tensorflow estimator实例

```python
from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/home/ts/PycharmProjects/tf_tt/ts/tensorflow/pys/test1/tmp/tensorflow/mnist/input_data", one_hot=False)

print(mnist.train.labels.shape)

import tensorflow as tf

# ????
learning_rate = 0.1  # ???
num_steps = 1000  # ????
batch_size = 128  # ?????
display_step = 100  # ????
# ????

n_hidden_1 = 256  # ?????????
n_hidden_2 = 256  # ?????????
num_input = 784  # 28*28
num_classes = 10  # ????

#??????
def neural_net(x_dict):
    x=x_dict['images']
    layer_1=tf.layers.dense(x,n_hidden_1) #????
    layer_2=tf.layers.dense(layer_1,n_hidden_2) #????
    out_layer=tf.layers.dense(layer_2,num_classes) #????????
    return out_layer


def model_fn(features, labels, mode):
    logits = neural_net(features)  # ??
    # ??
    pred_classes = tf.argmax(logits, axis=1)
    pred_probas = tf.nn.softmax(logits)
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)
    # ?????????
    loss_op = tf.reduce_mean(
        tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.cast(labels, dtype=tf.int32)))
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)  # ???????
    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())  # ?????
    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)  # ??
    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

model = tf.estimator.Estimator(model_fn)
input_fn = tf.estimator.inputs.numpy_input_fn(x={'images': mnist.train.images}, y=mnist.train.labels,
                                              batch_size=batch_size, num_epochs=None, shuffle=True)
model.train(input_fn, steps=num_steps)
input_fn = tf.estimator.inputs.numpy_input_fn(x={'images': mnist.test.images}, y=mnist.test.labels,
                                              batch_size=batch_size, shuffle=False)
e = model.evaluate(input_fn)

print("准确率", e['accuracy'])

```