tf.estimator - 如何在每个时期后在测试集上打印精度?

Question

我希望能够在具有不同纪元数的测试 MNIST 数据集上打印此神经网络模型的准确性 - 我在最后使用 for 循环并测试 1 与 2 个纪元，但由于某种原因，我得到了相同的精度。在 for 循环的第二次迭代中，它实际上并没有训练一个具有 2 个 epoch 的新模型吗？

任何想法都非常感谢！

from __future__ import print_function
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)
import tensorflow as tf
# Parameters
learning_rate = 0.1
num_steps = 1000
batch_size = 128
display_step = 100
# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 10 # MNIST total classes (0-9 digits)

# Define the neural network
def neural_net(x_dict):
# TF Estimator input is a dict, in case of multiple inputs
x = x_dict['images']
# Hidden fully connected layer with 256 neurons
layer_1 = tf.layers.dense(x, n_hidden_1)
# Hidden fully connected layer with 256 neurons
layer_2 = tf.layers.dense(layer_1, n_hidden_2)
# Output fully connected layer with a neuron for each class
out_layer = tf.layers.dense(layer_2, num_classes)
return out_layer

# Define the model function (following TF Estimator Template)
def model_fn(features, labels, mode):
# Build the neural network
logits = neural_net(features)
# Predictions
pred_classes = tf.argmax(logits, axis=1)
pred_probas = tf.nn.softmax(logits)
# If prediction mode, early return
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)
##squared loss
loss_op=tf.reduce_sum(tf.pow(tf.subtract(pred_probas,tf.one_hot(labels,10)), 2))/batch_size
##cross-entropy loss (exclusive labels)
#loss_op=tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred_probas, labels=labels))

# Evaluate the accuracy of the model
acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)

# TF Estimators requires to return a EstimatorSpec, that specify
# the different ops for training, evaluating, ...
estim_specs = tf.estimator.EstimatorSpec(
mode=mode,
predictions=pred_classes,
loss=loss_op,
train_op=train_op,
eval_metric_ops={'accuracy': acc_op})
return estim_specs
# Build the Estimator
model = tf.estimator.Estimator(model_fn)
f=open("nn_errors_sqloss.txt","w")
for i in [1,2]:
# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.train.images}, y=mnist.train.labels,
batch_size=batch_size, num_epochs=i, shuffle=True)
# Train the Model
model.train(input_fn, steps=num_steps)
# Evaluate the Model
# Define the input function for evaluating
input_fn_test = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.test.images}, y=mnist.test.labels,
batch_size=batch_size, shuffle=False)
# Use the Estimator 'evaluate' method
e = model.evaluate(input_fn_test)
f.write("%fn" % e['accuracy'])
f.close()

Answer 1

您可以使用train_and_evaluate.首先，您需要为训练模式和评估模式返回不同的EstimatorSpec。

tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

您还需要添加带有save_checkpoints_steps的RunConfig，它控制应执行评估的频率

run_config = tf.estimator.RunConfig(save_checkpoints_steps=1000)
train_spec = tf.estimator.TrainSpec(input_fn, max_steps)
eval_spec = tf.estimator.EvalSpec(input_fn) 
tf.estimator.train_and_evaluate(model, train_spec, eval_spec)

https://www.tensorflow.org/api_docs/python/tf/estimator/train_and_evaluate