jupyter notebook中split使用方法

在Jupyter Notebook中，可以使用split方法来拆分单元格。具体使用方法如下：

1. 首先，选中需要拆分的单元格。可以通过点击单元格左侧的边框来选中单元格。

2. 然后，使用快捷键Shift + M（或者在菜单栏中选择"Edit" -> "Split Cell"）来拆分选中的单元格。拆分后，原来的单元格会被分成两个单元格，光标会停留在新拆分出来的单元格中。

3. 如果需要继续拆分单元格，可以重复上述步骤。

需要注意的是，拆分单元格只能在代码单元格和Markdown单元格中进行，对于其他类型的单元格（如Raw NBConvert、Heading等），拆分操作是无效的。

相关问题

jupyter notebook中GBM调参

以下是在Jupyter Notebook中使用LightGBM进行调参的步骤：

1.导入必要的库和数据集

import lightgbm as lgb
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

2.将数据集转换为LightGBM的数据格式

train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

3.设置参数空间并进行调参

from hyperopt import fmin, tpe, hp, STATUS_OK, Trials

space = {
    'max_depth': hp.quniform('max_depth', 2, 10, 1),
    'learning_rate': hp.loguniform('learning_rate', -5, 0),
    'n_estimators': hp.quniform('n_estimators', 50, 500, 1),
    'subsample': hp.uniform('subsample', 0.1, 1),
    'colsample_bytree': hp.uniform('colsample_bytree', 0.1, 1),
    'reg_alpha': hp.uniform('reg_alpha', 0, 1),
    'reg_lambda': hp.uniform('reg_lambda', 0, 1),
    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
    'objective': 'binary',
    'boosting_type': 'gbdt',
    'metric': 'binary_logloss',
    'num_threads': 4,
    'verbose': -1
}

def objective(params):
    model = lgb.train(params, train_data, valid_sets=[test_data], num_boost_round=1000, early_stopping_rounds=50, verbose_eval=False)
    score = model.best_score['valid_0']['binary_logloss']
    return {'loss': score, 'status': STATUS_OK}

trials = Trials()
best = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=100, trials=trials)

4.输出最佳参数

print(best)

生成一个deepspeech的训练代码，能在jupyter notebook中使用

好的，以下是利用DeepSpeech进行训练的代码，可以在Jupyter Notebook中使用：

!pip install deepspeech

import os
import numpy as np
import matplotlib.pyplot as plt
import deepspeech
import scipy.io.wavfile as wav

# 设置模型路径
model_path = "deepspeech-0.9.3-models.pbmm"
# 设置字典路径
alphabet_path = "deepspeech-0.9.3-models.scorer"

# 加载DeepSpeech模型
model = deepspeech.Model(model_path)

# 加载字典
model.enableExternalScorer(alphabet_path)

# 设置一些训练参数
batch_size = 64
n_epochs = 10
learning_rate = 0.0001
dropout_rate = 0.2

# 定义训练函数
def train(model, audio_files, transcripts, batch_size, n_epochs, learning_rate, dropout_rate):
    # 将音频文件和对应的文本转换为mfcc特征和标签
    def convert_audio_to_features(file_path):
        rate, audio = wav.read(file_path)
        features = model.sttWithMetadata(audio)
        return features[0].mfcc.tolist(), features[0].transcript

    # 创建训练集和验证集
    n_samples = len(audio_files)
    n_train = int(0.8 * n_samples)
    indices = np.arange(n_samples)
    np.random.shuffle(indices)
    train_indices = indices[:n_train]
    val_indices = indices[n_train:]

    train_features = []
    train_labels = []
    for i in train_indices:
        features, label = convert_audio_to_features(audio_files[i])
        train_features.append(features)
        train_labels.append(label)

    val_features = []
    val_labels = []
    for i in val_indices:
        features, label = convert_audio_to_features(audio_files[i])
        val_features.append(features)
        val_labels.append(label)

    # 定义模型结构
    input_shape = train_features[0].shape
    n_classes = len(set(train_labels))
    model = deepspeech.models.DeepSpeech(input_shape, n_classes, dropout_rate)

    # 定义优化器和损失函数
    optimizer = deepspeech.optimizers.Adam(learning_rate)
    loss_fn = deepspeech.losses.SparseCategoricalCrossentropy(from_logits=True)

    # 定义训练和验证函数
    @tf.function
    def train_step(x, y):
        with tf.GradientTape() as tape:
            logits = model(x, training=True)
            loss = loss_fn(y, logits)
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
        return loss

    @tf.function
    def val_step(x, y):
        logits = model(x, training=False)
        loss = loss_fn(y, logits)
        return loss

    # 开始训练循环
    history = {"train_loss": [], "val_loss": []}
    for epoch in range(n_epochs):
        train_loss = 0.0
        for i in range(0, n_train, batch_size):
            x_batch = train_features[i:i+batch_size]
            y_batch = train_labels[i:i+batch_size]
            x_batch = tf.ragged.constant(x_batch).to_tensor()
            y_batch = tf.strings.unicode_split(y_batch, "UTF-8")
            y_batch = tf.ragged.constant(y_batch).to_tensor()
            train_loss += train_step(x_batch, y_batch)
        train_loss /= (n_train / batch_size)

        val_loss = 0.0
        for i in range(0, len(val_features), batch_size):
            x_batch = val_features[i:i+batch_size]
            y_batch = val_labels[i:i+batch_size]
            x_batch = tf.ragged.constant(x_batch).to_tensor()
            y_batch = tf.strings.unicode_split(y_batch, "UTF-8")
            y_batch = tf.ragged.constant(y_batch).to_tensor()
            val_loss += val_step(x_batch, y_batch)
        val_loss /= (len(val_features) / batch_size)

        print(f"Epoch {epoch+1}/{n_epochs}: train_loss={train_loss:.4f}, val_loss={val_loss:.4f}")
        history["train_loss"].append(train_loss.numpy())
        history["val_loss"].append(val_loss.numpy())

    return history

# 定义训练数据集和标签
audio_files = ["audio1.wav", "audio2.wav", "audio3.wav"]
transcripts = ["transcript1", "transcript2", "transcript3"]

# 开始训练
history = train(model, audio_files, transcripts, batch_size, n_epochs, learning_rate, dropout_rate)

# 绘制训练和验证损失曲线
plt.plot(history["train_loss"], label="train_loss")
plt.plot(history["val_loss"], label="val_loss")
plt.legend()
plt.show()

需要注意的是，此代码中使用了DeepSpeech 0.9.3版本的模型和字典，如果需要使用其他版本的模型和字典，需要相应地更改文件路径。另外，此代码可能需要一些调整才能适配您的数据集和训练需求。