def plot_image(i, predictions_array, true_label, img): predictions_array, true_label, img = predictions_array, true_label[i], img[i] plt.grid(False) plt.xticks([]) plt.yticks([]) plt.imshow(img, cmap=plt.cm.binary) predicted_label = np.argmax(predictions_array) if predicted_label == true_label: color = 'blue' else: color = 'red' plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label], 100 * np.max(predictions_array), class_names[true_label]), color=color) def plot_value_array(i, predictions_array, true_label): predictions_array, true_label = predictions_array, true_label[i] plt.grid(False) plt.xticks(range(10)) plt.yticks([]) thisplot = plt.bar(range(10), predictions_array, color="#777777") plt.ylim([0, 1]) predicted_label = np.argmax(predictions_array) thisplot[predicted_label].set_color('red') thisplot[true_label].set_color('blue') print("验证预测结果:") i = 12 plt.figure(figsize=(6, 3)) plt.subplot(1, 2, 1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(1, 2, 2) plot_value_array(i, predictions[i], test_labels) plt.show() num_rows = 5 num_cols = 3 num_images = num_rows * num_cols plt.figure(figsize=(2 * 2 * num_cols, 2 * num_rows)) for i in range(num_images): plt.subplot(num_rows, 2 * num_cols, 2 * i + 1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(num_rows, 2 * num_cols, 2 * i + 2) plot_value_array(i, predictions[i], test_labels) plt.tight_layout() plt.show() # 使用训练好的模型对单个图像进行预测 img = test_images[1] print(img.shape) # tf.keras 模型经过了优化，可同时对一个批或一组样本进行预测 img = (np.expand_dims(img, 0)) print(img.shape) # 增加相应标签 predictions_single = probability_model.predict(img) print(predictions_single) plot_value_array(1, predictions_single[0], test_labels) _ = plt.xticks(range(10), class_names, rotation=45)

def plot_confuse(model, x_val, y_val): predictions = model.predict_classes(x_val) truelabel = y_val.argmax(axis=-1) # 将one-hot转化为label conf_mat = confusion_matrix(y_true=truelabel, y_pred=predictions) plt.figure() plot_confusion_matrix(conf_mat, range(np.max(truelabel) + 1))

conf_mat = confusion_matrix(y_true=truelabel, y_pred=predicted_classes) plt.figure() plot_confusion_matrix(conf_mat, classes=range(np.max(truelabel) + 1)) 这个代码将使用model的predict()方法获取...

为以下每句代码做注释：def VGG19_model(self, learning_rate=0.005, decay=1e-6, momentum=0.9, nb_classes=2, img_rows=197, img_cols=197, RGB=True, is_plot_model=False): color = 3 if RGB else 1 base_model = VGG19(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color), classes=nb_classes) # 冻结base_model所有层，这样就可以正确获得bottleneck特征 for layer in base_model.layers: layer.trainable = False x = base_model.output # 添加自己的全链接分类层 x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(nb_classes, activation='softmax')(x) # 训练模型 model = Model(inputs=base_model.input, outputs=predictions) sgd = SGD(learning_rate=learning_rate, decay=decay, momentum=momentum, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # 绘图 if is_plot_model: plot_model(model, to_file='vgg/vgg19_model.png', show_shapes=True) return model

def VGG19_model(self, learning_rate=0.005, decay=1e-6, momentum=0.9, nb_classes=2, img_rows=197, img_cols=197, RGB=True, is_plot_model=False): # 确定输入图片的通道数（RGB为3，否则为1） color = 3 if ...

为每句代码加注释：# ResNet模型 color = 3 if RGB else 1 base_model = ResNet50(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color), classes=nb_classes) # 冻结base_model所有层，这样就可以正确获得bottleneck特征 for layer in base_model.layers: layer.trainable = True x = base_model.output # 添加自己的全链接分类层 x = Flatten()(x) predictions = Dense(nb_classes, activation='softmax')(x) # 训练模型 model = Model(inputs=base_model.input, outputs=predictions) sgd = SGD(learning_rate=learning_rate, decay=decay, momentum=momentum, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # 绘制模型 if is_plot_model: plot_model(model, to_file='vgg/resnet50_model.png', show_shapes=True) return model

# 定义一个 ResNet 模型，color 根据 RGB 是否为 True 来确定为 3 或 1 color = 3 if RGB else 1 # 载入 ResNet50 模型的权重，并冻结所有层，只保留特征提取层 base_model = ResNet50(weights='imagenet', include...

def color_map(ax2, lamx, lamy, lamplot, model_BT, model_weights, Psi_model, cmaplist, terms, label): predictions = np.zeros([lamx.shape[0], terms]) cmap_r = list(reversed(cmaplist)) for i in range(len(model_weights)-1): model_plot = GetZeroList(model_weights) model_plot[i] = model_weights[i] model_plot[-1][i] = model_weights[-1][i] # print(model_plot) Psi_model.set_weights(model_plot) lower = np.sum(predictions,axis=1) if label == 'x': upper = lower + model_BT.predict([lamx, lamy])[0][:].flatten() predictions[:,i] = model_BT.predict([lamx, lamy])[0][:].flatten() else: upper = lower + model_BT.predict([lamx, lamy])[1][:].flatten() predictions[:,i] = model_BT.predict([lamx, lamy])[1][:].flatten() im = ax2.fill_between(lamplot[:], lower.flatten(), upper.flatten(), zorder=i+1, alpha=1.0, color=cmap_r[i])

lamplot是要绘制的区域，model_BT是一个模型对象，model_weights是一个包含模型权重的列表，Psi_model是一个神经网络对象，cmaplist是一个颜色映射列表，terms是预测值的数量，label是标签（'x' 或 'y...

# 使用训练好的模型对单个图像进行预测 img = test_images[1] print(img.shape) # tf.keras 模型经过了优化，可同时对一个批或一组样本进行预测 img = (np.expand_dims(img, 0)) print(img.shape) # 增加相应标签 predictions_single = probability_model.predict(img) print(predictions_single) plot_value_array(1, predictions_single[0], test_labels) _ = plt.xticks(range(10), class_names, rotation=45)

img = (np.expand_dims(img, 0))是将图片转化为模型所需的输入格式，predictions_single = probability_model.predict(img)是用训练好的模型对图片进行预测，输出的是每个类别的概率，最后调用plot_value_array函数...

data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据副本3.csv', index_col=0, parse_dates=True) data.index = pd.to_datetime(data.index, unit='s') data = data.dropna() # 绘制原始时间序列图 plt.plot(data) plt.title('Original Time Series') plt.show() # 对时间序列进行平稳化处理 predictions_diff = pd.Series(data.fittedvalues, copy=True) predictions_diff_cumsum = predictions_diff.cumsum() predictions = pd.Series(data['y'].iloc[0], index=data.index) predictions = predictions.add(predictions_diff_cumsum, fill_value=0) # 绘制平稳化后的时间序列图 plt.plot(predictions) plt.title('Stabilized Time Series') plt.show(),如何修改成功运行AttributeError: 'DataFrame' object has no attribute 'fittedvalues'

根据提示信息，这段代码出错的原因是 DataFrame 对象没有属性 'fittedvalues'。这可能是因为在对时间序列进行平稳化处理时，使用了某个模型对数据进行了拟合，但是没有将拟合后的结果保存在 DataFrame 对象中。...

import yfinance as yf def download_stock_data(ticker_symbol, start_date, end_date): stock_data = yf.download(ticker_symbol, start=start_date, end=end_date) return stock_data ticker = "AAPL" start_date = "2015-01-01" end_date = "2023-01-01" stock_data = download_stock_data(ticker, start_date, end_date) print(stock_data.head()) import pandas as pd def preprocess_data(data): data['MA_5'] = data['Close'].rolling(window=5).mean() data['MA_20'] = data['Close'].rolling(window=20).mean() data.dropna(inplace=True) # 去除含有NaN的行 features = ['Open', 'High', 'Low', 'Volume', 'MA_5', 'MA_20'] target = 'Close' X = data[features] y = data[target] return X, y X, y = preprocess_data(stock_data) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False) print(f"Training set size: {len(X_train)}") print(f"Testing set size: {len(X_test)}") from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Dropout def build_lstm_model(input_shape): model = Sequential([ LSTM(50, return_sequences=True, input_shape=input_shape), Dropout(0.2), LSTM(50, return_sequences=False), Dropout(0.2), Dense(25), Dense(1) ]) model.compile(optimizer='adam', loss='mse') return model input_shape = (X_train.shape[1], 1) model = build_lstm_model(input_shape) model.summary() history = model.fit( X_train.values.reshape(-1, X_train.shape[1], 1), y_train.values, epochs=50, batch_size=32, validation_data=(X_test.values.reshape(-1, X_test.shape[1], 1), y_test.values), verbose=1 ) predictions = model.predict(X_test.values.reshape(-1, X_test.shape[1], 1)) predicted_prices = predictions.flatten() # 可视化实际 vs 预测结果 import matplotlib.pyplot as plt plt.figure(figsize=(14,7)) plt.plot(y_test.index, y_test.values, label="Actual Price", color="blue") plt.plot(y_test.index, predicted_prices, label="Predicted Price", color="red") plt.title("Stock Price Prediction with LSTM Model") plt.xlabel("Date") plt.ylabel("Price ($)") plt.legend() plt.show()修改正确

return np.array(X_new), np.array(y_new) X_train_seq, y_train_seq = create_dataset(X_train, y_train, n_steps=n_steps) X_test_seq, y_test_seq = create_dataset(X_test, y_test, n_steps=n_steps) # 确保...

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima.model import ARIMAfrom sklearn.metrics import mean_squared_error# 读取csv文件data = pd.read_csv("data.csv", header=None)# 划分训练集和测试集train_size = int(len(data) * 0.7)train_data, test_data = data[:train_size], data[train_size:]# 将时间编号设置为索引train_data.set_index(0, inplace=True)test_data.set_index(0, inplace=True)# 转换为时间序列train_ts = train_data[1]test_ts = test_data[1]# 训练ARIMA模型model = ARIMA(train_ts, order=(1, 1, 1))model_fit = model.fit()# 预测测试集数据predictions = model_fit.forecast(steps=len(test_ts))[0]# 计算rmsermse = np.sqrt(mean_squared_error(test_ts, predictions))print('Test RMSE: %.3f' % rmse)# 绘制图像plt.figure(figsize=(10, 6))plt.plot(train_ts, label='Train')plt.plot(test_ts, label='Test')plt.plot(test_ts.index, predictions, label='Predictions')plt.xlabel('Time/h')plt.ylabel('kwh')plt.title('ARIMA Model Predictions')plt.legend()plt.show()以上代码运行报错如下ValueError: 0 is not in range，请修正代码

plt.plot(test_ts.index, predictions, label='Predictions') plt.xlabel('Time/h') plt.ylabel('kwh') plt.title('ARIMA Model Predictions') plt.legend() plt.show() 这样就可以解决报错了。

import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.arima.model import ARIMA from sklearn.metrics import mean_squared_error # 读取csv文件 data = pd.read_csv("77.csv", header=None) # 划分训练集和测试集 train_size = int(len(data) * 0.7) train_data, test_data = data[:train_size], data[train_size:] # 将时间编号设置为索引 train_data.set_index(0, inplace=True) test_data.set_index(0, inplace=True) # 删除原来的索引列 train_data.index.name = None test_data.index.name = None # 转换为时间序列 train_ts = train_data[1] test_ts = test_data[1] # 训练ARIMA模型 model = ARIMA(train_ts, order=(1, 1, 1)) model_fit = model.fit() # 预测测试集数据 predictions = model_fit.forecast(steps=len(test_ts))[0] # 计算rmse rmse = np.sqrt(mean_squared_error(test_ts, predictions)) print('Test RMSE: %.3f' % rmse) # 绘制图像 plt.figure(figsize=(10, 6)) plt.plot(train_ts, label='Train') plt.plot(test_ts, label='Test') plt.plot(test_ts.index, predictions, label='Predictions') plt.xlabel('Time(h)') plt.ylabel('kwh') plt.title('ARIMA Model Predictions') plt.legend() plt.show()以上代码报错如下，请解释并给出修改后的代码KeyError: 0

plt.plot(test_ts.index, predictions, label='Predictions') 修改为： python plt.plot(test_data.index, predictions, label='Predictions') 这样就可以正确绘制出预测结果的图像了。

import numpy as np class Linearclass: def init(self,data,labels): self.data = data self.labels = labels num_features = self.data.shape[1]#有几个特征就是几列 self.theta = np.zeros((num_features,1))#这个初始化做了一个矩阵,列向量 def train(self,alpha,num_iterations = 500):#训练函数，学习率，迭代次数 """实际训练模块，执行梯度下降""" cost_history = self.gradient_descent(alpha,num_iterations) return self.theta,cost_history def gradient_descent(self,alpha,num_iterations):#梯度下降，学习率，迭代次数 """实际迭代模块，迭代num_iterations次""" cost_history = []#定义一个损失值列表 for _ in range(num_iterations):#迭代次数 self.gradient_step(alpha)#每次都走一步，更新一次theta(w,参数列) cost_history.append(self.cost_function(self.data,self.labels))#cost_history是个列表，用到列表的方法将损失值添加到列表的末尾，没走一步更新一次列表，用于记录损失值 return cost_history def gradient_step(self,alpha):#进行一次参数更新，走一步，学习率做步长 """梯度下降""" num_examples = self.data.shape[0]#样本的行（样本个数） prediction = Linearclass.hypothesis(self.data,self.theta)#调用预测值函数 t = prediction - self.labels#设置了一个临时变量，预测值-真实值 theta = self.theta theta = theta - alpha(1/num_examples)(np.dot(t.T,self.data))#公式 self.theta = theta#更新 def cost_function(self,data,labels):#损失函数 """损失计算方法""" num_examples = self.data.shape[0]#样本个数 t = Linearclass.hypothesis(self.data,self.theta)-labels#预测值-真实值 cost = (1/2)*np.dot(t.T,t)#损失值1/2的差值的平方，定义了一个均方误差。 return cost[0][0] @staticmethod#为了可以直接调用类方法，不用实例化 def hypothesis(data,theta):#数据和参数 predictions = np.dot(data,theta)#预测值，当前的数据成一组参数，y=wx，矩阵乘法，结果是一列向量，在上面函数会引用 return predictions#返回预测值 def get_cost(self,data,labels): """得到损失""" return self.cost_function(data,labels)#得到当前的损失值 def predict(self,data): """ 用训练的参数模型，与预测得到回归值结果 """ predictions = Linearclass.hypothesis(data,self.theta) return predictions import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from 梯度下降类 import Linearclass data = pd.read_csv("credit-overdue.csv") """预处理删除空白值""" data = data.dropna(how='any') data = data.reset_index(drop=True) """划分训练集和测试集""" data_train, data_test, y_train, y_test = train_test_split(data[["debt","income"]].values,data[["overdue"]].values,test_size=0.2,random_state=42)#.values为了转化成矩阵 input_param_name_1= ("debt") input_param_name_2= ("income") output_param_name= ("overdue") num_iterations = 500 learning_rate = 0.01 linear_regression = Linearclass(data_train,y_train) (theta,cost_history) = linear_regression.train(learning_rate,num_iterations) print ('开始时的损失：',cost_history[0]) print ('训练后的损失：',cost_history[-1]) """ 绘制每次迭代后的损失值的函数 """ """ plt.plot(range(num_iterations),cost_history) plt.xlabel('Iter') plt.ylabel('cost') plt.title('GD') plt.show() predictions_num = 100 x_predictions = np.linspace(data_train.min(),data_train.max(),predictions_num).reshape(predictions_num, 1)#reshape改变数组形状 y_predictions = linear_regression.predict(x_predictions) plt.scatter(data_train,y_train,label='Train data') plt.scatter(data_test,y_test,label='test data') plt.plot(x_predictions,y_predictions,'r',label = 'Prediction') plt.xlabel("debt","income") plt.ylabel("overdue") plt.title('overdue') plt.legend() plt.show() """ 帮我改改把

所以原代码中的计算是t.T @ data，这等价于sum(t_i * x_i)，但正确的梯度应该是X.T @ t，即每个特征与误差的内积。这里可能有一个错误，需要检查。举个例子，假设data是(m, n)，theta是(n,1)，预测是(m,1)，t是(m,...

# 结果可视化（可视化数字样本的分布） def plot_digits(images, labels, predictions): fig, ax = plt.subplots(3, 10, figsize=(10,

def plot_digits(images, labels, predictions): fig, ax = plt.subplots(3, 10, figsize=(10, 4)) for i in range(3): for j in range(10): index = i * 10 + j ax[i][j].imshow(images[index].reshape...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from pyswarm import pso import matplotlib.pyplot as plt file = "zhong.xlsx" data = pd.read_excel(file) #reading file X=np.array(data.loc[:,'种植密度':'有效积温']) y=np.array(data.loc[:,'产量']) # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=42) # 定义BP神经网络模型 def nn_model(X): model = Sequential() model.add(Dense(X[0], input_dim=X_train.shape[1], activation='relu')) model.add(Dense(X[1], activation='relu')) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') return model # 定义适应度函数 def fitness_func(X): model = nn_model(X) model.fit(X_train, y_train, epochs=100, verbose=0) score = model.evaluate(X_test, y_test, verbose=0) return score # 定义变量的下限和上限 lb = [5, 5] ub = [20, 20] # 利用PySwarm库实现改进的粒子群算法来优化BP神经网络预测模型 result = pso(fitness_func, lb, ub) # 输出最优解和函数值 print('最优解:', result[0]) print('最小函数值:', result[1]) # 绘制预测值和真实值对比图 model = nn_model(result[0]) model.fit(X_train, y_train, epochs=100, verbose=0) y_pred = model.predict(X_test) plt.plot(y_test, y_pred, 'o') plt.xlabel('True values') plt.ylabel('Predictions') plt.show() # 绘制损失函数曲线图 model = nn_model(result[0]) history = model.fit(X_train, y_train, epochs=100, validation_data=(X_test, y_test), verbose=0) plt.plot(history.history['loss'], label='train') plt.plot(history.history['val_loss'], label='test') plt.legend() plt.show()

这段代码实现了一个利用改进的粒子群算法优化BP神经网络预测模型的过程，并绘制了预测值和真实值对比图以及损失函数曲线图。其中，数据集被分为训练集和测试集，BP神经网络模型被定义，并且适应度函数被定义为神经...

翻译代码import matplotlib.pyplot as plt import numpy as np from sklearn import datasets, linear_model from sklearn.metrics import mean_squared_error, r2_score # Load the diabetes dataset diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True) # Use only one feature diabetes_X = diabetes_X[:, np.newaxis, 2] # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes_y[:-20] diabetes_y_test = diabetes_y[-20:] # Create linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets regr.fit(diabetes_X_train, diabetes_y_train) # Make predictions using the testing set diabetes_y_pred = regr.predict(diabetes_X_test) # The coefficients print("Coefficients: \n", regr.coef_) # The mean squared error print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred)) # The coefficient of determination: 1 is perfect prediction print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred)) # Plot outputs plt.scatter(diabetes_X_test, diabetes_y_test, color="black") plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3) plt.xticks(()) plt.yticks(()) plt.show()

这是一段使用 Scikit-learn 库进行线性回归的 Python 代码。具体步骤如下： 1.导入需要的库，包括 Matplotlib 和 Numpy 用于绘图和数据处理，以及 Scikit-learn 中的 datasets、linear_model 和 metrics 模块。...

sns.set_style('white') sns.set_context("paper", font_scale=1.5) plt.figure(figsize=(16,5)) plt.plot(y_test[:720], label = "test") plt.plot(y_pred[:720], label = "Predictions") plt.tick_params(left=False, labelleft=True) #remove ticks plt.tight_layout() sns.despine(top=True) plt.subplots_adjust(left=0.07) plt.xlabel('Time Index') plt.ylabel('Energy Consumption') plt.legend(loc='upper right'); plt.savefig('Fig8.png', dpi=500)在这段代码中，y轴坐标的值是什么

在这段代码中，y轴坐标的值是 "Energy Consumption"，该标签是通过 plt.ylabel('Energy Consumption') 进行设置的。这段代码的作用是绘制一个时间序列图，其中 y 轴表示能源消耗量，x 轴表示时间索引。...

import tensorflow as tf import tensorflow_hub as hub import tensorflow_text as text # 需要安装 import kagglehub import matplotlib.pyplot as plt import os from sklearn.model_selection import train_test_split # 示例：使用IMDB影评数据集 dataset, info = tf.load('imdb_reviews', with_info=True, as_supervised=True) train_data, test_data = dataset['train'], dataset['test'] # 转换为numpy格式 texts = [ex[0].numpy().decode('utf-8') for ex in train_data] labels = [ex[1].numpy() for ex in train_data] # 使用预训练的小型BERT（如bert_en_uncased_L-4_H-512_A-8） # 下载模型 bert_preprocess = hub.KerasLayer(kagglehub.model_download("tensorflow/bert/tensorFlow2/en-uncased-preprocess")) bert_encoder = hub.KerasLayer(kagglehub.model_download("tensorflow/bert/tensorFlow2/bert-en-uncased-l-4-h-512-a-8")) # 构建分类模型 inputs = tf.keras.layers.Input(shape=(), dtype=tf.string) preprocessed = bert_preprocess(inputs) outputs = bert_encoder(preprocessed) pooled_output = outputs['pooled_output'] x = tf.keras.layers.Dense(64, activation='relu')(pooled_output) predictions = tf.keras.layers.Dense(3, activation='softmax')(x) # 假设3分类 model = tf.keras.Model(inputs=inputs, outputs=predictions) lowadam = tf.keras.optimizers.Adam(learning_rate=3e-5) # 更小的学习率 model.compile(optimizer=lowadam, loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 增加验证集和早停机制 val_texts = ["Great product", "Poor quality", "Average experience"] val_labels = [0, 1, 2] train_texts, val_texts, train_labels, val_labels = train_test_split(texts, labels, test_size=0.2) # 调整训练参数 history = model.fit( texts, labels, validation_data=(val_texts, val_labels), epochs=500, batch_size=32, callbacks=[tf.keras.callbacks.EarlyStopping(patience=3)] ) file = open('./weights.txt', 'w') # 参数提取 for v in model.trainable_variables: file.write(str(v.name) + '\n') file.write(str(v.shape) + '\n') file.write(str(v.numpy()) + '\n') file.close() loss = history.history['loss'] val_loss = history.history['val_loss'] plt.plot(loss, label='Training Loss') plt.plot(val_loss, label='Validation Loss') plt.title('Training and Validation Loss') plt.legend() plt.show() model.save('transform_model.keras')如何增加验证集数据

def augment_text(text, n=2): return list(set(augmenter.augment(text)[:n])) # 生成n个增强样本 # 对训练数据做增强（注意：仅增强训练集！） augmented_texts = [] augmented_labels = [] for text, label in ...

def plot_image(i, predictions_array, true_label, img): predictions_array, true_label, img = predictions_array, true_label[i], img[i] plt.grid(False) plt.xticks([]) plt.yticks([])

相关推荐

详解使用python绘制混淆矩阵（confusion_matrix）

Neural_Network：使用Python实现简单的前馈神经网络算法

xgb_python算法_xgboost_xgboost二分类_源码

def plot_confuse(model, x_val, y_val): predictions = model.predict_classes(x_val) truelabel = y_val.argmax(axis=-1) # 将one-hot转化为label conf_mat = confusion_matrix(y_true=truelabel, y_pred=predictions) plt.figure() plot_confusion_matrix(conf_mat, range(np.max(truelabel) + 1))

# 结果可视化（可视化数字样本的分布） def plot_digits(images, labels, predictions): fig, ax = plt.subplots(3, 10, figsize=(10,

大家在看

读写通达信股票软件二进制dat文件

CMOS反相器的掩膜版图-集成电路版图设计

调制解调文档

Windows系统kb2577795-kb2553549 补丁

ISO/IEC 27005:2022 英文原版

最新推荐

虚拟串口软件：实现IP信号到虚拟串口的转换

【Python进阶篇】：掌握这些高级特性，让你的编程能力飞跃提升

后端调用ragflow api

IE6下实现PNG图片背景透明的技术解决方案

【欧姆龙触摸屏故障诊断全攻略】

Educoder综合练习—C&C++选择结构

VBS简明教程：批处理之家论坛下载指南

【欧姆龙触摸屏：新手必读的10个操作技巧】

阿里云物联网平台不支持新购

诺基亚C6-00安全稳定中文刷机包发布