我有大约550K样本,每个样本为200x50x1。该数据集的大小约为57GB。
我想在此设备上训练网络,但无法读取它。
batch_size=8
def _read_py_function(filename,labels_slice):
with h5py.File(filename, 'r') as f:
data_slice = np.asarray(f['feats'])
print(data_slice.shape)
return data_slice, labels_slice
placeholder_files = tf.placeholder(tf.string, [None])
placeholder_labels = tf.placeholder(tf.int32, [None])
dataset = tf.data.Dataset.from_tensor_slices((placeholder_files,placeholder_labels))
dataset = dataset.map(
lambda filename, label: tuple(tf.py_func(
_read_py_function, [filename,label], [tf.uint8, tf.int32])))
dataset = dataset.shuffle(buffer_size=50000)
dataset = dataset.batch(batch_size)
iterator = tf.data.Iterator.from_structure(dataset.output_types, dataset.output_shapes)
data_X, data_y = iterator.get_next()
data_y = tf.cast(data_y, tf.int32)
net = conv_layer(inputs=data_X,num_outputs=8, kernel_size=3, stride=2, scope='rcl_0')
net = pool_layer(inputs=net,kernel_size=2,scope='pl_0')
net = dropout_layer(inputs=net,scope='dl_0')
net = flatten_layer(inputs=net,scope='flatten_0')
net = dense_layer(inputs=net,num_outputs=256,scope='dense_0')
net = dense_layer(inputs=net,num_outputs=64,scope='dense_1')
out = dense_layer(inputs=net,num_outputs=10,scope='dense_2')
然后我使用:
sess.run(train_iterator, feed_dict = {placeholder_files: filenames, placeholder_labels: ytrain})
try:
while True:
_, loss, acc = sess.run([train_op, loss_op, accuracy_op])
train_loss += loss
train_accuracy += acc
except tf.errors.OutOfRangeError:
pass
但是我什至在运行会话之前就收到了错误消息:
Traceback (most recent call last):
File "SFCC-trial-134.py", line 297, in <module>
net = rcnn_layer(inputs=data_X,num_outputs=8, kernel_size=3, stride=2, scope='rcl_0')
File "SFCC-trial-134.py", line 123, in rcnn_layer
reuse=False)
File "SFCC-trial-134.py", line 109, in conv_layer
reuse = reuse
File "/home/priyam.jain/tensorflow-gpu-python3/lib/python3.5/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 183, in func_with_args
return func(*args, **current_args)
File "/home/priyam.jain/tensorflow-gpu-python3/lib/python3.5/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1154, in convolution2d
conv_dims=2)
File "/home/priyam.jain/tensorflow-gpu-python3/lib/python3.5/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 183, in func_with_args
return func(*args, **current_args)
File "/home/priyam.jain/tensorflow-gpu-python3/lib/python3.5/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1025, in convolution
(conv_dims + 2, input_rank))
TypeError: %d format: a number is required, not NoneType
尽管我打算使用TFRecords,但是创建它们却很困难。在学习如何为我的数据集创建它们的地方找不到好的帖子。
conv_layer定义如下:
def conv_layer(inputs, num_outputs, kernel_size, stride, normalizer_fn=None, activation_fn=nn.relu, trainable=True, scope='noname', reuse=False):
net = slim.conv2d(inputs = inputs,
num_outputs = num_outputs,
kernel_size = kernel_size,
stride = stride,
normalizer_fn = normalizer_fn,
activation_fn = activation_fn,
trainable = trainable,
scope = scope,
reuse = reuse
)
return net
答案 0 :(得分:0)
请勿在地图函数内传递tf.py_func
。您可以通过直接在map
函数内部传递函数名称来读取文件图像。我只摆出了代码的相关部分。
def _read_py_function(filename, label):
return tf.zeros((224, 224, 3), dtype=tf.float32), tf.ones((1,), dtype=tf.int32)
dataset = dataset.map(lambda filename, label: _read_py_function(filename, label))
另一个变化是您的iterator
将只期望输入的浮点数。因此,您必须将tf.uint8
的输出类型更改为float
。