def Training_click(self,
orgPath,
batch_size,
epochs,
feature=None,
valPath=None):
orgLrn_Path = orgPath[0]
orgTrg_Path = orgPath[1]
#---------------
# 型変換
#---------------
if batch_size != int:
batch_size = int(batch_size)
if epochs != int:
epochs = int(epochs)
#---------------------
# データの読み込み
#---------------------
x = dataLoad(orgLrn_Path, float)
t = dataLoad(orgTrg_Path, float)
#-------------------------------
# DataFeature
#-------------------------------
if feature != None:
x = Datafeature(x, feature)
t = Datafeature(t, feature)
#-----------------------------
# Validationデータの読み込み
#-----------------------------
if valPath != None:
valLRN_Path = valPath[0]
valTrg_Path = valPath[1]
x_val = dataLoad(valLRN_Path, float)
t_val = dataLoad(valTrg_Path, float)
#-------------------------------
# DataFeature
#-------------------------------
if feature != None:
x_val = Datafeature(x_val, feature)
t_val = Datafeature(t_val, feature)
validation = (x_val, t_val)
else:
validation = None
# 学習曲線を可視化するコールバックを用意する
higher_better_metrics = ['r2']
visualize_cb = LearningVisualizationCallback(higher_better_metrics)
callbacks = [
visualize_cb,
]
self.model.fit(x=x,
t=t,
batch_size=batch_size,
epochs=epochs,
validation=validation,
callbacks=callbacks)
def nn(x, t, batch_size, epochs, feature=None, validation=None):
"""
簡単なニューラルネットワークのモデルを作成する関数
Parameters
----------
x : ndarray
学習用データ
t : ndarray
教師データ
batch_size : int
バッチサイズ
eopchs : int
エポック数
feature : int
Feature Scalingの選択
"""
#-------------------------------
# DataFeature
#-------------------------------
if feature != None:
x = Datafeature(x, feature)
t = Datafeature(t, feature)
#-------------------------------
# Validation
#-------------------------------
if validation != None: # バリデーションが最初からセットされているとき
x_val = validation[0]
t_val = validation[1]
else:
x = __shuffle__(x)
t = __shuffle__(t)
x_val, x = __sorting__(x, 100)
t_val, t = __sorting__(t, 100)
# 学習曲線を可視化するコールバックを用意する
higher_better_metrics = ['r2']
visualize_cb = LearningVisualizationCallback(higher_better_metrics)
callbacks = [
visualize_cb,
]
model = Sequential()
model.add(Input(input_shape=x.shape[1]))
model.add(Dense(50, activation='relu', weight_initializer='relu'))
model.add(Dense(50, activation='relu', weight_initializer='relu'))
#model.add(Dense(50, activation='sigmoid', weight_initializer='sigmoid'))
#model.add(Dense(50, activation='sigmoid', weight_initializer='sigmoid'))
#model.add(Dense(t.shape[1], activation='softmax'))
#model.compile(loss='cross_entropy_error')
model.add(Dense(t.shape[1], activation = 'liner'))
model.compile(loss='mean_squared_error', metrics = ['r2', 'rsme'])
#history=model.fit(x, t, batch_size=batch_size, epochs=epochs, validation=validation)
history = model.fit(x, t, batch_size=batch_size,
epochs=epochs, validation=(x_val, t_val), callbacks=callbacks)
# lossグラフ
loss = history['loss_ave']
val_loss = history['val_loss']
nb_epoch = len(loss)
plt.plot(range(nb_epoch), loss, marker = '.', label = 'loss')
plt.plot(range(nb_epoch), val_loss, marker='.', label='val_loss')
plt.legend(loc = 'best', fontsize = 10)
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()