python" y = to_categorical(y)"

这行代码是将一个一维的向量 y 转换为一个二维的矩阵，其中每一行表示一个类别。转换后的矩阵是一个 one-hot 编码形式的矩阵，即每个向量的第 i 个元素为 1，其余元素均为 0，表示该样本属于第 i 个类别。这通常用于将分类问题中的输出标签转换为神经网络可以处理的形式。这个函数是 keras 的一个工具函数，可以直接调用。

相关问题

修改和补充下列代码得到十折交叉验证的平均auc值和平均aoc曲线，平均分类报告以及平均混淆矩阵 min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1) y_pred11 = tree.predict(X_test1) y_pred1.append(y_pred11 X_train.append(X_train1) X_test.append(X_test1) y_test.append(y_test1) y_train.append(y_train1) X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id] y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id] X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1) X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1) X_train_fuzzy1 = np.array(X_train_fuzzy1) X_test_fuzzy1 = np.array(X_test_fuzzy1) config = get_config() tree = gcForest(config) tree.fit(X_train_fuzzy1, y_train_fuzzy1) y_predd = tree.predict(X_test_fuzzy1) y_pred.append(y_predd) X_test_fuzzy.append(X_test_fuzzy1) y_test_fuzzy.append(y_test_fuzzy1)y_pred = to_categorical(np.concatenate(y_pred), num_classes=3) y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3) y_test = to_categorical(np.concatenate(y_test), num_classes=3) y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3) print(y_pred.shape) print(y_pred1.shape) print(y_test.shape) print(y_test_fuzzy.shape) # 深度森林 report1 = classification_report(y_test, y_prprint("DF",report1) report = classification_report(y_test_fuzzy, y_pred) print("DF-F",report) mse = mean_squared_error(y_test, y_pred1) rmse = math.sqrt(mse) print('深度森林RMSE:', rmse) print('深度森林Accuracy:', accuracy_score(y_test, y_pred1)) mse = mean_squared_error(y_test_fuzzy, y_pred) rmse = math.sqrt(mse) print('F深度森林RMSE:', rmse) print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred)) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse) print('F?深度森林RMSE:', rmse) print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))

以下是修改和补充后的代码，实现了十折交叉验证的平均auc值和平均aoc曲线，平均分类报告以及平均混淆矩阵：

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc
from sklearn.model_selection import StratifiedKFold

min_max_scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = [], [], [], []
X_train_fuzzy, X_test_fuzzy, y_train_fuzzy, y_test_fuzzy = [], [], [], []
y_pred, y_pred1 = [], []
y_pred_proba, y_pred_proba1 = [], []

config = get_config()
tree = gcForest(config)

skf = StratifiedKFold(n_splits=10)

for train_id, test_id in skf.split(x, y):
    # split data and normalize
    X_train1, X_test1 = x[train_id], x[test_id]
    y_train1, y_test1 = y[train_id], y[test_id]
    X_train1 = min_max_scaler.fit_transform(X_train1)
    X_test1 = min_max_scaler.transform(X_test1)
    X_train1 = np.array(X_train1)
    X_test1 = np.array(X_test1)

    # train gcForest
    tree.fit(X_train1, y_train1)

    # predict on test set
    y_pred11 = tree.predict(X_test1)
    y_pred_proba11 = tree.predict_proba(X_test1)

    # append predictions and test data
    y_pred1.append(y_pred11)
    y_pred_proba1.append(y_pred_proba11)
    X_train.append(X_train1)
    X_test.append(X_test1)
    y_test.append(y_test1)
    y_train.append(y_train1)

    # split fuzzy data and normalize
    X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id]
    y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id]
    X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1)
    X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1)
    X_train_fuzzy1 = np.array(X_train_fuzzy1)
    X_test_fuzzy1 = np.array(X_test_fuzzy1)

    # train gcForest on fuzzy data
    tree.fit(X_train_fuzzy1, y_train_fuzzy1)

    # predict on fuzzy test set
    y_predd = tree.predict(X_test_fuzzy1)
    y_predd_proba = tree.predict_proba(X_test_fuzzy1)

    # append predictions and test data
    y_pred.append(y_predd)
    y_pred_proba.append(y_predd_proba)
    X_test_fuzzy.append(X_test_fuzzy1)
    y_test_fuzzy.append(y_test_fuzzy1)

# concatenate and convert to categorical
y_pred = to_categorical(np.concatenate(y_pred), num_classes=3)
y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3)
y_test = to_categorical(np.concatenate(y_test), num_classes=3)
y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3)

# calculate and print average accuracy and RMSE
mse = mean_squared_error(y_test, y_pred1)
rmse = math.sqrt(mse)
print('深度森林RMSE:', rmse)
print('深度森林Accuracy:', accuracy_score(y_test, y_pred1))

mse = mean_squared_error(y_test_fuzzy, y_pred)
rmse = math.sqrt(mse)
print('F深度森林RMSE:', rmse)
print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred))

mse = mean_squared_error(y_test, y_pred)
rmse = math.sqrt(mse)
print('F?深度森林RMSE:', rmse)
print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))

# calculate and print average classification report
report1 = classification_report(y_test, y_pred1)
print("DF", report1)

report = classification_report(y_test_fuzzy, y_pred)
print("DF-F", report)

# calculate and print average confusion matrix
cm1 = confusion_matrix(y_test.argmax(axis=1), y_pred1.argmax(axis=1))
cm = confusion_matrix(y_test_fuzzy.argmax(axis=1), y_pred.argmax(axis=1))
print('DF Confusion Matrix:')
print(cm1)
print('DF-F Confusion Matrix:')
print(cm)

# calculate and print average ROC curve and AUC value
fpr1, tpr1, threshold1 = roc_curve(y_test.ravel(), y_pred_proba1.ravel())
fpr, tpr, threshold = roc_curve(y_test_fuzzy.ravel(), y_pred_proba.ravel())
roc_auc1 = auc(fpr1, tpr1)
roc_auc = auc(fpr, tpr)
print('DF ROC AUC:', roc_auc1)
print('DF-F ROC AUC:', roc_auc)

# plot average ROC curve
plt.title('Receiver Operating Characteristic')
plt.plot(fpr1, tpr1, 'b', label = 'DF AUC = %0.2f' % roc_auc1)
plt.plot(fpr, tpr, 'g', label = 'DF-F AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

def load_data(): # 加载数据集 with open('D:/浏览器下载/cifar-100-python/cifar-100-python/train', 'rb') as f: data_dict = pickle.load(f, encoding='bytes') x_train = data_dict[b'data'].reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1) y_train = data_dict[b'fine_labels'] with open('D:/浏览器下载/cifar-100-python/cifar-100-python/test', 'rb') as f: data_dict = pickle.load(f, encoding='bytes') x_test = data_dict[b'data'].reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1) y_test = data_dict[b'fine_labels'] # 归一化图像数据 x_train = x_train / 255.0 x_test = x_test / 255.0 # 将标签进行one-hot编码 num_classes = 100 y_train = to_categorical(y_train, num_classes) y_test = to_categorical(y_test, num_classes) return x_train, y_train, x_test, y_test # 加载数据 x_train, y_train, x_test, y_test = load_data() # 数据增强 train_datagen = ImageDataGenerator( rotation_range=20, horizontal_flip=True, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, fill_mode='nearest') train_datagen.fit(x_train) # 增加回调函数 callbacks = [ EarlyStopping(monitor='val_loss', patience=10, verbose=1), ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1) ] 请在此代码基础上使用tensorflow2.0版本创建神经网络，并给出最终代码

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense

# 加载数据集
def load_data():
with open('D:/浏览器下载/cifar-100-python/cifar-100-python/train', 'rb') as f:
data_dict = pickle.load(f, encoding='bytes')
x_train = data_dict[b'data'].reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
y_train = data_dict[b'fine_labels']

with open('D:/浏览器下载/cifar-100-python/cifar-100-python/test', 'rb') as f:
data_dict = pickle.load(f, encoding='bytes')
x_test = data_dict[b'data'].reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
y_test = data_dict[b'fine_labels']

# 归一化图像数据
x_train = x_train / 255.0
x_test = x_test / 255.0

# 将标签进行one-hot编码
num_classes = 100
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)

return x_train, y_train, x_test, y_test

# 创建神经网络
def create_model():
model = Sequential()

# 卷积层
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# 全连接层
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

return model

# 加载数据
x_train, y_train, x_test, y_test = load_data()

# 数据增强
train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=20,
horizontal_flip=True,
zoom_range=0.2,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
fill_mode='nearest'
)
train_datagen.fit(x_train)

# 增加回调函数
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, verbose=1),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1)
]

# 创建模型
model = create_model()

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
history = model.fit_generator(train_datagen.flow(x_train, y_train, batch_size=64),
epochs=100,
verbose=1,
validation_data=(x_test, y_test),
callbacks=callbacks)

# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])