我正在按照链接的指导，使用自己的数据集实现cDCGAN。我的数据集包含近391510张图片，图片大小为64，而此链接中使用的MNIST数据集大小为28。我的数据集有2350个标签，而MNIST数据集只有10个。
我的数据集以.tfrecords格式存储，因此我使用get_image()函数从中检索图像和标签批次，如下所示。运行代码时，我遇到了以下错误。
当我搜索这个错误时，发现如果批处理大小很大，就会出现这种情况，所以我将批处理大小更改为32，然后出现了这个新错误。
我的数据集以.tfrecords格式存储，因此我使用get_image()函数从中检索图像和标签批次，如下所示。运行代码时，我遇到了以下错误。
`tensorflow.python.framework.errors_impl.InternalError: Dst tensor is not initialized.
[[Node: _arg_Placeholder_3_0_3/_43 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_2488__arg_Placeholder_3_0_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[Node: discriminator_1/batch_normalization/AssignMovingAvg_1/_86 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_2364_discriminator_1/batch_normalization/AssignMovingAvg_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]`
当我搜索这个错误时，发现如果批处理大小很大，就会出现这种情况，所以我将批处理大小更改为32，然后出现了这个新错误。
` tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[32,64,64,2351] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: discriminator/concat = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](_arg_Placeholder_0_0/_41, _arg_Placeholder_3_0_3/_43, discriminator/concat/axis)]]
Caused by op 'discriminator/concat', defined at:
File "cdcgan.py", line 221, in <module>
D_real, D_real_logits = discriminator(x, y_fill, isTrain)
File "cdcgan.py", line 48, in discriminator
cat1 = tf.concat([x, y_fill], 3)
`
我更改默认代码的代码部分如下：IMAGE_WIDTH = 64
IMAGE_HEIGHT = 64
# G(z)
def generator(x, y_label, isTrain=True, reuse=False):
with tf.variable_scope('generator', reuse=reuse):
# initializer
w_init = tf.truncated_normal_initializer(mean=0.0, stddev=0.02)
b_init = tf.constant_initializer(0.0)
# concat layer
cat1 = tf.concat([x, y_label], 3)
# 1st hidden layer
deconv1 = tf.layers.conv2d_transpose(cat1, 256, [16, 16], strides=(1, 1), padding='valid', kernel_initializer=w_init, bias_initializer=b_init)
lrelu1 = lrelu(tf.layers.batch_normalization(deconv1, training=isTrain), 0.2)
# 2nd hidden layer
deconv2 = tf.layers.conv2d_transpose(lrelu1, 128, [5, 5], strides=(2, 2), padding='same', kernel_initializer=w_init, bias_initializer=b_init)
lrelu2 = lrelu(tf.layers.batch_normalization(deconv2, training=isTrain), 0.2)
# output layer
deconv3 = tf.layers.conv2d_transpose(lrelu2, 1, [5, 5], strides=(2, 2), padding='same', kernel_initializer=w_init, bias_initializer=b_init)
o = tf.nn.tanh(deconv3)
return o
# D(x)
def discriminator(x, y_fill, isTrain=True, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
# initializer
w_init = tf.truncated_normal_initializer(mean=0.0, stddev=0.02)
b_init = tf.constant_initializer(0.0)
# concat layer
cat1 = tf.concat([x, y_fill], 3)
# 1st hidden layer
conv1 = tf.layers.conv2d(cat1, 128, [5, 5], strides=(2, 2), padding='same', kernel_initializer=w_init, bias_initializer=b_init)
lrelu1 = lrelu(conv1, 0.2)
# 2nd hidden layer
conv2 = tf.layers.conv2d(lrelu1, 256, [5, 5], strides=(2, 2), padding='same', kernel_initializer=w_init, bias_initializer=b_init)
lrelu2 = lrelu(tf.layers.batch_normalization(conv2, training=isTrain), 0.2)
# output layer
conv3 = tf.layers.conv2d(lrelu2, 1, [16, 16], strides=(1, 1), padding='valid', kernel_initializer=w_init)
o = tf.nn.sigmoid(conv3)
return o, conv3
def get_image(files, num_classes):
"""This method defines the retrieval image examples from TFRecords files.
Here we will define how the images will be represented (grayscale,
flattened, floating point arrays) and how labels will be represented
(one-hot vectors).
"""
# Convert filenames to a queue for an input pipeline.
file_queue = tf.train.string_input_producer(files)
# Create object to read TFRecords.
reader = tf.TFRecordReader()
# Read the full set of features for a single example.
key, example = reader.read(file_queue)
# Parse the example to get a dict mapping feature keys to tensors.
# image/class/label: integer denoting the index in a classification layer.
# image/encoded: string containing JPEG encoded image
features = tf.parse_single_example(
example,
features={
'image/class/label': tf.FixedLenFeature([], tf.int64),
'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
default_value='')
})
label = features['image/class/label']
image_encoded = features['image/encoded']
# Decode the JPEG.
image = tf.image.decode_jpeg(image_encoded, channels=1)
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.reshape(image, [IMAGE_WIDTH*IMAGE_HEIGHT])
# Represent the label as a one hot vector.
label = tf.stack(tf.one_hot(label, num_classes))
return label, image
# training parameters
batch_size = 32
# lr = 0.0002
train_epoch = 30
global_step = tf.Variable(0, trainable=False)
lr = tf.train.exponential_decay(0.0002, global_step, 500, 0.95, staircase=True)
# load MNIST
#mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=[])
SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
# Default paths.
DEFAULT_LABEL_FILE = os.path.join(SCRIPT_PATH, './labels.txt')
DEFAULT_TFRECORDS_DIR = os.path.join(SCRIPT_PATH, 'tfrecords-output')
MODEL_NAME = 'hangul_tensorflow'
IMAGE_WIDTH = 64
IMAGE_HEIGHT = 64
DEFAULT_NUM_TRAIN_STEPS = 117453 # (for 30 epochs as my training set is 391510)
"""Perform graph definition and model training.
Here we will first create our input pipeline for reading in TFRecords
files and producing random batches of images and labels.
"""
labels = io.open(DEFAULT_LABEL_FILE, 'r', encoding='utf-8').read().splitlines()
num_classes = len(labels)
print('Processing data...')
tf_record_pattern = os.path.join(DEFAULT_TFRECORDS_DIR, '%s-*' % 'train')
train_data_files = tf.gfile.Glob(tf_record_pattern)
label, image = get_image(train_data_files, num_classes)
# Associate objects with a randomly selected batch of labels and images.
image_batch, label_batch = tf.train.shuffle_batch(
[image, label], batch_size=batch_size,
capacity=2000,
min_after_dequeue=1000)
# variables : input
x = tf.placeholder(tf.float32, shape=(None, img_size, img_size, 1))
z = tf.placeholder(tf.float32, shape=(None, 1, 1, 100))
y_label = tf.placeholder(tf.float32, shape=(None, 1, 1, 2350))
y_fill = tf.placeholder(tf.float32, shape=(None, img_size, img_size, 2350))
isTrain = tf.placeholder(dtype=tf.bool)
# Initialize the queue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# training-loop
print('training start!')
for epoch in range(train_epoch):
G_losses = []
D_losses = []
for iter in range(117453): #steps for 1 epoch
# update discriminator
train_images, train_labels = sess.run([image_batch, label_batch])
x_ = train_images.reshape(-1, img_size, img_size, 1)
y_label_ = train_labels.reshape([batch_size, 1, 1, 2350])
y_fill_ = y_label_ * np.ones([batch_size, img_size, img_size, 2350])
z_ = np.random.normal(0, 1, (batch_size, 1, 1, 100))
loss_d_, _ = sess.run([D_loss, D_optim], {x: x_, z: z_, y_fill: y_fill_, y_label: y_label_, isTrain: True})
# update generator
z_ = np.random.normal(0, 1, (batch_size, 1, 1, 100))
y_ = np.random.randint(0, 9, (batch_size, 1))
y_label_ = onehot[y_.astype(np.int32)].reshape([batch_size, 1, 1, 2350])
y_fill_ = y_label_ * np.ones([batch_size, img_size, img_size, 2350])
loss_g_, _ = sess.run([G_loss, G_optim], {z: z_, x: x_, y_fill: y_fill_, y_label: y_label_, isTrain: True})
这是我的系统配置
名称: GeForce GTX 1070 主要版本: 6 次要版本: 1 内存时钟速率(GHz): 1.645 PCI总线ID: 0000:01:00.0 总内存: 8.00GiB 可用内存: 6.62GiB
我该如何解决问题？