from sklearn import metrics print('Mean Absolute Error:', metrics.mean_absolute_error(Y_validation,y_pred)) print('Mean Squared Error:', metrics.mean_squared_error(Y_validation,y_pred)) print('Root Mean Squared Error:',np.sqrt(metrics.mean_squared_error(Y_validation,y_pred))) print('R2 =',metrics.r2_score(Y_validation,y_pred))请优化

时间: 2024-04-12 12:30:02 浏览: 173
当处理大型数据集时,计算一些评估指标可能会导致内存不足错误。为了优化这段代码,你可以采取以下措施: 1. 分批计算:将验证集分成小批次进行评估,而不是一次性计算所有指标。这样可以减少内存的使用量。示例代码如下: ```python batch_size = 1000 num_batches = len(Y_validation) // batch_size mae = 0 mse = 0 rmse = 0 r2 = 0 for i in range(num_batches): start_idx = i * batch_size end_idx = (i + 1) * batch_size batch_Y_validation = Y_validation[start_idx:end_idx] batch_y_pred = y_pred[start_idx:end_idx] mae += metrics.mean_absolute_error(batch_Y_validation, batch_y_pred) mse += metrics.mean_squared_error(batch_Y_validation, batch_y_pred) r2 += metrics.r2_score(batch_Y_validation, batch_y_pred) mae /= num_batches mse /= num_batches rmse = np.sqrt(mse) r2 /= num_batches print('Mean Absolute Error:', mae) print('Mean Squared Error:', mse) print('Root Mean Squared Error:', rmse) print('R2 =', r2) ``` 2. 释放内存:在每次计算指标后,手动释放不再需要的变量占用的内存。示例代码如下: ```python mae = metrics.mean_absolute_error(Y_validation, y_pred) print('Mean Absolute Error:', mae) del mae mse = metrics.mean_squared_error(Y_validation, y_pred) print('Mean Squared Error:', mse) del mse rmse = np.sqrt(metrics.mean_squared_error(Y_validation, y_pred)) print('Root Mean Squared Error:', rmse) del rmse r2 = metrics.r2_score(Y_validation, y_pred) print('R2 =', r2) del r2 ``` 通过这些优化措施,你应该能够减少内存的使用,从而避免出现内存不足错误。
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import numpy as np import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv1D, Dense, Dropout, Flatten, TimeDistributed from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_absolute_error # 生成虚拟交通流量数据 def generate_virtual_taxi_data(num_nodes=50, time_steps=288, features=2): np.random.seed(42) # 确保可复现性 base_flow = np.linspace(100, 500, num_nodes) # 基础流量(辆/小时) inflow = np.random.normal(loc=base_flow*0.8, scale=base_flow*0.1, size=(time_steps, num_nodes)) outflow = np.random.normal(loc=base_flow*0.7, scale=base_flow*0.1, size=(time_steps, num_nodes)) # 添加早晚高峰特征 peak_mask = np.zeros((time_steps, num_nodes), dtype=bool) peak_mask[np.logical_or(time_steps*0.25<np.arange(time_steps), time_steps*0.75>np.arange(time_steps))] = True inflow[peak_mask] *= 1.5 outflow[peak_mask] *= 1.3 # 构建完整数据集 traffic_data = np.stack([inflow, outflow], axis=-1) return traffic_data # 创建序列数据 def create_sequences(data, seq_length): X, y = [], [] for i in range(len(data) - seq_length): X.append(data[i:i+seq_length]) y.append(data[i+seq_length]) return np.array(X), np.array(y) # 构建多任务模型 def build_multi_task_model(input_shape): inputs = Input(shape=input_shape) x = tf.keras.layers.TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu', padding='same'))(inputs) x = tf.keras.layers.TimeDistributed(Flatten())(x) x = tf.keras.layers.LSTM(128, return_sequences=True)(x) # 分支预测流入和流出 inflow_output = Dense(1, name='inflow')(x) outflow_output = Dense(1, name='outflow')(x) model = Model(inputs, [inflow_output, outflow_output]) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss={'inflow': 'mse', 'outflow': 'mse'}, metrics={'inflow': 'mae', 'outflow': 'mae'}) return model # 主函数 def main(): num_nodes = 50 time_steps = 288 features = 2 seq_length = 12 # 生成虚拟数据 traffic_data = generate_virtual_taxi_data(num_nodes, time_steps, features) print(f"Virtual Traffic Data Shape: {traffic_data.shape}") # 应输出 (288,50,2) # 数据预处理 scaler = StandardScaler() traffic_data_scaled = scaler.fit_transform(traffic_data.reshape(-1, features)).reshape(traffic_data.shape) # 创建序列数据 X, y = create_sequences(traffic_data_scaled, seq_length) # 划分训练集/测试集 split = int(0.8 * X.shape[0]) X_train, X_test = X[:split], X[split:] y_train, y_test = y[:split], y[split:] # 模型训练 model = build_multi_task_model(input_shape=(seq_length, num_nodes, features)) history = model.fit(X_train, {'inflow': y_train[..., 0].reshape(-1, 1), 'outflow': y_train[..., 1].reshape(-1, 1)}, epochs=50, batch_size=32, validation_split=0.2) # 结果评估 y_pred_inflow, y_pred_outflow = model.predict(X_test) y_pred_inflow = scaler.inverse_transform(y_pred_inflow).flatten() y_pred_outflow = scaler.inverse_transform(y_pred_outflow).flatten() y_test_inflow = scaler.inverse_transform(y_test[..., 0]).flatten() y_test_outflow = scaler.inverse_transform(y_test[..., 1]).flatten() mae_inflow = mean_absolute_error(y_test_inflow, y_pred_inflow) mae_outflow = mean_absolute_error(y_test_outflow, y_pred_outflow) print(f"MAE Inflow: {mae_inflow:.2f}辆/小时") print(f"MAE Outflow: {mae_outflow:.2f}辆/小时") if __name__ == '__main__': main()帮我修改这段代码

import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix, classification_report from sklearn.metrics import roc_auc_score from sklearn.utils.class_weight import compute_class_weight # 读取数据 data = pd.read_csv('database.csv') # 数据预处理 X = data.iloc[:, :-1].values y = data.iloc[:, -1].values scaler = StandardScaler() X = scaler.fit_transform(X) # 特征选择 pca = PCA(n_components=10) X = pca.fit_transform(X) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train) # 构建CNN模型 model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(10, 1))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # 训练模型 X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1)) X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1)) model.fit(X_train, y_train,class_weight=class_weights,epochs=100, batch_size=64, validation_data=(X_test, y_test)) # 预测结果 y_pred = model.predict(X_test) #检验值 accuracy = accuracy_score(y_test, y_pred) auc = roc_auc_score(y_test, y_pred) print(auc) print("Accuracy:", accuracy) print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred)) print('Classification Report:\n', classification_report(y_test, y_pred))

果然刚才那个代码““best_val_acc = 0.0for epoch in range(training_epochs): model.train() train_loss = 0.0 train_mae = 0.0 with tqdm(total=len(train_loader), desc=f’Epoch {epoch + 1}/{training_epochs}', unit=‘batch’, position=0, leave=True) as pbar: for X_batch, Y_batch in train_loader: X_batch, Y_batch = X_batch.to(device), Y_batch.to(device) optimizer.zero_grad() output = model(X_batch) loss = criterion(output, Y_batch) #训练过程 loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() output_np = output.detach().cpu().numpy() Y_batch_np = Y_batch.detach().cpu().numpy() output_np_flat = output_np.reshape(output_np.shape[0], -1) Y_batch_np_flat = Y_batch_np.reshape(Y_batch_np.shape[0], -1) mae = mean_absolute_error(Y_batch_np_flat, output_np_flat) train_loss += loss.item() train_mae += mae.item() pbar.set_postfix({'loss': f'{loss.item():.4f}', 'mae': f'{mae.item():.4f}'}) pbar.update(1) train_loss /= len(train_loader) train_mae /= len(train_loader) history['train_loss'].append(train_loss) history['train_mae'].append(train_mae) model.eval() val_loss = 0.0 val_mae = 0.0 val_acc = 0.0 with torch.no_grad(): for X_batch, Y_batch in valid_loader: X_batch, Y_batch = X_batch.to(device), Y_batch.to(device) output = model(X_batch) loss = criterion(output, Y_batch) output_np = output.detach().cpu().numpy() Y_batch_np = Y_batch.detach().cpu().numpy() output_np_flat = output_np.reshape(output_np.shape[0], -1) Y_batch_np_flat = Y_batch_np.reshape(Y_batch_np.shape[0], -1) mae = mean_absolute_error(Y_batch_np_flat, output_np_flat) val_loss += loss.item() val_mae += mae.item() acc = precision_score(Y_batch_np_flat, output_np_flat) val_acc += acc.item() val_loss /= len(valid_loader) val_mae /= len(valid_loader) val_acc /= len(valid_loader) history['val_loss'].append(val_loss) history['val_mae'].append(val_mae) print( f'Epoch {epoch + 1}/{training_epochs}, Train Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}, Validation MAE: {val_mae:.4f}') if val_acc > best_val_acc: best_val_acc = val_acc torch.save(model.state_dict(), file_weights) print(f'Best model saved at epoch {epoch + 1} with validation loss {val_loss:.4f}')””,出现这个问题"Traceback (most recent call last): File "/home/featurize/work/trainkanmuacc.py", line 205, in <module> acc = precision_score(Y_batch_np_flat, output_np_flat) File "/environment/miniconda3/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 214, in wrapper return func(*args, **kwargs) File "/environment/miniconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py", line 2131, in precision_score p, _, _, _ = precision_recall_fscore_support( File "/environment/miniconda3/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 187, in wrapper return func(*args, **kwargs) File "/environment/miniconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py", line 1724, in precision_recall_fscore_support labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label) File "/environment/miniconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py", line 1501, in _check_set_wise_labels y_type, y_true, y_pred = _check_targets(y_true, y_pred) File "/environment/miniconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py", line 104, in _check_targets raise ValueError("{0} is not supported".format(y_type)) ValueError: continuous-multioutput is not supported"

import numpy as np import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Activation, Dropout, Flatten from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.utils import np_utils from keras.datasets import mnist from keras import backend as K from keras.optimizers import Adam import skfuzzy as fuzz import pandas as pd from sklearn.model_selection import train_test_split # 绘制损失曲线 import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score data = pd.read_excel(r"D:\pythonProject60\filtered_data1.xlsx") # 读取数据文件 # Split data into input and output variables X = data.iloc[:, :-1].values y = data.iloc[:, -1].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 导入MNIST数据集 # 数据预处理 y_train = np_utils.to_categorical(y_train, 3) y_test = np_utils.to_categorical(y_test, 3) # 创建DNFN模型 model = Sequential() model.add(Dense(64, input_shape=(11,), activation='relu')) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(3, activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) # 训练模型 history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=128) # 使用DNFN模型进行预测 y_pred = model.predict(X_test) y_pred= np.argmax(y_pred, axis=1) print(y_pred) # 计算模糊分类 fuzzy_pred = [] for i in range(len(y_pred)): fuzzy_class = np.zeros((3,)) fuzzy_class[y_pred[i]] = 1.0 fuzzy_pred.append(fuzzy_class) fuzzy_pred = np.array(fuzzy_pred) print(fuzzy_pred)获得其运行时间

import jieba import pynlpir import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split # 读取文本文件 with open('1.txt', 'r', encoding='utf-8') as f: text = f.read() # 对文本进行分词 word_list = list(jieba.cut(text, cut_all=False)) # 打开pynlpir分词器 pynlpir.open() # 对分词后的词语进行词性标注 pos_list = pynlpir.segment(text, pos_tagging=True) # 将词汇表映射成整数编号 vocab = set(word_list) vocab_size = len(vocab) word_to_int = {word: i for i, word in enumerate(vocab)} int_to_word = {i: word for i, word in enumerate(vocab)} # 将词语和词性标记映射成整数编号 pos_tags = set(pos for word, pos in pos_list) num_tags = len(pos_tags) tag_to_int = {tag: i for i, tag in enumerate(pos_tags)} int_to_tag = {i: tag for i, tag in enumerate(pos_tags)} # 将文本和标签转换成整数序列 X = np.array([word_to_int[word] for word in word_list]) y = np.array([tag_to_int[pos] for word, pos in pos_list]) # 将数据划分成训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 定义模型参数 embedding_size = 128 rnn_size = 256 batch_size = 128 epochs = 10 # 定义RNN模型 model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size, embedding_size), tf.keras.layers.SimpleRNN(rnn_size), tf.keras.layers.Dense(num_tags, activation='softmax') ]) # 编译模型 model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 训练模型 model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test)) # 对测试集进行预测 y_pred = model.predict(X_test) y_pred = np.argmax(y_pred, axis=1) # 计算模型准确率 accuracy = np.mean(y_pred == y_test) print('Accuracy: {:.2f}%'.format(accuracy * 100)) # 将模型保存到文件中 model.save('model.h5')出现下述问题:ValueError: Found input variables with inconsistent numbers of samples:

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1. Use the credictcard-reduced.csv dataset ([Data description](https://www.kaggle.com/mlg-ulb/creditcardfraud)) and build Five classification models. Please plot confusion matrix, and evaluate your model performance using accuracy, precision, recall, F-score (70 points). A list of classification models can be found [here](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) 我现在要完成以上作业,已输入下面代码from sklearn.model_selection import train_test_split # this function provides a single "Hold-Out" Validation. import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score #similar to MAE, we use accuracy_score evaluation metric. import pandas as pd import numpy as np data = pd.read_csv('Credit_approval.csv',header='infer') data.head(5)Y = data['Approved'] #this is our prediction target X = data.drop(['Approved'],axis=1) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.4, random_state=1) rom sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier(max_depth=2) # number estimators is the key parameter clf = clf.fit(X_train, Y_train) Y_predTrain = clf.predict(X_train) Y_predTest = clf.predict(X_test) print('Training accuracy is %f' % accuracy_score(Y_train, Y_predTrain)) print('Validation accuracy is %f' %accuracy_score(Y_test, Y_predTest))from sklearn.ensemble import RandomForestClassifier np.random.seed(1) #because the model has random sampling, a random seed can ensure repeated results. clf = RandomForestClassifier(max_depth=2,n_estimators=500) clf = clf.fit(X_train, Y_train) Y_predTrain = clf.predict(X_train) Y_predTest = clf.predict(X_test) print('Training accuracy is %f' % accuracy_score(Y_train, Y_predTrain)) print('Validation accuracy is %f' %accuracy_score(Y_test, Y_predTest)) 接下来怎么办

import numpy as np import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Activation, Dropout, Flatten from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.utils import np_utils from keras.datasets import mnist from keras import backend as K from keras.optimizers import Adam import skfuzzy as fuzz import pandas as pd from sklearn.model_selection import train_test_split # 绘制损失曲线 import matplotlib.pyplot as plt import time from sklearn.metrics import accuracy_score data = pd.read_excel(r"D:\pythonProject60\filtered_data1.xlsx") # 读取数据文件 # Split data into input and output variables X = data.iloc[:, :-1].values y = data.iloc[:, -1].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 导入MNIST数据集 # 数据预处理 y_train = np_utils.to_categorical(y_train, 3) y_test = np_utils.to_categorical(y_test, 3) # 创建DNFN模型 start_time=time.time() model = Sequential() model.add(Dense(64, input_shape=(11,), activation='relu')) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(3, activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) # 训练模型 history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=128) # 使用DNFN模型进行预测 y_pred = model.predict(X_test) y_pred= np.argmax(y_pred, axis=1) print(y_pred) # 计算模糊分类 fuzzy_pred = [] for i in range(len(y_pred)): fuzzy_class = np.zeros((3,)) fuzzy_class[y_pred[i]] = 1.0 fuzzy_pred.append(fuzzy_class) fuzzy_pred = np.array(fuzzy_pred) end_time = time.time() print("Total time taken: ", end_time - start_time, "seconds")获得结果并分析

详细分析下述代码:import jieba import pynlpir import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split # 读取文本文件with open('1.txt', 'r', encoding='utf-8') as f: text = f.read()# 对文本进行分词word_list = list(jieba.cut(text, cut_all=False))# 打开pynlpir分词器pynlpir.open()# 对分词后的词语进行词性标注pos_list = pynlpir.segment(text, pos_tagging=True)# 将词汇表映射成整数编号vocab = set(word_list)vocab_size = len(vocab)word_to_int = {word: i for i, word in enumerate(vocab)}int_to_word = {i: word for i, word in enumerate(vocab)}# 将词语和词性标记映射成整数编号pos_tags = set(pos for word, pos in pos_list)num_tags = len(pos_tags)tag_to_int = {tag: i for i, tag in enumerate(pos_tags)}int_to_tag = {i: tag for i, tag in enumerate(pos_tags)}# 将文本和标签转换成整数序列X = np.array([word_to_int[word] for word in word_list])y = np.array([tag_to_int[pos] for word, pos in pos_list])# 将数据划分成训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 定义模型参数embedding_size = 128rnn_size = 256batch_size = 128epochs = 10# 定义RNN模型model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size, embedding_size), tf.keras.layers.SimpleRNN(rnn_size), tf.keras.layers.Dense(num_tags, activation='softmax')])# 编译模型model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])# 训练模型model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))# 对测试集进行预测y_pred = model.predict(X_test)y_pred = np.argmax(y_pred, axis=1)# 计算模型准确率accuracy = np.mean(y_pred == y_test)print('Accuracy: {:.2f}%'.format(accuracy * 100))# 将模型保存到文件中model.save('model.h5')

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)

# -*- coding: utf-8 -*- """ @author: zhang'xin'ge """ # 导入必要的库和数据 import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from keras.models import Sequential from keras.layers import LSTM, Dense data = pd.read_csv('D:/MATLAB/data_test/0713_电子版更新.csv') # 将数据集拆分为训练集和测试集,并进行特征缩放: X = data.drop(['体质类型'], axis=1).values y = data['体质类型'].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) #使用LSTM算法训练一个分类模型 model = Sequential() model.add(LSTM(64, input_shape=(X_train_scaled.shape[1], 1))) model.add(Dense(32, activation='relu')) model.add(Dense(9, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 将训练集和测试集转换为LSTM模型需要的输入格式: X_train_lstm = X_train_scaled.reshape((X_train_scaled.shape[0], X_train_scaled.shape[1], 1)) X_test_lstm = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1)) # 使用训练集对模型进行训练: model.fit(X_train_lstm, y_train, epochs=50, batch_size=32, validation_data=(X_test_lstm, y_test)) # 使用训练好的模型对测试集进行预测,并计算准确率: y_pred = model.predict_classes(X_test_lstm) accuracy = (y_pred == y_test).mean() print('Accuracy:', accuracy)

# -*- coding: utf-8 -*- """ @author: zhang'xin'ge """ # 导入必要的库和数据 import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from keras.models import Sequential from keras.layers import LSTM, Dense data = pd.read_csv('D:/MATLAB/data_test/0713_电子版更新.csv') # 将数据集拆分为训练集和测试集,并进行特征缩放: X = data.drop(['体质类型'], axis=1).values y = data['体质类型'].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) #使用LSTM算法训练一个分类模型 model = Sequential() model.add(LSTM(64, input_shape=(X_train_scaled.shape[1], 1))) model.add(Dense(32, activation='relu')) model.add(Dense(9, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 将训练集和测试集转换为LSTM模型需要的输入格式: X_train_lstm = X_train_scaled.reshape((X_train_scaled.shape[0], X_train_scaled.shape[1], 1)) X_test_lstm = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1)) # 使用训练集对模型进行训练: model.fit(X_train_lstm, y_train, epochs=50, batch_size=32, validation_data=(X_test_lstm, y_test)) # 使用训练好的模型对测试集进行预测,并计算准确率: y_pred = model.predict_classes(X_test_lstm) accuracy = (y_pred == y_test).mean() print('Accuracy:', accuracy) 出现以下报错,如何解决 File "D:\Anaconda\lib\site-packages\typing_extensions.py", line 137, in _collect_type_vars 'TypedDict', NameError: name '_should_collect_from_parameters' is not defined

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