将现有项目转换为Tensorflow

pip install tensorflow

import tensorflow as tf

# 假设你有一个数据集
data = ...
labels = ...

dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.shuffle(buffer_size=len(data)).batch(batch_size)

from tensorflow.keras import layers, models

model = models.Sequential([
    layers.Dense(64, activation='relu', input_shape=(input_dim,)),
    layers.Dense(64, activation='relu'),
    layers.Dense(num_classes, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(dataset, epochs=num_epochs)

import tensorflow as tf
import numpy as np

# 生成模拟数据
x_train = np.linspace(-1, 1, 101)
y_train = 2 * x_train + np.random.randn(*x_train.shape) * 0.33

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

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

# 训练模型
history = model.fit(x_train, y_train, epochs=100)

# 预测
print(model.predict([0.5]))