y_train = to_categorical(y_train, 10) y_test = to_categorical(y_test, 10)是什么意思

Mnist_keras.py.zip_keras_kerasmnist_py神经网络_神经网络_神经网络 keras

y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10) 3. 构建模型：创建一个Sequential模型，然后添加卷积层（Conv2D）、池化层（MaxPooling2D）、Dropout层...

ml_data.zip

X_train, X_test, y_train, y_test = train_test_split( data.drop('target', axis=1), # 特征 data['target'], # 目标变量 test_size=0.2, # 20%的数据作为测试集 random_state=42) # 训练模型 model = Linear...

y_train = np_utils.to_categorical(y_train, 10) y_test = np_utils.to_categorical(y_test, 10)是什么意思

- y_train 和 y_test 是样本的分类标签，每个标签是一个整数值，表示该样本属于哪个类别。 - np_utils.to_categorical 是 Keras 中的一个实用函数，用于将整数型的类别标签转换为 one-hot 编码形式。 - 10 ...

# Def X and Y X = data.drop('Outcome', axis=1) y = data['Outcome'] X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1) y_train = to_categorical(y_train) y_test = to_categorical(y_test)这段代码是什么意思

第三行代码使用 to_categorical() 函数将训练集的标签 y_train 进行独热编码，将其从原来的类别值转换为了一个长度为类别数目的向量，向量中只有一个元素为 1，其余均为 0，表示该样本属于这个类别。同理，第四行...

解释这段代码# Def X and Y X = data.drop('Outcome', axis=1) y = data['Outcome'] X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1) y_train = to_categorical(y_train) y_test = to_categorical(y_test)

- train_test_split 方法将数据集随机分割成训练集和测试集，其中 train_size=0.8 表示训练集占原始数据集的80%； - shuffle=True 表示在分割数据集之前要先打乱数据集的顺序，random_state=1 表示打乱顺序...

y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes)

在这段代码中，num_classes 表示类别的数量，而 y_train 和 y_test 是训练集和测试集的标签。通过 keras.utils.to_categorical() 函数将标签进行one-hot编码，最终得到的是一个二维的数组，其中每一行表示一个样本的...

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)对该数据进行归一化和标准化处理

X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # 对数据进行归一化处理 min_max_scaler = MinMaxScaler() X_train_norm = min_max_scaler.fit_transform(X_train) X_...

解释下这段代码：def generate_word_vector(word): return np.zeros((embedding_dim,)) embeddings_index = {} for word, i in word_index.items(): embeddings_index[word] = generate_word_vector(word) embedding_matrix = np.zeros((vocab_size, embedding_dim)) for word, i in word_index.items(): embedding_vector = embeddings_index.get(word) if embedding_vector is not None: embedding_matrix[i] = embedding_vector maxlen = 200 x_train = pad_sequences(x_train, maxlen=maxlen) x_test = pad_sequences(x_test, maxlen=maxlen) num_classes = np.max(y_train) + 1 y_train = to_categorical(y_train, num_classes) y_test = to_categorical(y_test, num_classes) train_x, dev_x, train_y, dev_y = train_test_split(x_train,y_train,test_size=0.2,random_state=42) train_x = np.array(train_x) dev_x = np.array(dev_x) train_y = np.array(train_y) dev_y = np.array(dev_y)

这段代码主要用于构建一个文本分类模型。下面是代码的主要功能： 1. 定义了一个名为 generate_word_vector 的函数，用于生成单词的向量表示。函数使用 numpy 库创建一个全为零的数组，其形状为 (embedding_dim,)，...

def get_data(index_dict,word_vectors,combined,y): n_symbols = len(index_dict) + 1 # 所有单词的索引数，频数小于10的词语索引为0，所以加1 embedding_weights = np.zeros((n_symbols, vocab_dim)) # 初始化索引为0的词语，词向量全为0 for word, index in index_dict.items(): # 从索引为1的词语开始，对每个词语对应其词向量 embedding_weights[index, :] = word_vectors[word] x_train, x_test, y_train, y_test = train_test_split(combined, y, test_size=0.2) y_train = keras.utils.to_categorical(y_train,num_classes=3) y_test = keras.utils.to_categorical(y_test,num_classes=3) # print x_train.shape,y_train.shape return n_symbols,embedding_weights,x_train,y_train,x_test,y_test

这段代码主要是用于将原始的文本数据转换成神经网络...4. 返回n_symbols、embedding_weights、x_train、y_train、x_test、y_test这些变量，其中x_train和x_test是经过转换后的文本数据，y_train和y_test是对应的标签。

#将分类标签变为onehot编码 #Keras采用独热编码输出 num_class = 10 y_train = keras.utils.to_categorical(y_train, num_class) y_test = keras.utils.to_categorical(y_test, num_class) print(y_train[0]) 查看结果： y_train[0]： ,该样本对应数字是： y_test[0]: ,该样本对应数字是：

y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) # 输出转换后的标签 print("y_train[0]:", y_train[0]) print("y_test[0]:", y_test[0]) ...

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 # 对数据进行归一化处理 min_max_scaler = MinMaxScaler() X_train = min_max_scaler.fit_transform(X_train) X_test = min_max_scaler.transform(X_test) 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(Dropout(0.5)) 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 model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=32) y_pred = model.predict(X_test) y_pred = np.argmax(y_pred, axis=1) # 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)画它的loss曲线

history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=32) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model Loss') plt....

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

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...

请将此代码修改为tensorflow2.7，cuda11.2版本的代码 import tensorflow as tf from tensorflow import keras from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, ReLU from tensorflow.keras.utils import to_categorical # 加载MNIST数据集 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 数据预处理 x_train = x_train.astype("float32") / 255.0 x_test = x_test.astype("float32") / 255.0 x_train = tf.expand_dims(x_train, axis=3) x_test = tf.expand_dims(x_test, axis=3) y_train = to_categorical(y_train, num_classes=10) y_test = to_categorical(y_test, num_classes=10) # 构建LeNet-5模型 model = Sequential([ Conv2D(6, kernel_size=3, strides=1), MaxPooling2D(pool_size=2, strides=2), ReLU(), Conv2D(16, kernel_size=3, strides=1), MaxPooling2D(pool_size=2, strides=2), ReLU(), Flatten(), Dense(units=120, activation='relu'), Dense(units=84, activation='relu'), Dense(units=10, activation='softmax') ]) # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, batch_size=128, epochs=30, validation_data=(x_test, y_test)) # 在测试集上输出精度 test_loss, test_acc = model.evaluate(x_test, y_test) print('Test accuracy:', test_acc)

y_train = to_categorical(y_train, num_classes=10) y_test = to_categorical(y_test, num_classes=10) # Define LeNet-5 model model = Sequential([ Conv2D(6, kernel_size=3, strides=1), MaxPooling2D(pool_...

import keras from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D from pathlib import Path import tensorflow as tf # Load data set (x_train, y_train), (x_test, y_test) = cifar10.load_data() # Normalize data set to 0-to-1 range x_train = x_train.astype("float32") x_test = x_test.astype("float32") x_train = x_train / 255 x_test = x_test / 255 # Convert class vectors to binary class matrices # Our labels are single values from 0 to 9 # Instead, we want each label to be an array with on element set to 1 y_train = tf.keras.utils.to_categorical(y_train, 10) y_test = tf.keras.utils.to_categorical(y_test, 10)代码解释

然后，通过调用cifar10.load_data()函数加载CIFAR10数据集，并将训练集和测试集分别赋值给(x_train, y_train)和(x_test, y_test)。接下来，对数据集进行规范化处理，将像素值从0到255的范围转换为0到1之间的...

if name == 'main': file = "../datasets/识别_data.xlsx" dataset = read_data(file) train_X, train_Y, test_X, test_Y = split_train_test(dataset) # 读取数据集 x_train, x_test = normailize(train_X, test_X) # 归一化 y_train = tf.keras.utils.to_categorical(train_Y - 1) y_test = tf.keras.utils.to_categorical(train_Y - 1) model = DnnModel() model.compile( optimizer=keras.optimizers.Adam(), loss='categorical_crossentropy', metrics=['accuracy']) history = model.fit(x_train, y_train, batch_size=32, epochs=100, validation_data=(x_test, y_test), verbose=1) # 模型训练代码最后增加混淆矩阵

3. 对标签进行one-hot编码，使用to_categorical()函数将训练集和测试集的标签进行one-hot编码。 4. 定义DNN模型，使用Sequential()函数定义模型，包括4层，第一层32，第二层32，第三层32，第四层4分类。 5. 编译...

修改和补充下列代码得到十折交叉验证的平均每一折auc值和平均每一折aoc曲线，平均每一折分类报告以及平均每一折混淆矩阵 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)

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) ...

介绍一下这段代码的Depthwise卷积层def get_data4EEGNet(kernels, chans, samples): K.set_image_data_format('channels_last') data_path = '/Users/Administrator/Desktop/project 5-5-1/' raw_fname = data_path + 'concatenated.fif' event_fname = data_path + 'concatenated.fif' tmin, tmax = -0.5, 0.5 #event_id = dict(aud_l=769, aud_r=770, foot=771, tongue=772) raw = io.Raw(raw_fname, preload=True, verbose=False) raw.filter(2, None, method='iir') events, event_id = mne.events_from_annotations(raw, event_id={'769': 1, '770': 2,'770': 3, '771': 4}) #raw.info['bads'] = ['MEG 2443'] picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False) epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False, picks=picks, baseline=None, preload=True, verbose=False) labels = epochs.events[:, -1] print(len(labels)) print(len(epochs)) #epochs.plot(block=True) X = epochs.get_data() * 250 y = labels X_train = X[0:144,] Y_train = y[0:144] X_validate = X[144:216, ] Y_validate = y[144:216] X_test = X[216:, ] Y_test = y[216:] Y_train = np_utils.to_categorical(Y_train - 1) Y_validate = np_utils.to_categorical(Y_validate - 1) Y_test = np_utils.to_categorical(Y_test - 1) X_train = X_train.reshape(X_train.shape[0], chans, samples, kernels) X_validate = X_validate.reshape(X_validate.shape[0], chans, samples, kernels) X_test = X_test.reshape(X_test.shape[0], chans, samples, kernels) return X_train, X_validate, X_test, Y_train, Y_validate, Y_test kernels, chans, samples = 1, 3, 251 X_train, X_validate, X_test, Y_train, Y_validate, Y_test = get_data4EEGNet(kernels, chans, samples) model = EEGNet(nb_classes=3, Chans=chans, Samples=samples, dropoutRate=0.5, kernLength=32, F1=8, D=2, F2=16, dropoutType='Dropout') model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) checkpointer = ModelCheckpoint(filepath='/Users/XXX/baseline.h5', verbose=1, save_best_only=True) class_weights = {0: 1, 1: 1, 2: 1, 3: 1} fittedModel = model.fit(X_train, Y_train, batch_size=2, epochs=100, verbose=2, validation_data=(X_validate, Y_validate), callbacks=[checkpointer], class_weight=class_weights) probs = model.predict(X_test) preds = probs.argmax(axis=-1) acc = np.mean(preds == Y_test.argmax(axis=-1)) print("Classification accuracy: %f " % (acc))

这段代码是用于对EEG数据进行分类的。首先，它读取了一个数据集，将数据集分成训练集、验证集和测试集。然后，它使用EEGNet模型对数据进行训练和验证，并输出分类准确率。其中，EEGNet模型是一种针对EEG数据设计的...

from keras.datasets import reuters from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout, Embedding from keras.callbacks import EarlyStopping # 加载数据集 (X_train, y_train), (X_test, y_test) = reuters.load_data(num_words=10000) # 将文本数据转换为向量表示 maxlen = 500 X_train = pad_sequences(X_train, maxlen=maxlen) X_test = pad_sequences(X_test, maxlen=maxlen) # 将标签转换为one-hot编码 num_classes = 46 y_train = to_categorical(y_train, num_classes) y_test = to_categorical(y_test, num_classes) # 构建LSTM模型 model = Sequential() model.add(Embedding(10000, 128, input_length=maxlen)) model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(num_classes, activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 定义EarlyStopping回调函数 early_stopping = EarlyStopping(monitor='val_loss', patience=5) # 训练模型 batch_size = 32 epochs = 50 history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test), callbacks=[early_stopping]) # 评估模型 score = model.evaluate(X_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])给出一个输出结果

4. 该模型编译时使用 categorical_crossentropy 作为损失函数，使用 Adam 优化器进行模型优化，并使用 accuracy 作为评估指标。 5. 训练模型时使用了 batch_size=32，epochs=50。 6. 最后输出了模型的测试损失和测试...

import tensorflow as tf from tensorflow.keras.datasets import mnist from tensorflow.keras.utils import to_categorical # 1.读取数据集 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 将图像数据转换为float32类型 x_train = x_train.astype('float32') x_test = x_test.astype('float32') # 归一化图像数据 x_train /= 255 x_test /= 255 # 2.将y标签转为独热编码格式 y_train = to_categorical(y_train, 10) y_test = to_categorical(y_test, 10) # 3.创建模型 model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(600, activation='relu'), tf.keras.layers.Dropout(0.3), tf.keras.layers.Dense(400, activation='relu'), tf.keras.layers.Dropout(0.3), tf.keras.layers.Dense(200, activation='relu'), tf.keras.layers.Dropout(0.3), tf.keras.layers.Dense(10, activation='softmax') ]) # 搭建模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, batch_size=256, epochs=20, validation_split=0.2) test_loss, test_acc = model.evaluate(x_test, y_test) print('Test accuracy:', test_acc)请帮我给代码添加注释

y_train = to_categorical(y_train, 10) y_test = to_categorical(y_test, 10) # 3.创建模型 # 创建一个序列模型，包含一个输入层、三个隐藏层和一个输出层 model = tf.keras.Sequential([ tf.keras.layers....

y_train = to_categorical(y_train, 10) y_test = to_categorical(y_test, 10)是什么意思

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ml_data.zip

y_train = np_utils.to_categorical(y_train, 10) y_test = np_utils.to_categorical(y_test, 10)是什么意思

# Def X and Y X = data.drop('Outcome', axis=1) y = data['Outcome'] X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1) y_train = to_categorical(y_train) y_test = to_categorical(y_test)这段代码是什么意思

解释这段代码# Def X and Y X = data.drop('Outcome', axis=1) y = data['Outcome'] X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1) y_train = to_categorical(y_train) y_test = to_categorical(y_test)

y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes)

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