我有一个代码（see here）使用slice_X()（位置：keras.engine.training）修改常见的TensorBoard后端（这将导致OOM（GPU使用））以便制作在GPU使用期间使用TensorBoard。
不幸的是，由于我升级到Keras 2.0.0，这将导致导入错误，因为keras.engine.training不再包含slice_X()。
它去了哪里？可能的替代解决方案是什么？
非常感谢你的帮助。
E D I T：
我已将代码（see here）更新为Keras 2.0.0和Tensorflow r1.0。
class TensorBoard(keras.callbacks.Callback):
'''
Avoids OOM problem.
Adapted by: https://github.com/Vladimir-Yashin/keras/blob/13e6a1f99f33a3cc7bc0a44d285fda457cc808e4/keras/callbacks.py
Updated according to discussion:
http://stackoverflow.com/questions/42852495/where-did-keras-function-slice-x-go/42855104?noredirect=1#42855104
Tensorboard basic visualizations.
This callback writes a log for TensorBoard, which allows
you to visualize dynamic graphs of your training and test
metrics, as well as activation histograms for the different
layers in your model.
TensorBoard is a visualization tool provided with TensorFlow.
If you have installed TensorFlow with pip, you should be able
to launch TensorBoard from the command line:
```
tensorboard --logdir=/full_path_to_your_logs
```
You can find more information about TensorBoard
[here](https://www.tensorflow.org/versions/master/how_tos/summaries_and_tensorboard/index.html).
# Arguments
log_dir: the path of the directory where to save the log
files to be parsed by Tensorboard
histogram_freq: frequency (in epochs) at which to compute activation
histograms for the layers of the model. If set to 0,
histograms won't be computed.
write_graph: whether to visualize the graph in Tensorboard.
The log file can become quite large when
write_graph is set to True.
'''
def __init__(self, log_dir='./logs', histogram_freq=0, write_graph=True, write_images=False):
super(BatchedTensorBoard, self).__init__()
if K._BACKEND != 'tensorflow':
raise RuntimeError('TensorBoard callback only works '
'with the TensorFlow backend.')
self.log_dir = log_dir
self.histogram_freq = histogram_freq
self.merged = None
self.write_graph = write_graph
self.write_images = write_images
#print(dir(self))
def set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and self.merged is None:
for layer in self.model.layers:
for weight in layer.weights:
tf.summary.histogram(weight.name, weight)
if self.write_images:
w_img = tf.squeeze(weight)
shape = w_img.get_shape()
if len(shape) > 1 and shape[0] > shape[1]:
w_img = tf.transpose(w_img)
if len(shape) == 1:
w_img = tf.expand_dims(w_img, 0)
w_img = tf.expand_dims(tf.expand_dims(w_img, 0), -1)
tf.image_summary(weight.name, w_img)
if hasattr(layer, 'output'):
tf.summary.histogram('{}_out'.format(layer.name),
layer.output)
if parse_version(tf.__version__) >= parse_version('0.12.0'):
self.merged = tf.summary.merge_all()
else:
self.merged = tf.merge_all_summaries()
if self.write_graph:
if parse_version(tf.__version__) >= parse_version('0.12.0'):
self.writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
elif parse_version(tf.__version__) >= parse_version('0.8.0'):
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
else:
if parse_version(tf.__version__) >= parse_version('0.12.0'):
self.writer = tf.summary.FileWriter(self.log_dir)
else:
self.writer = tf.train.SummaryWriter(self.log_dir)
def on_epoch_end(self, epoch, logs={}):
import tensorflow as tf
from keras.engine.training import _slice_arrays #original: from keras.engine.training import slice_X
tf_session = K.get_session()
#result = []
if self.validation_data and self.histogram_freq:
if epoch % self.histogram_freq == 0:
if self.model.uses_learning_phase:
cut_v_data = len(self.model.inputs)
val_data = self.validation_data[:cut_v_data] + [0]
tensors = self.model.inputs + [K.learning_phase()]
else:
val_data = self.validation_data
tensors = self.model.inputs
# Sample one batch of validation data to avoid OOM on GPU
if 'batch_size' in self.params:
index_array = np.arange(len(val_data[0]))
batch_ids = np.random.choice(index_array, self.params['batch_size'])
if self.model.uses_learning_phase:
ins_batch = _slice_arrays(val_data[:-1], batch_ids) + [val_data[-1]] #original: slice_X(val_data[:-1], batch_ids) + [val_data[-1]]
else:
ins_batch = _slice_arrays(val_data, batch_ids) #original: slice_X(val_data, batch_ids)
else:
# Generators yield one batch at a time and don't provide batch_size
ins_batch = val_data
my_feed_dict = dict(zip(tensors, ins_batch))
result = tf_session.run([self.merged], feed_dict=my_feed_dict)
#result = self.sess.run([self.merged], feed_dict=my_feed_dict)
summary_str = result[0]
self.writer.add_summary(summary_str, epoch)
for name, value in logs.items():
if name in ['batch', 'size']:
continue
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value.item()
summary_value.tag = name
self.writer.add_summary(summary, epoch)
self.writer.flush()
def on_train_end(self, _):
self.writer.close()
答案 0 :(得分：4)
似乎slice_X()不再存在，但keras.engine.training：_slice_array()中有一个内部函数可以完成切片工作。请参阅code here。
如果您有其他问题，请不要犹豫。
编辑：
这是两个功能。 旧的：
def slice_X(X, start=None, stop=None):
"""This takes an array-like, or a list of
array-likes, and outputs:
- X[start:stop] if X is an array-like
- [x[start:stop] for x in X] if X in a list
Can also work on list/array of indices: `slice_X(x, indices)`
# Arguments
start: can be an integer index (start index)
or a list/array of indices
stop: integer (stop index); should be None if
`start` was a list.
"""
新的：
def _slice_arrays(arrays, start=None, stop=None):
"""Slice an array or list of arrays.
This takes an array-like, or a list of
array-likes, and outputs:
- arrays[start:stop] if `arrays` is an array-like
- [x[start:stop] for x in arrays] if `arrays` is a list
Can also work on list/array of indices: `_slice_arrays(x, indices)`
# Arguments
arrays: Single array or list of arrays.
start: can be an integer index (start index)
or a list/array of indices
stop: integer (stop index); should be None if
`start` was a list.
# Returns
A slice of the array(s).
"""
这里要理解的是，他们基本上只是更改了名称。只需使用相同的参数更改slice_X() _slice_arrays()，即可编辑弗拉基米尔的代码。同时将导入更改为
from keras.engine.training import _slice_arrays
我希望它现在正在运作。