keras, Gridsearch, エポック1回しか出来てない？

Question

前提・実現したいこと

kerasでIrisデータを使ってgridsearchを試しています．

発生している問題・エラーメッセージ

Epoch1/1と出力されているのですが，epoch2以降は学習されていないのでしょうか？

該当のソースコード

import numpy as np
from sklearn import datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
from keras import backend as K
from keras.wrappers.scikit_learn import KerasClassifier


# import data and divided it into training and test purposes
iris = datasets.load_iris()
x = preprocessing.scale(iris.data)
y = np_utils.to_categorical(iris.target)
x_tr, x_te, y_tr, y_te = train_test_split(x, y, train_size  = 0.7)
num_classes = y_te.shape[1]


# Define model for iris classification
def iris_model(activation="relu", optimizer="adam", out_dim=100):
    model = Sequential()
    model.add(Dense(out_dim, input_dim=4, activation=activation))
    model.add(Dense(out_dim, activation=activation))
    model.add(Dense(num_classes, activation="softmax"))
    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model

# Define options for parameters
activation = ["relu", "sigmoid"]
optimizer = ["adam", "adagrad"]
out_dim = np.array([100, 200])
nb_epoch = np.array([10, 25])
batch_size = np.array([10, 20])


# Retrieve model and parameter into GridSearchCV
model = KerasClassifier(build_fn=iris_model, verbose=1)
param_grid = dict(activation=activation,
                  optimizer=optimizer,
                  out_dim=out_dim,
                  nb_epoch=nb_epoch,
                  batch_size=batch_size)
grid = GridSearchCV(estimator=model, param_grid=param_grid)


# Run grid search
grid_result = grid.fit(x_tr, y_tr)


# Get the best score and the optimized mode
print (grid_result.best_score_)
print (grid_result.best_params_)

# Evaluate the model with test data
grid_eval = grid.predict(x_te)
def y_binary(i):
    if   i == 0: return [1, 0, 0]
    elif i == 1: return [0, 1, 0]
    elif i == 2: return [0, 0, 1]
y_eval = np.array([y_binary(i) for i in grid_eval])
accuracy = (y_eval == y_te)
print (np.count_nonzero(accuracy == True) / (accuracy.shape[0] * accuracy.shape[1]))


# Now see the optimized model
model = iris_model(activation=grid_result.best_params_['activation'],
                   optimizer=grid_result.best_params_['optimizer'],
                   out_dim=grid_result.best_params_['out_dim'])
model.summary()

試したこと

Epochを例えば10000のように膨大な数字にして，本当に指定のエポック数の学習ができているのか試しましたが，特に膨大な時間がかかることもなく学習ができました．

補足情報（FW/ツールのバージョンなど）

Python3.6
Keras2.2.4
sklearn0.19.1

Answer 1

確かに動作してないですね。

Keras 2.0 から nb_epochはepochsに変更されております

https://github.com/keras-team/keras/wiki/Keras-2.0-release-notes

The nb_epoch argument has been renamed epochs everywhere.

ですので

param_grid = dict(activation=activation,
                  optimizer=optimizer,
                  out_dim=out_dim,
                  nb_epoch=nb_epoch,
                  batch_size=batch_size)

の部分を

param_grid = dict(activation=activation,
                  optimizer=optimizer,
                  out_dim=out_dim,
                  epochs=nb_epoch,
                  batch_size=batch_size)

と書き換えてみてください

Comments

[iforin]

回答ありがとうございます！ご指摘の通りに書き換えたところ，指定したエポック数通りに動作するようになりました！