百度网盘提取码：lala
二、代码运行环境： Tensorflow-gpu==2.4.0 Python==3.7 三、训练代码如下所示：import tensorflow as tf
import os
import pandas as pd
import matplotlib.pyplot as plt
# 环境变量配置
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
# 数据的读取
data = pd.read_csv(r'dataset/getter.csv')
# 数据的展示
plt.scatter(data.Education, data.Income)
plt.show()
# 模型的构建
x = data.Education
y = data.Income
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1, input_shape=(1,)))
# 模型的相关配置
model.compile(
optimizer='adam',
loss='mse'
)
# 模型的训练
history = model.fit(x, y, epochs=60000, batch_size=20)
# 模型的预测
pre_y = model.predict(x)
pre_y = pre_y.flatten()
# 预测结果的展示
plt.scatter(x, y)
plt.plot(x, pre_y, 'red')
plt.show()
# 模型的保存
model.save(r'model_data/model.h5')
四、预测代码如下所示：
import tensorflow as tf import os import pandas as pd import matplotlib.pyplot as plt # 环境变量配置 os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices' os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # 数据的读取 data = pd.read_csv(r'dataset/getter.csv') x = data.Education y = data.Income # 模型的加载 pre_model = tf.keras.models.load_model(r'model_data/model.h5') # 结果的预测 pre_y = pre_model.predict(x) # 预测结果的展示 plt.scatter(x, y) plt.plot(x, pre_y, 'red') plt.show()五、预测结果展示
