estimator.predict引发“ValueError：None values not supported”

Question

所以我基本上采用了适应这个模型的tensorflow教程的代码：

试图模拟神经网络以识别“阶梯”形状，如下所示：

import numpy as np
import tensorflow as tf
import _pickle as cPickle

with open("var_x.txt", "rb") as fp:   # Unpickling
    var_x = cPickle.load(fp)

with open("var_y.txt", "rb") as fp:   # Unpickling
    var_y = cPickle.load(fp)

# Declare list of features, we only have one real-valued feature
def model_fn(features, labels, mode):

  # Build a linear model and predict values
  W = tf.get_variable("W", [4], dtype=tf.float64)
  b = tf.get_variable("b", [1], dtype=tf.float64)
  y = tf.sigmoid( W*features['x'] + b)
  # Loss sub-graph
  loss = tf.reduce_sum(tf.square(y - labels))
  # Training sub-graph
  global_step = tf.train.get_global_step()
  optimizer = tf.train.GradientDescentOptimizer(0.01)
  train = tf.group(optimizer.minimize(loss),
                   tf.assign_add(global_step, 1))
  # EstimatorSpec connects subgraphs we built to the
  # appropriate functionality.
  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions=y,
      loss=loss,
      train_op=train)

estimator = tf.estimator.Estimator(model_fn=model_fn)
# define our data sets
x_train = np.array(var_x)
y_train = np.array(var_y)

input_fn = tf.estimator.inputs.numpy_input_fn(
    {"x": x_train}, y_train, batch_size=4, num_epochs=10, shuffle=True)

# train
estimator.train(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did.

print(estimator.get_variable_value("b"))
print(estimator.get_variable_value("W"))

new_samples = np.array(
    [255., 1., 255., 255.], dtype=np.float64)
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": new_samples},
    num_epochs=1,
    shuffle=False)

predictions = list(estimator.predict(input_fn=predict_input_fn))

print(predictions)

问题在于，当我试图预测一个应该明显是楼梯的数字：[255。，1.，255。，255。]我得到一个“ValueError：None values not supported。”。训练效果很好（除了它找到的权重与这里的权重不太相似的事实：http://blog.kaggle.com/2017/11/27/introduction-to-neural-networks/）。但预测方法不起作用。此代码必须只是张量流示例的副本，适用于x的四维向量。

Answer 1

在model_fn中，您可以在每种模式（火车/评估/预测）中定义loss。这意味着即使在预测模式下，也会使用labels并需要提供。

当你处于预测模式时，你实际上只需要返回预测，这样你就可以从函数中提前返回：

def model_fn(features, labels, mode):
    #...
    y = ...
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=y)
    #...

顺便说一句，W * features会返回一个形状(4,)的张量，您需要在添加偏差之前对其进行求和。