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为了学习的需要，我想检查tf.Metrics.mean_squared_error的准确性。令我惊讶的是，他们有很大的不同。我正在寻求解释。这里是我的体验简介，然后是我的示例代码：tf.metrics.mean_squared_error的准确性
1）通过tf.Metrics.mean_squared_error评估整个训练数据的训练过的玩具模型;和
2）在步骤1之后立即再次评估，首先收集给定相同整个训练数据的所有“Xs”（或图像）的预测，然后用所有的基本事实（或标签）的训练数据和预测。
我有两个未经验证的解释：（1）浮点精度损失累积和（2）tf.Metrics.mean_square_error在其实现中应用看似移动的平均值，导致不准确。
任何相关的想法，非常感谢！谢谢！
import tensorflow as tf
from numpy import genfromtxt
tf.logging.set_verbosity(tf.logging.INFO)
# (hyper)parameters
batch_size = 200
num_epochs = 1000
steps = 1000
# prepare data
with tf.Session() as sess:
training_x = sess.run(tf.random_normal([2048, 16], mean=-1, stddev=4, dtype=tf.float64))
training_y = norm = sess.run(tf.random_normal([2048, 1], mean=-1, stddev=4, dtype=tf.float64))
# input function
_input_fn = lambda _input_path: genfromtxt(_input_path, delimiter=',')
input_training = tf.contrib.learn.io.numpy_input_fn({"input": training_x}, training_y,
batch_size=batch_size, num_epochs=num_epochs)
input_evaluate_train_data = tf.contrib.learn.io.numpy_input_fn({"input": training_x}, training_y)
# remember to give the same column name as used in _input_fn
features = [tf.contrib.layers.real_valued_column('input', dimension=16)]
regressor = tf.contrib.learn.DNNRegressor(feature_columns=features,
hidden_units=[32, 8],
dropout=0.1,
model_dir="testDNNR/result",
optimizer=tf.train.AdamOptimizer(learning_rate=0.008),
activation_fn=tf.nn.elu)
# training
regressor.fit(input_fn=input_training, steps=steps)
# testing with training data
eval_metric_ops = {
"mse": lambda targets, predictions: tf.metrics.mean_squared_error(tf.cast(targets, tf.float64), predictions)
}
ev = regressor.evaluate(input_fn=input_evaluate_train_data, steps=1, metrics=eval_metric_ops)
pred = regressor.predict(input_fn=input_evaluate_train_data, as_iterable=False)
# using my MSE
mse = ((training_y - pred) ** 2).mean()
print ("evaluation result given training data using my MSE: " + str(mse))
print ("evaluation result given training data using the library built-in MSE: " + str(ev))