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私は1台のマシンの2つのgpuをすべて使用できる自分のmnistの例を書こうとしています。テンソルフローmulti-gpu mnistの例、損失は減少しません
これは単純な多層パーセプトロンです。
ここに私のコードです。直接実行することができます。
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
import tensorflow as tf
learning_rate = 0.001
training_steps = 100000
batch_size = 100
display_step = 100
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10
def _variable_on_cpu(name, shape, initializer):
with tf.device('/cpu:0'):
dtype = tf.float32
var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
return var
def build_model():
def multilayer_perceptron(x, weights, biases):
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
with tf.variable_scope('aaa'):
weights = {
'h1': _variable_on_cpu('h1',[n_input, n_hidden_1],tf.constant_initializer(0.0)),
'h2': _variable_on_cpu('h2',[n_hidden_1, n_hidden_2],tf.constant_initializer(0.0)),
'out': _variable_on_cpu('out_w',[n_hidden_2, n_classes],tf.constant_initializer(0.0))
}
biases = {
'b1': _variable_on_cpu('b1',[n_hidden_1],tf.constant_initializer(0.0)),
'b2': _variable_on_cpu('b2',[n_hidden_2],tf.constant_initializer(0.0)),
'out': _variable_on_cpu('out_b',[n_classes],tf.constant_initializer(0.0))
}
pred = multilayer_perceptron(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
return cost
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(axis=0, values=grads)
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
with tf.Graph().as_default(), tf.device('/cpu:0'):
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
tower_grads = []
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
with tf.variable_scope(tf.get_variable_scope()):
for i in xrange(2):
with tf.device('/gpu:%d' % i):
cost = build_model()
tf.get_variable_scope().reuse_variables()
grads = optimizer.compute_gradients(cost)
tower_grads.append(grads)
grads = average_gradients(tower_grads)
apply_gradient_op = optimizer.apply_gradients(grads)
train_op = apply_gradient_op
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for step in range(training_steps):
image_batch, label_batch = mnist.train.next_batch(batch_size)
_, cost_print = sess.run([train_op, cost],
{x:image_batch,
y:label_batch})
if step % display_step == 0:
print("step=%04d" % (step+1)+ " cost=" + str(cost_print))
print("Optimization Finished!")
sess.close()
印刷情報は、次のようになります。
step=0001 cost=2.30258
step=0101 cost=2.30246
step=0201 cost=2.30128
step=0301 cost=2.30376
step=0401 cost=2.29817
step=0501 cost=2.2992
step=0601 cost=2.3104
step=0701 cost=2.29995
step=0801 cost=2.29802
step=0901 cost=2.30524
step=1001 cost=2.29673
step=1101 cost=2.30016
step=1201 cost=2.31057
step=1301 cost=2.29815
step=1401 cost=2.29669
step=1501 cost=2.30345
step=1601 cost=2.29811
step=1701 cost=2.30867
step=1801 cost=2.30757
step=1901 cost=2.29716
step=2001 cost=2.30394
損失が減少しません。私はそれを修正する方法を知らない。
ところで、GPU-Utilは約26％と26％です。どのようにGPU-Utilを増やすのですか？