我目前正在学习 tensorflow ,但无法理解为什么 tensorflow 不能对以下简单回归问题进行正确的预测。
X是1000到8000之间的随机数 Y 为 X + 250
所以如果 X 是 2000,Y 就是 2250。对我来说这似乎是一个线性回归问题。然而,当我尝试进行预测时,它与我的预期相差甚远,X of 1000 给我的预测是 1048,而不是 1250。
损失和平均损失也很大:
{'average_loss': 10269.81, 'loss': 82158.48, 'global_step': 1000}
完整代码如下:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.model_selection import train_test_split
x_data = np.random.randint(1000, 8000, 1000000)
y_true = x_data + 250
feat_cols = [tf.feature_column.numeric_column('x', shape=[1])]
estimator = tf.estimator.LinearRegressor(feature_columns=feat_cols)
x_train, x_eval, y_train, y_eval = train_test_split(x_data, y_true, test_size=0.3, random_state=101)
input_func = tf.estimator.inputs.numpy_input_fn({'x': x_train}, y_train, batch_size=8, num_epochs=None, shuffle=True)
train_input_func = tf.estimator.inputs.numpy_input_fn({'x': x_train}, y_train, batch_size=8, num_epochs=1000, shuffle=False)
eval_input_func = tf.estimator.inputs.numpy_input_fn({'x': x_eval}, y_eval, batch_size=8, num_epochs=1000, shuffle=False)
estimator.train(input_fn=input_func, steps=1000)
train_metrics = estimator.evaluate(input_fn=train_input_func, steps=1000)
eval_metrics = estimator.evaluate(input_fn=eval_input_func, steps=1000)
print(train_metrics)
print(eval_metrics)
brand_new_data = np.array([1000, 2000, 7000])
input_fn_predict = tf.estimator.inputs.numpy_input_fn({'x': brand_new_data}, shuffle=False)
prediction_result = estimator.predict(input_fn=input_fn_predict)
print(list(prediction_result))
我做错了什么或者我误解了线性回归的含义吗?
最佳答案
我认为当你调整一些超参数时就会发生这种情况。我还将优化器更改为 AdamOptimizer。
主要是批量大小为1,epochs为None。
train_input_func = tf.estimator.inputs.numpy_input_fn({'x': x_train}, y_train, batch_size=1, num_epochs=None, shuffle=True)
代码:
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
x_data = np.random.randint(1000, 8000, 10000)
y_true = x_data + 250
feat_cols = tf.feature_column.numeric_column('x')
optimizer = tf.train.AdamOptimizer(1e-3)
estimator = tf.estimator.LinearRegressor(feature_columns=[feat_cols],optimizer=optimizer)
x_train, x_eval, y_train, y_eval = train_test_split(x_data, y_true, test_size=0.3, random_state=101)
train_input_func = tf.estimator.inputs.numpy_input_fn({'x': x_train}, y_train, batch_size=1, num_epochs=None,
shuffle=True)
eval_input_func = tf.estimator.inputs.numpy_input_fn({'x': x_eval}, y_eval, batch_size=1, num_epochs=None,
shuffle=True)
estimator.train(input_fn=train_input_func, steps=1005555)
train_metrics = estimator.evaluate(input_fn=train_input_func, steps=10000)
eval_metrics = estimator.evaluate(input_fn=eval_input_func, steps=10000)
print(train_metrics)
print(eval_metrics)
brand_new_data = np.array([1000, 2000, 7000])
input_fn_predict = tf.estimator.inputs.numpy_input_fn({'x': brand_new_data}, num_epochs=1,shuffle=False)
prediction_result = estimator.predict(input_fn=input_fn_predict)
for prediction in prediction_result:
print(prediction['predictions'])
指标:
{'average_loss': 3.9024353e-06, 'loss': 3.9024353e-06, 'global_step': 1005555}
{'average_loss': 3.9721594e-06, 'loss': 3.9721594e-06, 'global_step': 1005555}
[1250.003]
[2250.002]
[7249.997]
关于python - 为什么 TensorFlow 估计器无法进行这种简单的线性回归预测,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/51573068/