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1.环境配置

参考文献：https://www.geek-share.com/detail/2758173524.html

参考文献：https://www.cnblogs.com/Jay-CFD/p/6114852.html

参考文献：https://blog.csdn.net/qq_25792799/article/details/80500292

2.基于Tensorflow在Minist数据集上的实现（非多GPU版本）：

以下代码亲测可用。

[code]import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

mnist=input_data.read_data_sets("/tmp/mnist/",one_hot=True)

num_gpus=2
num_steps=200
learning_rate=0.001
batch_size=1024
display_step=10

num_input=784
num_classes=10
def conv_net(x,is_training):
# "updates_collections": None is very import ,without will only get 0.10
batch_norm_params = {"is_training": is_training, "decay": 0.9, "updates_collections": None}
#,'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ]
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01),
weights_regularizer=slim.l2_regularizer(0.0005),
normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params):
with tf.variable_scope("ConvNet",reuse=tf.AUTO_REUSE):
x = tf.reshape(x, [-1, 28, 28, 1])
net = slim.conv2d(x, 6, [5,5], scope="conv_1")
net = slim.max_pool2d(net, [2, 2],scope="pool_1")
net = slim.conv2d(net, 12, [5,5], scope="conv_2")
net = slim.max_pool2d(net, [2, 2], scope="pool_2")
net = slim.flatten(net, scope="flatten")
net = slim.fully_connected(net, 100, scope="fc")
net = slim.dropout(net,is_training=is_training)
net = slim.fully_connected(net, num_classes, scope="prob", activation_fn=None,normalizer_fn=None)
return net
def train_single():
X = tf.placeholder(tf.float32, [None, num_input])
Y = tf.placeholder(tf.float32, [None, num_classes])
logits=conv_net(X,True)
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y,logits=logits))
opt=tf.train.AdamOptimizer(learning_rate)
train_op=opt.minimize(loss)
logits_test=conv_net(X,False)
correct_prediction = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(1,num_steps+1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(train_op,feed_dict={X:batch_x,Y:batch_y})
if step%display_step==0 or step==1:
loss_value,acc=sess.run([loss,accuracy],feed_dict={X:batch_x,Y:batch_y})
print("Step:" + str(step) + ":" + str(loss_value) + " " + str(acc))
print("Done")
print("Testing Accuracy:",np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i + batch_size],
Y: mnist.test.labels[i:i + batch_size]}) for i in
range(0, len(mnist.test.images), batch_size)]))

if __name__ == "__main__":
train_single()

#此代码引用自：https://blog.csdn.net/minstyrain/article/details/80986397

3.基于Tensorflow在Minist数据集上的实现（多GPU版本）：

[code]import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow.contrib.slim as slim
import horovod.tensorflow as hvd
# from keras.datasets import mnist
mnist = input_data.read_data_sets("../../dataset/mnist/", one_hot=True)

num_gpus = 2
num_steps = 20000
learning_rate = 0.001
batch_size = 1024
display_step = 10

num_input = 784
num_classes = 10

def conv_net(x, is_training):
# "updates_collections": None is very import ,without will only get 0.10
batch_norm_params = {"is_training": is_training, "decay": 0.9, "updates_collections": None}
# ,'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ]
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01),
weights_regularizer=slim.l2_regularizer(0.0005),
normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params):
with tf.variable_scope("ConvNet", reuse=tf.AUTO_REUSE):
x = tf.reshape(x, [-1, 28, 28, 1])
net = slim.conv2d(x, 6, [5, 5], scope="conv_1")
net = slim.max_pool2d(net, [2, 2], scope="pool_1")
net = slim.conv2d(net, 12, [5, 5], scope="conv_2")
net = slim.max_pool2d(net, [2, 2], scope="pool_2")
net = slim.flatten(net, scope="flatten")
net = slim.fully_connected(net, 100, scope="fc")
net = slim.dropout(net, is_training=is_training)
net = slim.fully_connected(net, num_classes, scope="prob", activation_fn=None, normalizer_fn=None)
return net

#499.36237692832947
#326.96640610694885
def train_single():
hvd.init()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = str(hvd.local_rank())

X = tf.placeholder(tf.float32, [None, num_input])
Y = tf.placeholder(tf.float32, [None, num_classes])
logits = conv_net(X, True)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=logits))
opt = tf.train.AdamOptimizer(learning_rate)
opt = hvd.DistributedOptimizer(opt)
hooks = [
# Horovod: BroadcastGlobalVariablesHook broadcasts initial variable states
# from rank 0 to all other processes. This is necessary to ensure consistent
# initialization of all workers when training is started with random weights
# or restored from a checkpoint.
hvd.BroadcastGlobalVariablesHook(0),

# Horovod: adjust number of steps based on number of GPUs.
# tf.train.StopAtStepHook(last_step=200 // hvd.size()),

# tf.train.LoggingTensorHook(tensors={'step': global_step, 'loss': loss},
#                            every_n_iter=10),
]

train_op = opt.minimize(loss)
logits_test = conv_net(X, False)
correct_prediction = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
import time
sttime = time.time()
with tf.train.MonitoredTrainingSession(
config=config,
hooks=hooks) as mon_sess:
import time
time.localtime(1)
print(mon_sess.should_stop())
# while not mon_sess.should_stop():
# Perform synchronous training.
for step in range(1,num_steps+1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
mon_sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
# if step % display_step == 0 or step == 1:
if step % display_step == 0 or step == 1:
loss_value, acc = mon_sess.run([loss, accuracy], feed_dict={X: batch_x, Y: batch_y})
print("Step:" + str(step) + ":" + str(loss_value) + " " + str(acc))
if hvd.rank() == 0:
print("Done")
print("Testing Accuracy:", np.mean([mon_sess.run(accuracy, feed_dict={X: mnist.test.images[i:i + batch_size],
Y: mnist.test.labels[i:i + batch_size]})
for i in
range(0, len(mnist.test.images), batch_size)]))
endtime=time.time()
print(endtime-sttime)
mon_sess.should_stop()

if __name__ == "__main__":
train_single()