function correct_rate=Cross_validation(par,label) correct_rate=[]; indices=crossvalind('Kfold',par(1:size(par,1),size(par,2)),10); cp = classperf(label); for i = 1:10 label_test=[]; test = (indices == i); train = ~test; [label_test,~, ~] = fknn(par(train,:), label(train),par(test,:), label_test, 1, 0, true); classperf(cp,label_test,test); correct_rate=[correct_rate,cp.CorrectRate]; end end

from keras import applications from keras.preprocessing.image import ImageDataGenerator from keras import optimizers from keras.models import Sequential, Model from keras.layers import Dropout, Flatten, Dense img_width, img_height = 256, 256 batch_size = 16 epochs = 50 train_data_dir = 'C:/Users/Z-/Desktop/kaggle/train' validation_data_dir = 'C:/Users/Z-/Desktop/kaggle/test1' OUT_CATAGORIES = 1 nb_train_samples = 2000 nb_validation_samples = 100 base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3)) base_model.summary() for layer in base_model.layers[:15]: layer.trainable = False top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) top_model.add(Dense(256, activation='relu')) top_model.add(Dropout(0.5)) top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid')) model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) model.compile(loss='binary_crossentropy', optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9), metrics=['accuracy']) train_datagen = ImageDataGenerator(rescale=1. / 255, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary', shuffle=False ) model.fit_generator( train_generator, steps_per_epoch=nb_train_samples / batch_size, epochs=epochs, validation_data=validation_generator, validation_steps=nb_validation_samples / batch_size, verbose=2, workers=12 ) score = model.evaluate_generator(validation_generator, nb_validation_samples / batch_size) scores = model.predict_generator(validation_generator, nb_validation_samples / batch_size)看看这段代码有什么错误

top_model.add(Flatten(input_shape=base_model.output_shape[1:])) top_model.add(Dense(256, activation='relu')) top_model.add(Dropout(0.5)) top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid')) ...

#5折交叉验证 k = 5 mun_validation_samples = len(x_Train_normaliza) // k #np.random.shuffle(x_Train_normaliza) # validation_score = [] sum=0 import random for fold in range(k): validation_data = x_Train_normaliza[mun_validation_samplesfold:mun_validation_samples(fold+1)] validation_data_label=y_Trainonehot[mun_validation_samplesfold:mun_validation_samples(fold+1)] a=x_Train_normaliza[:mun_validation_samples * fold] b=x_Train_normaliza[mun_validation_samples * (fold+1):] training_data=np.append(a,b,axis=0) c=y_Trainonehot[:mun_validation_samplesfold] d= y_Trainonehot[mun_validation_samples(fold+1):] training_label=np.append(c,d,axis=0) #training_label=y_Trainonehot[:mun_validation_samplesfold] + y_Trainonehot[mun_validation_samples(fold+1):] #打散数据 index = [i for i in range(len(training_data))] random.shuffle(index) data = training_data[index] label = training_label[index] #开始训练 train_history=model.fit(x=data, #使用model.fit进行训练，训练过程存储在train_history变量里 y=label, epochs=2, batch_size=200,#每次处理200张 verbose=2)#显示训练过程 #model.train(training_data) validation_score = model.evaluate(validation_data,validation_data_label) validation_score_accuracy=validation_score[1] sum=sum+validation_score_accuracy print('validation_score=',validation_score_accuracy) validation_score_average=sum/k print('validation_score_average',validation_score_average)为什么np和model会报错

1. np 报错：如果是 numpy 库报错，有可能是你没有正确导入 numpy 库或者没有将其缩写为 np。你可以在代码开头加上 import numpy as np 来解决这个问题。 2. model 报错：如果是模型 model 报错，有...

将这段代码改为输出的AUC、f1_score、Accuracy是可重复的：# 定义模型参数 input_dim = X_train.shape[1] epochs = 100 batch_size = 32 learning_rate = 0.001 dropout_rate = 0.1 # 定义模型结构 def create_model(): model = Sequential() model.add(Dense(64, input_dim=input_dim, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(32, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(1, activation='sigmoid')) optimizer = Adam(learning_rate=learning_rate) model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model # 5折交叉验证 kf = KFold(n_splits=5, shuffle=True, random_state=42) cv_scores = [] for train_index, test_index in kf.split(X_train): # 划分训练集和验证集 X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index] y_train_fold, y_val_fold = y_train_forced_turnover_nolimited.iloc[train_index], y_train_forced_turnover_nolimited.iloc[test_index] # 创建模型 model = create_model() # 定义早停策略 #early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1) # 训练模型 model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size,verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 auc = roc_auc_score(y_val_fold, y_pred) cv_scores.append(auc) # 输出交叉验证结果 print('CV AUC:', np.mean(cv_scores)) # 在全量数据上重新训练模型 model = create_model() model.fit(X_train, y_train_forced_turnover_nolimited, epochs=epochs, batch_size=batch_size, verbose=1) #测试集结果 test_pred = model.predict(X_test) test_auc = roc_auc_score(y_test_forced_turnover_nolimited, test_pred) test_f1_score = f1_score(y_test_forced_turnover_nolimited, np.round(test_pred)) test_accuracy = accuracy_score(y_test_forced_turnover_nolimited, np.round(test_pred)) print('Test AUC:', test_auc) print('Test F1 Score:', test_f1_score) print('Test Accuracy:', test_accuracy) #训练集结果 train_pred = model.predict(X_train) train_auc = roc_auc_score(y_train_forced_turnover_nolimited, train_pred) train_f1_score = f1_score(y_train_forced_turnover_nolimited, np.round(train_pred)) train_accuracy = accuracy_score(y_train_forced_turnover_nolimited, np.round(train_pred)) print('Train AUC:', train_auc) print('Train F1 Score:', train_f1_score) print('Train Accuracy:', train_accuracy)

model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size, verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 ...

修改代码，使得输出结果是可重复的：# 定义模型参数 input_dim = X_train.shape[1] epochs = 100 batch_size = 32 learning_rate = 0.01 dropout_rate = 0.7 # 定义模型结构 def create_model(): model = Sequential() model.add(Dense(64, input_dim=input_dim, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(32, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(1, activation='sigmoid')) optimizer = Adam(learning_rate=learning_rate) model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model # 5折交叉验证 kf = KFold(n_splits=5, shuffle=True, random_state=42) cv_scores = [] for train_index, test_index in kf.split(X_train): # 划分训练集和验证集 X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index] y_train_fold, y_val_fold = y_train_forced_turnover_nolimited.iloc[train_index], y_train_forced_turnover_nolimited.iloc[test_index] # 创建模型 model = create_model() # 定义早停策略 #early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1) # 训练模型 model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size,verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 auc = roc_auc_score(y_val_fold, y_pred) cv_scores.append(auc) # 输出交叉验证结果 print('CV AUC:', np.mean(cv_scores)) # 在全量数据上重新训练模型 model = create_model() model.fit(X_train, y_train_forced_turnover_nolimited, epochs=epochs, batch_size=batch_size, verbose=1) #测试集结果 test_pred = model.predict(X_test) test_auc = roc_auc_score(y_test_forced_turnover_nolimited, test_pred) test_f1_score = f1_score(y_test_forced_turnover_nolimited, np.round(test_pred)) test_accuracy = accuracy_score(y_test_forced_turnover_nolimited, np.round(test_pred)) print('Test AUC:', test_auc) print('Test F1 Score:', test_f1_score) print('Test Accuracy:', test_accuracy) #训练集结果 train_pred = model.predict(X_train) train_auc = roc_auc_score(y_train_forced_turnover_nolimited, train_pred) train_f1_score = f1_score(y_train_forced_turnover_nolimited, np.round(train_pred)) train_accuracy = accuracy_score(y_train_forced_turnover_nolimited, np.round(train_pred)) print('Train AUC:', train_auc) print('Train F1 Score:', train_f1_score) print('Train Accuracy:', train_accuracy)

model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size,verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 auc =...

def PrepareDataset(speed_matrix, BATCH_SIZE = 40, seq_len = 10, pred_len = 1, train_propotion = 0.7, valid_propotion = 0.2):

Function to prepare dataset for training and validation Args: speed_matrix: numpy array of shape (num_days, num_timesteps, num_nodes) BATCH_SIZE: batch size for training (default = 40) seq_len: ...

X_train,X_test,y_train, y_test= cross validation.train_test_split(X_trainy_train,test_size=0.2,random_state=1)

这段代码使用了交叉验证中的train_test_split函数，将数据集X_train和y_train分为训练集和测试集，其中测试集占总数据集的20%，随机种子为1。具体来说，X_train是训练数据集的特征，y_train是训练数据集的标签，X_...

修改这段代码，使得输出训练集结果是可重复的：# 定义模型参数 input_dim = X_train.shape[1] epochs = 100 batch_size = 32 learning_rate = 0.001 dropout_rate = 0.1 # 定义模型结构 def create_model(): model = Sequential() model.add(Dense(64, input_dim=input_dim, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(32, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(1, activation='sigmoid')) optimizer = Adam(learning_rate=learning_rate) model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model # 5折交叉验证 kf = KFold(n_splits=5, shuffle=True, random_state=42) cv_scores = [] for train_index, test_index in kf.split(X_train): # 划分训练集和验证集 X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index] y_train_fold, y_val_fold = y_train_forced_turnover_nolimited.iloc[train_index], y_train_forced_turnover_nolimited.iloc[test_index] # 创建模型 model = create_model() # 定义早停策略 #early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1) # 训练模型 model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size,verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 auc = roc_auc_score(y_val_fold, y_pred) cv_scores.append(auc) # 输出交叉验证结果 print('CV AUC:', np.mean(cv_scores)) # 在全量数据上重新训练模型 model = create_model() model.fit(X_train, y_train_forced_turnover_nolimited, epochs=epochs, batch_size=batch_size, verbose=1) #测试集结果 test_pred = model.predict(X_test) test_auc = roc_auc_score(y_test_forced_turnover_nolimited, test_pred) test_f1_score = f1_score(y_test_forced_turnover_nolimited, np.round(test_pred)) test_accuracy = accuracy_score(y_test_forced_turnover_nolimited, np.round(test_pred)) print('Test AUC:', test_auc) print('Test F1 Score:', test_f1_score) print('Test Accuracy:', test_accuracy) #训练集结果 train_pred = model.predict(X_train) train_auc = roc_auc_score(y_train_forced_turnover_nolimited, train_pred) train_f1_score = f1_score(y_train_forced_turnover_nolimited, np.round(train_pred)) train_accuracy = accuracy_score(y_train_forced_turnover_nolimited, np.round(train_pred)) print('Train AUC:', train_auc) print('Train F1 Score:', train_f1_score) print('Train Accuracy:', train_accuracy)

model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size,verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 auc =...

validation_freq = 270000 // 32

如果你的训练集大小为 270000，batch size 为 32，那么 validation_freq 可以通过以下公式计算： validation_freq = N // (B * k) 其中，$N$ 表示训练集大小，$B$ 表示 batch size，$k$ 是一个常数，...

validation_freq = 100 // 32

假设你的训练集大小为 $N$，batch size 为 $B$，那么 validation_freq 参数可以通过以下公式计算： validation_freq = N // (B * k) 其中，$k$ 是一个常数，它的取值通常在 1-5 之间。这个公式的含义是...

解释一下self.learning_rate = tf.keras.Input(tf.float32, shape=[], name='learning_rate')

loss='categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val), callbacks=[tf.keras.callbacks.EarlyStopping(patience=2)], verbose=2,...

validation_data=(test_X, test_y)

在给定的代码中，validation_data=(test_X, test_y) 表示将测试集的特征数据 test_X 和对应的标签数据 test_y 作为验证数据。这样设置之后，在每个训练周期结束时，模型将会使用验证数据来评估当前模型的性能，并...

激活函数选项：activation : {'identity', 'logistic', 'tanh', 'relu'}, default='relu' model = Pipeline([('Scaler',StandardScaler()),('MLP',MLPRegressor(hidden_layer_sizes=500, activation='relu',solver='adam', batch_size='auto', learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=1000, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10))]) clf = model.fit(X_train,Y_train)这里面有哪些参数可以修改

以下是可以修改的参数： ...- beta_1：Adam 优化算法的参数。 - beta_2：Adam 优化算法的参数。 - epsilon：Adam 优化算法的参数。 - n_iter_no_change：在没有改善验证得分的情况下，连续迭代次数的最大数量。

解释cnn_model_history = cnn_model.fit_generator( train_generator, steps_per_epoch=nb_train_samples// batch_size , epochs=epochs, validation_data=test_generator, validation_steps=nb_validation_samples// batch_size, workers = 4)

- validation_steps=nb_validation_samples // batch_size：这是每个epoch中的验证步数。nb_validation_samples是验证集的样本数量，batch_size是每个批次中的样本数量。因此，validation_steps是将验证集分成...

history = model.fit(train_generator, epochs=10, steps_per_epoch=100, validation_data=val_generator, validation_steps=50)

这段代码是使用Keras中的模型训练函数fit()来训练模型。具体来说，它使用了一个训练数据生成器train_generator和一个验证数据生成器val_generator来训练模型。训练过程会进行10个epoch（即训练整个训练集的次数），...

correct_rate=Cross_validation(par,label)

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def PrepareDataset(speed_matrix, BATCH_SIZE = 40, seq_len = 10, pred_len = 1, train_propotion = 0.7, valid_propotion = 0.2):

X_train,X_test,y_train, y_test= cross validation.train_test_split(X_trainy_train,test_size=0.2,random_state=1)

validation_freq = 270000 // 32

validation_freq = 100 // 32

解释一下self.learning_rate = tf.keras.Input(tf.float32, shape=[], name='learning_rate')

validation_data=(test_X, test_y)

解释cnn_model_history = cnn_model.fit_generator( train_generator, steps_per_epoch=nb_train_samples// batch_size , epochs=epochs, validation_data=test_generator, validation_steps=nb_validation_samples// batch_size, workers = 4)

history = model.fit(train_generator, epochs=10, steps_per_epoch=100, validation_data=val_generator, validation_steps=50)

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