我发布了一个关于自动编码器（AutoEncoder）的问题。
我安装了以下程序，但现在，当我输入160个水平像素乘120像素的图像时，出现“ResourceExhaustedError”，我无法继续学习。 具体而言，错误发生在第130行。 另一方面，如果你将分辨率设置为80垂直60像素的宽度的一半，那么EPOC似乎在进步并且学习进展。 （它将图像除以程序2并使其变小。）
我认为图像尺寸（宽度160 x 120像素）和张数（约700张）并不是特别大，但为什么不能教导为什么会出现错误以及如何解决？ 考虑到主存储器不足可能受到影响的可能性，我制作了128 GB的内存，但同样的错误发生了。
请帮帮我。 谢谢。
环境描述如下。
CPU：Xeon E5-1620v4 4core / 8t
主板：华硕X99-E WS
内存：DDR4-2400 64 GB（8G×8）
GPU：NVIDIA Quadro GP100×2 16GB
操作系统：ubuntu 16.04 LTS
这是源代码
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
import cv2
import os
DATASET_PATH = "/home/densos/workspaces/autoencoder"
DIR_PATH = "input_gray_160*120"
IMAGE_PATH = os.path.join(DATASET_PATH, DIR_PATH)
X_PIXEL, Y_PIXEL = 160, 120
M = 1
N_HIDDENS = np.array(np.array([1.5]) * X_PIXEL * Y_PIXEL // (M*M), dtype = np.int)
TRANCE_FRAME_NUM = 700
ops.reset_default_graph()
def xavier_init(fan_in, fan_out, constant = 1):
low = -constant * np.sqrt(6.0 / (fan_in + fan_out))
high = constant * np.sqrt(6.0 / (fan_in + fan_out))
return tf.random_uniform((fan_in, fan_out), minval = low, maxval = high, dtype = tf.float32)
class AdditiveGaussianNoiseAutoencoder(object):
def __init__(self, n_input, n_hidden, transfer_function = tf.nn.sigmoid, optimizer = tf.train.AdamOptimizer(), scale = 0.1):
self.n_input = n_input
self.n_hidden = n_hidden
self.transfer = transfer_function
self.scale = tf.placeholder(tf.float32)
self.training_scale = scale
network_weights = self._initialize_weights()
self.weights = network_weights
self.sparsity_level = np.repeat([0.05], self.n_hidden).astype(np.float32)
self.sparse_reg = 0.1
# model
self.x = tf.placeholder(tf.float32, [None, self.n_input])
self.hidden = self.transfer(tf.add(tf.matmul(self.x + scale * tf.random_normal((n_input,)),
self.weights['w1']),
self.weights['b1']))
self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])
# cost
self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0)) + self.sparse_reg \
* self.kl_divergence(self.sparsity_level, self.hidden)
self.optimizer = optimizer.minimize(self.cost)
init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init)
def _initialize_weights(self):
all_weights = dict()
all_weights['w1'] = tf.Variable(xavier_init(self.n_input, self.n_hidden))
all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden], dtype = tf.float32))
all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden, self.n_input], dtype = tf.float32))
all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype = tf.float32))
return all_weights
def partial_fit(self, X):
cost, opt = self.sess.run((self.cost, self.optimizer), feed_dict = {self.x: X,
self.scale: self.training_scale
})
return cost
def kl_divergence(self, p, p_hat):
return tf.reduce_mean(p * tf.log(p) - p * tf.log(p_hat) + (1 - p) * tf.log(1 - p) - (1 - p) * tf.log(1 - p_hat))
def calc_total_cost(self, X):
return self.sess.run(self.cost, feed_dict = {self.x: X,
self.scale: self.training_scale
})
def transform(self, X):
return self.sess.run(self.hidden, feed_dict = {self.x: X,
self.scale: self.training_scale
})
def generate(self, hidden = None):
if hidden is None:
hidden = np.random.normal(size = self.weights["b1"])
return self.sess.run(self.reconstruction, feed_dict = {self.hidden: hidden})
def reconstruct(self, X):
return self.sess.run(self.reconstruction, feed_dict = {self.x: X,
self.scale: self.training_scale
})
def getWeights(self):
return self.sess.run(self.weights['w1'])
def getBiases(self):
return self.sess.run(self.weights['b1'])
def get_random_block_from_data(data, batch_size):
start_index = np.random.randint(0, len(data) - batch_size)
return data[start_index:(start_index + batch_size)]
if __name__ == '__main__':
#get input data lists
lists = []
for file in os.listdir(IMAGE_PATH):
if file.endswith(".jpeg"):
lists.append(file)
lists.sort()
#read input data
input_images = []
for image in lists:
tmp = cv2.imread(os.path.join(IMAGE_PATH, image), cv2.IMREAD_GRAYSCALE)
tmp = cv2.resize(tmp, (X_PIXEL // M, Y_PIXEL // M))
tmp = tmp.reshape(tmp.shape[0] * tmp.shape[1])
input_images.append(tmp)
#preprocess images
input_images = np.array(input_images) / 255.
#convert data to float16
input_images = np.array(input_images, dtype = np.float16)
#set train and test data
X_train = input_images[:500]
X_test = input_images[500:]
n_samples = X_train.shape[0]
training_epochs = 200
batch_size = X_train.shape[0] // 4
display_step = 10
autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = X_train.shape[1],
n_hidden = N_HIDDENS[0],
transfer_function = tf.nn.relu6,
optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
scale = 0.01)
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(n_samples / batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs = get_random_block_from_data(X_train, batch_size)
# Fit training using batch data
cost = autoencoder.partial_fit(X_train)
# Compute average loss
avg_cost += cost / n_samples * batch_size
# Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch + 1), "cost=", avg_cost)
print("Finish Train")
predicted_imgs = autoencoder.reconstruct(X_test)
predicted_imgs = np.array((predicted_imgs) * 255, dtype = np.uint8)
input_imgs = np.array((X_test) * 255, dtype = np.uint8)
# plot the reconstructed images
for i in range(100):
im1 = predicted_imgs[i].reshape((Y_PIXEL//M, X_PIXEL//M))
im2 = input_imgs[i].reshape((Y_PIXEL//M, X_PIXEL//M))
img_v_union = cv2.vconcat([im1, im2])
cv2.moveWindow('result.jpg', 100, 200)
cv2.imshow('result.jpg', img_v_union)
cv2.waitKey(33)
答案 0 :(得分：0)
您的ResourceExhaustedError不是因超出主内存资源造成的。这是因为您尝试在单个GPU中分配超过16GB的可用内存。请注意，N_HIDDENS为28800，n_input为X_PIXEL * Y_PIXEL，即19200。在__init__中，这些巨大的数字分别作为_initialize_weights()和n_hidden传递给n_input。然后使用这些值初始化行all_weights['w1'] = tf.Variable(xavier_init(self.n_input, self.n_hidden))中的权重变量。创建一个大量完全连接的层，几乎肯定会超过您的GPU内存大小。运行下面的代码来估计该矩阵的大小。如果您的系统没有足够的主存储器来存储结果矩阵，它可能会因MemoryError而崩溃。
import numpy as np
# Here's a stand in vector - I'm only using it to compute batch_size.
input_images = np.random.rand(1000)
X_train = input_images[:500]
X_test = input_images[500:]
n_samples = X_train.shape[0]
training_epochs = 200
batch_size = X_train.shape[0] // 4
print(batch_size)
# Now, let's compute the number of hidden units
X_PIXEL, Y_PIXEL = 160, 120
M = 1
N_HIDDENS = np.array(np.array([1.5]) * X_PIXEL * Y_PIXEL // (M*M), dtype = np.int)
print(N_HIDDENS[0])
# Now we compute the number of input units.
input_vector_size = X_PIXEL * Y_PIXEL
print(input_vector_size)
# Finally, we make an approximate replica of your first weight matrix.
# Note: THis is huge, and is why you're getting an out of memory error.
your_batch = np.zeros((N_HIDDENS[0], input_vector_size, batch_size), dtype=float)
# If this didn't exceed you main memory allocation, this will print it's size.
print(your_batch.nbytes/1000000000)
您可以看到，在宽度或高度上缩小图像大小将平方减少完全连接的图层权重矩阵的内存占用量。这就是降低图像高度和宽度的原因。请注意，减少批量大小可能无济于事！这样做不会改变完全连接层的大小。因此，您应该考虑卷积而不是完全连接的方法。
希望您觉得这个解释很有帮助。