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🧠 深度学习入门课 📚 第一步：安装好Python环境 ```bash pip install tensorflow keras matplotlib seaborn numpy pandas scikit-learn pillow opencv-python ``` 🛠️ 第二步：导入必要的库 ```python import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import cv2 import os ``` 💻 制作数据集 🔍 加载并预处理数据 ```python data = pd.read_csv('path_to_your_data.csv') X = data.drop(columns=['target_column']) y = data['target_column'] scaler = StandardScaler() X_scaled = scaler.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42) ``` 🤖 开始训练模型 ```python model = tf.keras.models.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(X.shape[1],)), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) history = model.fit( X_train, y_train, epochs=20, validation_data=(X_test, y_test), batch_size=32 ) plt.plot(history.history['loss'], label='Training Loss') plt.plot(history.history['val_loss'], label='Validation Loss') plt.legend() plt.show() plt.plot(history.history['accuracy'], label='Training Accuracy') plt.plot(history.history['val_accuracy'], label='Validation Accuracy') plt.legend() plt.show() ``` 🌟 结果展示 ```python 假设我们的模型已经训练完成 predictions = model.predict(X_test) print(predictions[:5]) 输出前五条预测结果 ``` 希望这些例子能帮助你理解如何使用 TensorFlow 和 Keras 在 Python 中进行深度学习。记得根据你的具体需求调整代码哦！