我试图在Windows上使用TensorFlow代码运行两个Jupyter笔记本。当我运行第一台笔记本电脑时，它运行成功，但是当我尝试运行第二台笔记本电脑而没有关闭第一台笔记本电脑时，它会抛出错误。尝试运行第二台笔记本时，第一台笔记本中没有运行代码。
我通过激活Conda中的Tensorflow环境打开Jupyter笔记本，然后调用jupyter笔记本。
运行#Cell 4＃代码时出错，如果第一台笔记本电脑关闭 然后第二个笔记本运行没有任何错误，但#Cell 3＃需要再次运行才能执行出现错误的单元格（单元格4）
WARNING:tensorflow:From <ipython-input-6-0702350d4d1b>:4: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Initialized
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1021 try:
-> 1022 return fn(*args)
1023 except errors.OpError as e:
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1003 feed_dict, fetch_list, target_list,
-> 1004 status, run_metadata)
1005
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
InternalError: Blas SGEMM launch failed : a.shape=(128, 784), b.shape=(784, 1024), m=128, n=1024, k=784
[[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_Placeholder_0/_13, Variable/read)]]
[[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_55_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
InternalError Traceback (most recent call last)
<ipython-input-6-0702350d4d1b> in <module>()
15 # and the value is the numpy array to feed to it.
16 feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
---> 17 _, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict)
18 if (step % 500 == 0):
19 print("Minibatch loss at step {}: {}".format(step, l))
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
765 try:
766 result = self._run(None, fetches, feed_dict, options_ptr,
--> 767 run_metadata_ptr)
768 if run_metadata:
769 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
963 if final_fetches or final_targets:
964 results = self._do_run(handle, final_targets, final_fetches,
--> 965 feed_dict_string, options, run_metadata)
966 else:
967 results = []
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1013 if handle is None:
1014 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015 target_list, options, run_metadata)
1016 else:
1017 return self._do_call(_prun_fn, self._session, handle, feed_dict,
C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1033 except KeyError:
1034 pass
-> 1035 raise type(e)(node_def, op, message)
1036
1037 def _extend_graph(self):
InternalError: Blas SGEMM launch failed : a.shape=(128, 784), b.shape=(784, 1024), m=128, n=1024, k=784
[[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_Placeholder_0/_13, Variable/read)]]
[[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_55_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'MatMul', defined at:
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tornado\ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2683, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2787, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2847, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-5-356ef7c4c4f2>", line 21, in <module>
logits_1 = tf.matmul(tf_train_dataset, weights_1) + biases_1
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1765, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 1454, in _mat_mul
transpose_b=transpose_b, name=name)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
op_def=op_def)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 2327, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\Auro\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 1226, in __init__
self._traceback = _extract_stack()
InternalError (see above for traceback): Blas SGEMM launch failed : a.shape=(128, 784), b.shape=(784, 1024), m=128, n=1024, k=784
[[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_Placeholder_0/_13, Variable/read)]]
[[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_55_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
以下是代码：
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
#CELL 1#
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
pickle_file = 'notMNIST.pickle'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
valid_dataset = save['valid_dataset']
valid_labels = save['valid_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
del save # hint to help gc free up memory
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
image_size = 28
num_labels = 10
#CELL 2#
def reformat(dataset, labels):
dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
# Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]
labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
#CELL 3#
num_nodes= 1024
batch_size = 128
graph = tf.Graph()
with graph.as_default():
# Input data. For the training data, we use a placeholder that will be fed
# at run time with a training minibatch.
tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size * image_size))
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# Variables.
weights_1 = tf.Variable(tf.truncated_normal([image_size * image_size, num_nodes]))
biases_1 = tf.Variable(tf.zeros([num_nodes]))
weights_2 = tf.Variable(tf.truncated_normal([num_nodes, num_labels]))
biases_2 = tf.Variable(tf.zeros([num_labels]))
# Training computation.
logits_1 = tf.matmul(tf_train_dataset, weights_1) + biases_1
relu_layer= tf.nn.relu(logits_1)
logits_2 = tf.matmul(relu_layer, weights_2) + biases_2
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits_2, labels=tf_train_labels))
# Optimizer.
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
# Predictions for the training
train_prediction = tf.nn.softmax(logits_2)
# Predictions for validation
logits_1 = tf.matmul(tf_valid_dataset, weights_1) + biases_1
relu_layer= tf.nn.relu(logits_1)
logits_2 = tf.matmul(relu_layer, weights_2) + biases_2
valid_prediction = tf.nn.softmax(logits_2)
# Predictions for test
logits_1 = tf.matmul(tf_test_dataset, weights_1) + biases_1
relu_layer= tf.nn.relu(logits_1)
logits_2 = tf.matmul(relu_layer, weights_2) + biases_2
test_prediction = tf.nn.softmax(logits_2)
#CELL 4#
num_steps = 3001
with tf.Session(graph=graph) as session:
tf.initialize_all_variables().run()
print("Initialized")
for step in range(num_steps):
# Pick an offset within the training data, which has been randomized.
# Note: we could use better randomization across epochs.
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
# Generate a minibatch.
batch_data = train_dataset[offset:(offset + batch_size), :]
batch_labels = train_labels[offset:(offset + batch_size), :]
# Prepare a dictionary telling the session where to feed the minibatch.
# The key of the dictionary is the placeholder node of the graph to be fed,
# and the value is the numpy array to feed to it.
feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
_, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict)
if (step % 500 == 0):
print("Minibatch loss at step {}: {}".format(step, l))
print("Minibatch accuracy: {:.1f}".format(accuracy(predictions, batch_labels)))
print("Validation accuracy: {:.1f}".format(accuracy(valid_prediction.eval(), valid_labels)))
print("Test accuracy: {:.1f}".format(accuracy(test_prediction.eval(), test_labels)))