本篇文章参考《Tensorflow实战Google深度学习框架》一书
import os.path import re import time import numpy as np import tensorflow as tf import cifar10 batch_size = 128 max_steps = 1000 num_gpus=1 # 具体gpu数量 def tower_loss(scope): images, labels = cifar10.distorted_inputs() logits = cifar10.inference(images) _ = cifar10.loss(logits, labels) losses = tf.get_collection('losses', scope) total_loss = tf.add_n(losses, name='total_loss') return total_loss def average_gradients(tower_grads): average_grads = [] for grad_and_vars in zip(*tower_grads): grads = [] for g, _ in grad_and_vars: expanded_g = tf.expand_dims(g, 0) grads.append(expanded_g) grad = tf.concat(grads, 0) grad = tf.reduce_mean(grad, 0) v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads def train(): with tf.Graph().as_default(), tf.device('/cpu:0'): global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False) num_batches_per_epoch = cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / batch_size decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY) lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE, global_step, decay_steps, cifar10.LEARNING_RATE_DECAY_FACTOR, staircase=True) opt = tf.train.GradientDescentOptimizer(lr) tower_grads = [] for i in range(num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope: loss = tower_loss(scope) tf.get_variable_scope().reuse_variables() grads = opt.compute_gradients(loss) tower_grads.append(grads) grads = average_gradients(tower_grads) apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) saver = tf.train.Saver(tf.all_variables()) init = tf.global_variables_initializer() sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) sess.run(init) tf.train.start_queue_runners(sess=sess) for step in range(max_steps): start_time = time.time() _, loss_value = sess.run([apply_gradient_op, loss]) duration = time.time() - start_time if step % 10 == 0: num_examples_per_step = batch_size * num_gpus examples_per_sec = num_examples_per_step / duration sec_per_batch = duration / num_gpus format_str = ('step %d, loss = %.2f (%.1f examples/sec; %.3f ' 'sec/batch)') print(format_str % (step, loss_value, examples_per_sec, sec_per_batch)) if step % 1000 == 0 or (step + 1) == max_steps: saver.save(sess, 'cifar10_train/model.ckpt', global_step=step) # 需要在当前py文件下存在cifar10_train目录 cifar10.maybe_download_and_extract() train() D:\Python\Anaconda3\python.exe C:/Users/amax/Desktop/UNet/test.py >> Downloading cifar-10-binary.tar.gz 100.0% Successfully downloaded cifar-10-binary.tar.gz 170052171 bytes. Filling queue with 20000 CIFAR images before starting to train. This will take a few minutes. step 0, loss = 4.67 (3.9 examples/sec; 33.204 sec/batch) step 10, loss = 4.60 (335.4 examples/sec; 0.382 sec/batch) step 20, loss = 4.35 (451.1 examples/sec; 0.284 sec/batch) step 30, loss = 4.28 (245.9 examples/sec; 0.520 sec/batch) step 40, loss = 4.35 (630.1 examples/sec; 0.203 sec/batch) step 50, loss = 4.30 (655.4 examples/sec; 0.195 sec/batch) step 60, loss = 4.39 (409.6 examples/sec; 0.313 sec/batch) step 70, loss = 4.19 (340.4 examples/sec; 0.376 sec/batch) step 80, loss = 4.09 (543.5 examples/sec; 0.235 sec/batch) step 90, loss = 4.45 (512.0 examples/sec; 0.250 sec/batch) step 100, loss = 4.08 (716.9 examples/sec; 0.179 sec/batch) step 110, loss = 4.09 (459.7 examples/sec; 0.278 sec/batch) step 120, loss = 4.05 (744.7 examples/sec; 0.172 sec/batch) step 130, loss = 4.09 (621.2 examples/sec; 0.206 sec/batch) step 140, loss = 3.98 (512.0 examples/sec; 0.250 sec/batch) step 150, loss = 3.96 (420.5 examples/sec; 0.304 sec/batch) step 160, loss = 3.92 (585.1 examples/sec; 0.219 sec/batch) step 170, loss = 3.82 (701.5 examples/sec; 0.182 sec/batch) step 180, loss = 3.74 (585.1 examples/sec; 0.219 sec/batch) step 190, loss = 3.96 (251.9 examples/sec; 0.508 sec/batch) step 200, loss = 3.74 (561.4 examples/sec; 0.228 sec/batch) step 210, loss = 3.91 (440.4 examples/sec; 0.291 sec/batch) step 220, loss = 3.74 (627.5 examples/sec; 0.204 sec/batch) step 230, loss = 3.94 (608.2 examples/sec; 0.210 sec/batch) step 240, loss = 3.74 (650.8 examples/sec; 0.197 sec/batch) step 250, loss = 3.65 (658.4 examples/sec; 0.194 sec/batch) step 260, loss = 3.69 (716.3 examples/sec; 0.179 sec/batch) step 270, loss = 3.73 (768.2 examples/sec; 0.167 sec/batch) step 280, loss = 3.52 (384.9 examples/sec; 0.333 sec/batch) step 290, loss = 3.69 (273.5 examples/sec; 0.468 sec/batch) step 300, loss = 3.57 (189.2 examples/sec; 0.677 sec/batch) step 310, loss = 3.54 (656.7 examples/sec; 0.195 sec/batch) step 320, loss = 3.76 (323.2 examples/sec; 0.396 sec/batch) step 330, loss = 3.46 (682.7 examples/sec; 0.188 sec/batch) step 340, loss = 3.45 (803.8 examples/sec; 0.159 sec/batch) step 350, loss = 3.48 (808.5 examples/sec; 0.158 sec/batch) step 360, loss = 3.29 (429.3 examples/sec; 0.298 sec/batch) step 370, loss = 3.46 (682.7 examples/sec; 0.188 sec/batch) step 380, loss = 3.28 (791.7 examples/sec; 0.162 sec/batch) step 390, loss = 3.38 (355.7 examples/sec; 0.360 sec/batch) step 400, loss = 3.53 (716.6 examples/sec; 0.179 sec/batch) step 410, loss = 3.18 (497.8 examples/sec; 0.257 sec/batch) step 420, loss = 3.26 (630.1 examples/sec; 0.203 sec/batch) step 430, loss = 3.23 (714.0 examples/sec; 0.179 sec/batch) step 440, loss = 3.24 (148.4 examples/sec; 0.862 sec/batch) step 450, loss = 3.23 (400.4 examples/sec; 0.320 sec/batch) step 460, loss = 3.19 (552.3 examples/sec; 0.232 sec/batch) step 470, loss = 3.26 (608.2 examples/sec; 0.210 sec/batch) step 480, loss = 3.25 (228.6 examples/sec; 0.560 sec/batch) step 490, loss = 3.22 (615.7 examples/sec; 0.208 sec/batch) step 500, loss = 3.44 (720.1 examples/sec; 0.178 sec/batch) step 510, loss = 3.06 (682.7 examples/sec; 0.188 sec/batch) step 520, loss = 3.27 (453.8 examples/sec; 0.282 sec/batch) step 530, loss = 3.09 (682.7 examples/sec; 0.188 sec/batch) step 540, loss = 3.02 (674.3 examples/sec; 0.190 sec/batch) step 550, loss = 3.17 (591.7 examples/sec; 0.216 sec/batch) step 560, loss = 3.11 (570.4 examples/sec; 0.224 sec/batch) step 570, loss = 3.17 (271.3 examples/sec; 0.472 sec/batch) step 580, loss = 3.13 (536.0 examples/sec; 0.239 sec/batch) step 590, loss = 2.98 (732.6 examples/sec; 0.175 sec/batch) step 600, loss = 3.03 (819.2 examples/sec; 0.156 sec/batch) step 610, loss = 3.01 (764.0 examples/sec; 0.168 sec/batch) step 620, loss = 3.08 (752.6 examples/sec; 0.170 sec/batch) step 630, loss = 3.09 (512.0 examples/sec; 0.250 sec/batch) step 640, loss = 3.09 (481.9 examples/sec; 0.266 sec/batch) step 650, loss = 2.96 (498.8 examples/sec; 0.257 sec/batch) step 660, loss = 2.71 (658.9 examples/sec; 0.194 sec/batch) step 670, loss = 2.85 (186.4 examples/sec; 0.687 sec/batch) step 680, loss = 2.77 (557.5 examples/sec; 0.230 sec/batch) step 690, loss = 3.06 (326.0 examples/sec; 0.393 sec/batch) step 700, loss = 2.86 (715.7 examples/sec; 0.179 sec/batch) step 710, loss = 2.77 (442.5 examples/sec; 0.289 sec/batch) step 720, loss = 2.86 (496.5 examples/sec; 0.258 sec/batch) step 730, loss = 2.85 (254.7 examples/sec; 0.503 sec/batch) step 740, loss = 2.83 (388.6 examples/sec; 0.329 sec/batch) step 750, loss = 2.73 (294.3 examples/sec; 0.435 sec/batch) step 760, loss = 2.77 (481.9 examples/sec; 0.266 sec/batch) step 770, loss = 2.59 (245.2 examples/sec; 0.522 sec/batch) step 780, loss = 2.75 (555.0 examples/sec; 0.231 sec/batch) step 790, loss = 2.73 (691.7 examples/sec; 0.185 sec/batch) step 800, loss = 2.89 (706.2 examples/sec; 0.181 sec/batch) step 810, loss = 2.76 (434.3 examples/sec; 0.295 sec/batch) step 820, loss = 2.80 (274.2 examples/sec; 0.467 sec/batch) step 830, loss = 2.93 (268.9 examples/sec; 0.476 sec/batch) step 840, loss = 2.72 (379.6 examples/sec; 0.337 sec/batch) step 850, loss = 2.53 (608.1 examples/sec; 0.210 sec/batch) step 860, loss = 2.58 (715.8 examples/sec; 0.179 sec/batch) step 870, loss = 2.53 (703.4 examples/sec; 0.182 sec/batch) step 880, loss = 2.58 (277.7 examples/sec; 0.461 sec/batch) step 890, loss = 2.77 (471.5 examples/sec; 0.272 sec/batch) step 900, loss = 2.55 (744.7 examples/sec; 0.172 sec/batch) step 910, loss = 2.60 (274.1 examples/sec; 0.467 sec/batch) step 920, loss = 2.51 (630.1 examples/sec; 0.203 sec/batch) step 930, loss = 2.53 (538.1 examples/sec; 0.238 sec/batch) step 940, loss = 2.54 (630.1 examples/sec; 0.203 sec/batch) step 950, loss = 2.45 (398.2 examples/sec; 0.321 sec/batch) step 960, loss = 2.43 (283.2 examples/sec; 0.452 sec/batch) step 970, loss = 2.60 (350.2 examples/sec; 0.365 sec/batch) step 980, loss = 2.41 (566.6 examples/sec; 0.226 sec/batch) step 990, loss = 2.55 (296.2 examples/sec; 0.432 sec/batch)