tf.estimator - 如何在每个纪元后打印测试集的准确度？

Question

我希望能够在具有不同数量的时期的测试MNIST数据集上打印这种神经网络模型的准确性 - 我在最后使用for循环并测试1对2个时期，但由于某种原因我两者都获得相同的准确度。在for循环的第二次迭代中，它是不是实际上没有在2个时期训练新模型？

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("%f\n" % e['accuracy'])
f.close()

Answer 1

你可以使用train_and_evaluate。首先，您需要为列车模式和eval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RunConfig和save_checkpoints_steps，它控制评估的频率

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