TensorFlow 环境搭建的实现示例

python -m venv tf_env
source tf_env/bin/activate  # Linux/macOS
tf_env\Scripts\activate     # Windows

pip install tensorflow

pip install tensorflow-gpu

import tensorflow as tf
print(tf.__version__)
print(tf.config.list_physical_devices('GPU'))  # 检查 GPU 是否可用

import numpy as np
import tensorflow as tf

# 生成模拟数据
X = np.linspace(0, 10, 100)
y = 2 * X + 1 + np.random.randn(100) * 2

# 定义模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1])
])

# 编译模型
model.compile(optimizer='sgd', loss='mean_squared_error')

# 训练模型
model.fit(X, y, epochs=100, verbose=0)

# 预测结果
print(model.predict([7.0]))  # 输出接近 15

tf.debugging.set_log_device_placement(True)
with tf.device('/GPU:0'):
    a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
    b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
    c = tf.matmul(a, b)
print(c)

mnist = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train, X_test = X_train / 255.0, X_test / 255.0

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
    tf.keras.layers.MaxPooling2D((2,2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(X_train[..., tf.newaxis], y_train, epochs=5)
model.evaluate(X_test[..., tf.newaxis], y_test, verbose=2)