python - 错误优化器参数在 Keras 函数中不合法

Question

我正在使用以下代码计算数据生成质量指标的拟合优度研究的概率标签。

from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV, cross_val_score
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.optimizers import Adam
from sklearn.metrics import accuracy_score

def baseline_model(X, optimizer='adam', learn_rate=0.1):
    model = Sequential()
    model.add(Dense(100, input_dim=X.shape[1], activation='relu'))
    model.add(Dense(50, activation='relu'))  # 8 is the dim/ the number of hidden units (units are the kernel)
    model.add(Dense(2, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    
    return model


def get_probability_labels(x, y, optimizer='adam'):
    all_predictions = []
    estimator = KerasClassifier(optimizer=optimizer, batch_size=32, epochs=100, build_fn=baseline_model(x), verbose=0)
    for train_index, test_index in cv_5.split(x, y):
        X_train, X_test = x.iloc[train_index], x.iloc[test_index]
        y_train, y_test = y.iloc[train_index], y.iloc[test_index]

        estimator.fit(X_train, y_train)
        predictions = estimator.predict_proba(X_test)
        predictions = list(predictions[:, 1])
        all_predictions.append(predictions)
        a = [j for i in all_predictions for j in i] #remove nested list
    return a

def add_labels(real_data, synthetic_data):

    # add labels 0 for real and 1 for synthetic
    data = pd.concat([real_data, synthetic_data], ignore_index=True)
    o_labels = np.zeros((len(real_data)), dtype=int)
    s_labels = np.ones((len(synthetic_data)), dtype=int)
    labels = np.concatenate([o_labels, s_labels], axis=0)
    data['class'] = labels
    x = data.drop('class', axis=1)
    y = data['class']

    return x, y

def main():
    X, Y = add_labels(df, df_synth)
    probability_labels = get_probability_labels(X, Y)
    print(probability_labels)

当我运行代码时，我收到与 optimizer 参数相关的错误，我不知道如何解决以使其正常工作。

  File "/home/*", line 42, in get_probability_labels
    estimator = KerasClassifier(optimizer=optimizer, batch_size=32, epochs=100, build_fn=baseline_model(x), verbose=0)

  File "/home/*", line 77, in __init__
    self.check_params(sk_params)

  File "/home/*", line 106, in check_params
    raise ValueError('{} is not a legal parameter'.format(params_name))

ValueError: optimizer is not a legal parameter

Answer 1

已修复，这应该可以工作!问题是我找不到将 X 作为参数传递给 baseline_model 函数的方法。

from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV, cross_val_score
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.optimizers import Adam
from sklearn.metrics import accuracy_score

#removed param X
def baseline_model(optimizer='adam', learn_rate=0.1):
    model = Sequential()
    model.add(Dense(100, input_dim=X.shape[1], activation='relu'))
    model.add(Dense(50, activation='relu'))  # 8 is the dim/ the number of hidden units (units are the kernel)
    model.add(Dense(2, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    
    return model


def get_probability_labels(x, y, optimizer='adam'):
    all_predictions = []
    cv_5 = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)
    estimator = KerasClassifier(optimizer=optimizer, batch_size=32, epochs=100, build_fn=baseline_model, verbose=0)
    for train_index, test_index in cv_5.split(x, y):
        X_train, X_test = x.iloc[train_index], x.iloc[test_index]
        y_train, y_test = y.iloc[train_index], y.iloc[test_index]

        estimator.fit(X_train, y_train)
        # change predict_proba (deprecated) and remove list(predictions[:, 1])
        predictions = estimator.predict(X_test)
        all_predictions.append(predictions)
        a = [j for i in all_predictions for j in i] #remove nested list
    return a

def add_labels(real_data, synthetic_data):

    # add labels 0 for real and 1 for synthetic
    data = pd.concat([real_data, synthetic_data], ignore_index=True)
    o_labels = np.zeros((len(real_data)), dtype=int)
    s_labels = np.ones((len(synthetic_data)), dtype=int)
    labels = np.concatenate([o_labels, s_labels], axis=0)
    data['class'] = labels
    x = data.drop('class', axis=1)
    y = data['class']

    return x, y

# other file
def main():
    X, Y = add_labels(df, df_synth)
    probability_labels = get_probability_labels(X, Y)
    print(probability_labels)