据!
1 回答
实际上,您可以使用稀疏矩阵作为带有生成器的Keras的输入 . 这是我在以前的项目上工作的版本: