im试图阅读并解决francois的所有“使用python进行深度学习”（第253页第7章），在那里我找到了这种装置
import keras
from keras import layers
from keras.datasets import imdb
from keras.preprocessing import sequence
max_features = 2000
max_len = 500
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
model = keras.models.Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len, name='embed'))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))
model.summary()
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
callbacks = [
keras.callbacks.TensorBoard(
log_dir='my_log_dir',
histogram_freq=1,
embeddings_freq=1,
)
]
history = model.fit(x_train, y_train,
epochs=20,
batch_size=128,
validation_split=0.2,
callbacks=callbacks)
现在，当我尝试在第一个纪元的末尾进行训练时，我会收到此错误（实际上，它是在纪元的最后一步引起了验证，我在验证过程中如此）
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
Train on 20000 samples, validate on 5000 samples
Epoch 1/20
20000/20000 [==============================] - 52s 3ms/step - loss: 0.8421 - acc: 0.5126 - val_loss: 0.6876 - val_acc: 0.5532
ValueError Traceback (most recent call last)
<ipython-input-5-1a0c8832065d> in <module>
1 callbacks = [keras.callbacks.TensorBoard(log_dir='my_log_dir', histogram_freq=1, embeddings_freq=1,)]
----> 2 history = model.fit(x_train, y_train, epochs=20, batch_size=512, validation_split=0.2, callbacks=callbacks)
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
215 for l, o in zip(out_labels, val_outs):
216 epoch_logs['val_' + l] = o
--> 217 callbacks.on_epoch_end(epoch, epoch_logs)
218 if callback_model.stop_training:
219 break
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\callbacks.py in on_epoch_end(self, epoch, logs)
77 logs = logs or {}
78 for callback in self.callbacks:
---> 79 callback.on_epoch_end(epoch, logs)
80
81 def on_batch_begin(self, batch, logs=None):
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\callbacks.py in on_epoch_end(self, epoch, logs)
913 "provided, and cannot be a generator.")
914 if self.embeddings_data is None and self.embeddings_freq:
--> 915 raise ValueError("To visualize embeddings, embeddings_data must "
916 "be provided.")
917 if self.validation_data and self.histogram_freq:
ValueError: To visualize embeddings, embeddings_data must be provided.
我不知道可能是什么错误U.U，我需要在哪里设置embeddings_data？预先谢谢你们！
编辑：
现在将回调更改为
callbacks = [keras.callbacks.TensorBoard(log_dir='my_log_dir', histogram_freq=1, embeddings_freq=1, embeddings_data=x_test)]
我收到这个新错误
WARNING:tensorflow:From C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
WARNING:tensorflow:From C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\ops\math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
Train on 17500 samples, validate on 7500 samples
Epoch 1/20
---------------------------------------------------------------------------
ResourceExhaustedError Traceback (most recent call last)
~\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1333 try:
-> 1334 return fn(*args)
1335 except errors.OpError as e:
~\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1318 return self._call_tf_sessionrun(
-> 1319 options, feed_dict, fetch_list, target_list, run_metadata)
1320
~\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1406 self._session, options, feed_dict, fetch_list, target_list,
-> 1407 run_metadata)
1408
ResourceExhaustedError: OOM when allocating tensor of shape [25000,64000] and type float
[[{{node zeros}}]]
During handling of the above exception, another exception occurred:
ResourceExhaustedError Traceback (most recent call last)
<ipython-input-2-d7e702b62530> in <module>
3 callbacks = [keras.callbacks.TensorBoard(log_dir='my_log_dir', histogram_freq=1, embeddings_freq=1, embeddings_data=x_test)]
4
----> 5 history = model.fit(x_train, y_train, epochs=20, batch_size=128, validation_split=0.3, callbacks=callbacks)
~\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
~\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
~\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
2695
2696 def __call__(self, inputs):
-> 2697 if hasattr(get_session(), '_make_callable_from_options'):
2698 if py_any(is_sparse(x) for x in self.inputs):
2699 if py_any(is_tensor(x) for x in inputs):
~\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\backend\tensorflow_backend.py in get_session()
204 v._keras_initialized = True
205 if uninitialized_vars:
--> 206 session.run(tf.variables_initializer(uninitialized_vars))
207 # hack for list_devices() function.
208 # list_devices() function is not available under tensorflow r1.3.
~\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1150 if final_fetches or final_targets or (handle and feed_dict_tensor):
1151 results = self._do_run(handle, final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata)
1153 else:
1154 results = []
~\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1326 if handle is None:
1327 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata)
1329 else:
1330 return self._do_call(_prun_fn, handle, feeds, fetches)
~\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1346 pass
1347 message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message)
1349
1350 def _extend_graph(self):
ResourceExhaustedError: OOM when allocating tensor of shape [25000,64000] and type float
[[node zeros (defined at C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\callbacks.py:880) ]]
Caused by op 'zeros', defined at:
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\ipykernel\kernelapp.py", line 505, in start
self.io_loop.start()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\platform\asyncio.py", line 148, in start
self.asyncio_loop.run_forever()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\asyncio\base_events.py", line 539, in run_forever
self._run_once()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\asyncio\base_events.py", line 1775, in _run_once
handle._run()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\asyncio\events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\ioloop.py", line 690, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\ioloop.py", line 743, in _run_callback
ret = callback()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\gen.py", line 781, in inner
self.run()
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\gen.py", line 742, in run
yielded = self.gen.send(value)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\ipykernel\kernelbase.py", line 365, in process_one
yield gen.maybe_future(dispatch(*args))
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\gen.py", line 209, in wrapper
yielded = next(result)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\ipykernel\kernelbase.py", line 272, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\gen.py", line 209, in wrapper
yielded = next(result)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\ipykernel\kernelbase.py", line 542, in execute_request
user_expressions, allow_stdin,
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tornado\gen.py", line 209, in wrapper
yielded = next(result)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\ipykernel\ipkernel.py", line 294, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\ipykernel\zmqshell.py", line 536, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\IPython\core\interactiveshell.py", line 2848, in run_cell
raw_cell, store_history, silent, shell_futures)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\IPython\core\interactiveshell.py", line 2874, in _run_cell
return runner(coro)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\IPython\core\async_helpers.py", line 67, in _pseudo_sync_runner
coro.send(None)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\IPython\core\interactiveshell.py", line 3049, in run_cell_async
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\IPython\core\interactiveshell.py", line 3214, in run_ast_nodes
if (yield from self.run_code(code, result)):
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\IPython\core\interactiveshell.py", line 3296, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-d7e702b62530>", line 5, in <module>
history = model.fit(x_train, y_train, epochs=20, batch_size=128, validation_split=0.3, callbacks=callbacks)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\engine\training.py", line 1039, in fit
validation_steps=validation_steps)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\engine\training_arrays.py", line 117, in fit_loop
callbacks.set_model(callback_model)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\callbacks.py", line 54, in set_model
callback.set_model(model)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\callbacks.py", line 880, in set_model
embedding = tf.Variable(tf.zeros(shape),
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1817, in zeros
output = fill(shape, constant(zero, dtype=dtype), name=name)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 3367, in fill
"Fill", dims=dims, value=value, name=name)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\framework\ops.py", line 3300, in create_op
op_def=op_def)
File "C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\tensorflow\python\framework\ops.py", line 1801, in __init__
self._traceback = tf_stack.extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor of shape [25000,64000] and type float
[[node zeros (defined at C:\Users\mimus\Anaconda3\envs\tensorflow_g\lib\site-packages\keras\callbacks.py:880) ]]
答案 0 :(得分：0)
生成嵌入可视化需要额外的数据，因此您应该传递一些可用于生成可视化的数据，通常这是测试集：
keras.callbacks.TensorBoard(
log_dir='my_log_dir',
histogram_freq=1,
embeddings_freq=1,
embeddings_data=x_test
)
还有一些方法可以传递元数据（例如类标签）以改善可视化效果。