グリッドサーチの実行結果の表示を減らしたい。

Question

グリッドサーチを実行すると、実行結果（過程）が、ズラズラ～と表示されます。
これを、最終行のベストパラメータとscoreだけ（２行だけ）表示するにはどうすればよいでしょうか？
よろしくお願いいたします。

#　グリッドサーチ（実行）
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
# KerasClassifier/KerasRegressor can be used as same as scikit_learn estimator.
model = KerasClassifier(build_fn=create_model)

# Grid Search parameters (epochs, batch size and optimizer)
optimizers = ['adam']
init = [ 'normal']
epochs = [10]
batches = [5]
param_grid = dict(optimizer=optimizers, epochs=epochs, batch_size=batches,
                  init=init)
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(x_train_std, y_train)

# summarize results
# print("Best parameter set: {}".format(grid_result.best_params_))
print(grid.best_params_) 
print(grid.best_score_)

Epoch 1/10
320/320 [==============================] - 4s 12ms/step - loss: 0.4890 - acc: 0.7875
Epoch 2/10
320/320 [==============================] - 0s 477us/step - loss: 0.4209 - acc: 0.8156
Epoch 3/10
320/320 [==============================] - 0s 479us/step - loss: 0.3637 - acc: 0.8406
Epoch 4/10
320/320 [==============================] - 0s 468us/step - loss: 0.3355 - acc: 0.8469
Epoch 5/10
320/320 [==============================] - 0s 461us/step - loss: 0.3141 - acc: 0.8469
Epoch 6/10
320/320 [==============================] - 0s 470us/step - loss: 0.2729 - acc: 0.8938
Epoch 7/10
320/320 [==============================] - 0s 465us/step - loss: 0.2629 - acc: 0.8844
Epoch 8/10
320/320 [==============================] - 0s 466us/step - loss: 0.2447 - acc: 0.9094
Epoch 9/10
320/320 [==============================] - 0s 467us/step - loss: 0.2171 - acc: 0.9000
Epoch 10/10
320/320 [==============================] - 0s 469us/step - loss: 0.2007 - acc: 0.9250
160/160 [==============================] - 2s 10ms/step
Epoch 1/10
320/320 [==============================] - 4s 12ms/step - loss: 0.5110 - acc: 0.7438
Epoch 2/10
320/320 [==============================] - 0s 625us/step - loss: 0.4325 - acc: 0.7875
Epoch 3/10
320/320 [==============================] - 0s 700us/step - loss: 0.3702 - acc: 0.8313
Epoch 4/10
320/320 [==============================] - 0s 642us/step - loss: 0.3597 - acc: 0.8375
Epoch 5/10
320/320 [==============================] - 0s 636us/step - loss: 0.3022 - acc: 0.8813
Epoch 6/10
320/320 [==============================] - 0s 634us/step - loss: 0.2885 - acc: 0.8844
Epoch 7/10
320/320 [==============================] - 0s 649us/step - loss: 0.2553 - acc: 0.9063
Epoch 8/10
320/320 [==============================] - 0s 647us/step - loss: 0.2383 - acc: 0.9094
Epoch 9/10
320/320 [==============================] - 0s 690us/step - loss: 0.2311 - acc: 0.8906
Epoch 10/10
320/320 [==============================] - 0s 654us/step - loss: 0.2031 - acc: 0.9375
160/160 [==============================] - 2s 10ms/step
Epoch 1/10
320/320 [==============================] - 4s 12ms/step - loss: 0.5758 - acc: 0.7313
Epoch 2/10
320/320 [==============================] - 0s 739us/step - loss: 0.4505 - acc: 0.8094
Epoch 3/10
320/320 [==============================] - 0s 826us/step - loss: 0.3628 - acc: 0.8344
Epoch 4/10
320/320 [==============================] - 0s 650us/step - loss: 0.3599 - acc: 0.8469
Epoch 5/10
320/320 [==============================] - 0s 655us/step - loss: 0.3309 - acc: 0.8563
Epoch 6/10
320/320 [==============================] - 0s 640us/step - loss: 0.2848 - acc: 0.8844
Epoch 7/10
320/320 [==============================] - 0s 660us/step - loss: 0.2653 - acc: 0.8969
Epoch 8/10
320/320 [==============================] - 0s 641us/step - loss: 0.2523 - acc: 0.9094
Epoch 9/10
320/320 [==============================] - 0s 672us/step - loss: 0.2239 - acc: 0.9219
Epoch 10/10
320/320 [==============================] - 0s 530us/step - loss: 0.1995 - acc: 0.9469
160/160 [==============================] - 2s 10ms/step
Epoch 1/10
480/480 [==============================] - 4s 8ms/step - loss: 0.5254 - acc: 0.7667
Epoch 2/10
480/480 [==============================] - 0s 534us/step - loss: 0.4089 - acc: 0.8188
Epoch 3/10
480/480 [==============================] - 0s 523us/step - loss: 0.3696 - acc: 0.8292
Epoch 4/10
480/480 [==============================] - 0s 553us/step - loss: 0.3448 - acc: 0.8396
Epoch 5/10
480/480 [==============================] - 0s 551us/step - loss: 0.3042 - acc: 0.8833
Epoch 6/10
480/480 [==============================] - 0s 548us/step - loss: 0.2843 - acc: 0.8792
Epoch 7/10
480/480 [==============================] - 0s 525us/step - loss: 0.2667 - acc: 0.9000
Epoch 8/10
480/480 [==============================] - 0s 506us/step - loss: 0.2410 - acc: 0.9125
Epoch 9/10
480/480 [==============================] - 0s 520us/step - loss: 0.2372 - acc: 0.9042
Epoch 10/10
480/480 [==============================] - 0s 512us/step - loss: 0.2076 - acc: 0.9313
{'batch_size': 5, 'epochs': 10, 'init': 'normal', 'optimizer': 'adam'}
0.8062500103066365

Answer 1

このログを出しているのは GridSearchCV ではなく、Keras のほうです。
ログを抑制するには KerasClassifier に verbose=0 を指定してください。

- model = KerasClassifier(build_fn=create_model)
+ model = KerasClassifier(build_fn=create_model, verbose=0)

Comments

[python_2019]

一発で解決しました！
早々のご連絡、どうもありがとうございました。