Problems running in the tensorflow GPU version

[west410]

Here's my code ：
from sklearn.preprocessing import LabelBinarizer
n_class = 10 #总共10类
lb = LabelBinarizer().fit(np.array(range(n_class)))
y_train = lb.transform(y_train)
y_test = lb.transform(y_test)

from sklearn.model_selection import train_test_split

train_ratio = 0.8
x_train_, x_val, y_train_, y_val = train_test_split(x_train,
y_train,
train_size=train_ratio,
random_state=123)
img_shape = x_train.shape
keep_prob = 0.6
epochs=5
batch_size=64
inputs_ = tf.placeholder(tf.float32, [None, 32, 32, 3], name='inputs_')
targets_ = tf.placeholder(tf.float32, [None, n_class], name='targets_')

This is the warning of running this code :
D:\Program Files\Anaconda3\lib\site-packages\sklearn\model_selection_split.py:2010: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.
FutureWarning)

第一层卷积加池化

32 x 32 x 3 to 32 x 32 x 64

conv1 = tf.layers.conv2d(inputs_, 64, (2,2), padding='same', activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))

32 x 32 x 64 to 16 x 16 x 64

conv1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')

第二层卷积加池化

16 x 16 x 64 to 16 x 16 x 128

conv2 = tf.layers.conv2d(conv1, 128, (4,4), padding='same', activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))

16 x 16 x 128 to 8 x 8 x 128

conv2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')

重塑输出

shape = np.prod(conv2.get_shape().as_list()[1:])
conv2 = tf.reshape(conv2,[-1, shape])

第一层全连接层

8 x 8 x 128 to 1 x 1024

fc1 = tf.contrib.layers.fully_connected(conv2, 1024, activation_fn=tf.nn.relu)
fc1 = tf.nn.dropout(fc1, keep_prob)

第二层全连接层

1 x 1024 to 1 x 512

fc2 = tf.contrib.layers.fully_connected(fc1, 512, activation_fn=tf.nn.relu)

logits层

1 x 512 to 1 x 10

logits_ = tf.contrib.layers.fully_connected(fc2, 10, activation_fn=None)
logits_ = tf.identity(logits_, name='logits_')

cost & optimizer

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits_, labels=targets_))
optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)

accuracy

correct_pred = tf.equal(tf.argmax(logits_, 1), tf.argmax(targets_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
import time
save_model_path='./test_cifar'
count = 0
start = time.time()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(epochs):
for batch_i in range(img_shape[0]//batch_size-1):
feature_batch = x_train_[batch_i * batch_size: (batch_i+1)*batch_size]
label_batch = y_train_[batch_i * batch_size: (batch_i+1)*batch_size]
train_loss, _ = sess.run([cost, optimizer],
feed_dict={inputs_: feature_batch,
targets_: label_batch})

        val_acc = sess.run(accuracy,
                           feed_dict={inputs_: x_val,
                                      targets_: y_val})
         
        if(count%100==0):
            print('Epoch {:>2}, Train Loss {:.4f}, Validation Accuracy {:4f} '.format(epoch + 1, train_loss, val_acc))
        count += 1

end = time.time()
elapsed = end - start
print ("Time taken: ", elapsed, "seconds.")

This is the error of running this code :
ResourceExhaustedError Traceback (most recent call last)
D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1322 try:
-> 1323 return fn(*args)
1324 except errors.OpError as e:

D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1301 feed_dict, fetch_list, target_list,
-> 1302 status, run_metadata)
1303

D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in exit(self, type_arg, value_arg, traceback_arg)
472 compat.as_text(c_api.TF_Message(self.status.status)),
--> 473 c_api.TF_GetCode(self.status.status))
474 # Delete the underlying status object from memory otherwise it stays alive

ResourceExhaustedError: OOM when allocating tensor with shape[10000,32,32,64]
[[Node: conv2d_13/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](_arg_inputs__2_0_0/_15, conv2d_12/kernel/read)]]
[[Node: accuracy_6/_17 = _Recvclient_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_81_accuracy_6", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

During handling of the above exception, another exception occurred:

ResourceExhaustedError Traceback (most recent call last)
in ()
54 val_acc = sess.run(accuracy,
55 feed_dict={inputs_: x_val,
---> 56 targets_: y_val})
57
58 if(count%100==0):

D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
887 try:
888 result = self._run(None, fetches, feed_dict, options_ptr,
--> 889 run_metadata_ptr)
890 if run_metadata:
891 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1118 if final_fetches or final_targets or (handle and feed_dict_tensor):
1119 results = self._do_run(handle, final_targets, final_fetches,
-> 1120 feed_dict_tensor, options, run_metadata)
1121 else:
1122 results = []

D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1315 if handle is None:
1316 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1317 options, run_metadata)
1318 else:
1319 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)

D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1334 except KeyError:
1335 pass
-> 1336 raise type(e)(node_def, op, message)
1337
1338 def _extend_graph(self):

ResourceExhaustedError: OOM when allocating tensor with shape[10000,32,32,64]
[[Node: conv2d_13/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](_arg_inputs__2_0_0/_15, conv2d_12/kernel/read)]]
[[Node: accuracy_6/_17 = _Recvclient_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_81_accuracy_6", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

Caused by op 'conv2d_13/Conv2D', defined at:
File "D:\Program Files\Anaconda3\lib\runpy.py", line 184, in _run_module_as_main
"main", mod_spec)
File "D:\Program Files\Anaconda3\lib\runpy.py", line 85, in run_code
exec(code, run_globals)
File "D:\Program Files\Anaconda3\lib\site-packages\ipykernel_main.py", line 3, in
app.launch_new_instance()
File "D:\Program Files\Anaconda3\lib\site-packages\traitlets\config\application.py", line 653, in launch_instance
app.start()
File "D:\Program Files\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "D:\Program Files\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 162, in start
super(ZMQIOLoop, self).start()
File "D:\Program Files\Anaconda3\lib\site-packages\tornado\ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "D:\Program Files\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "D:\Program Files\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "D:\Program Files\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "D:\Program Files\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "D:\Program Files\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "D:\Program Files\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "D:\Program Files\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "D:\Program Files\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "D:\Program Files\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "D:\Program Files\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "D:\Program Files\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "D:\Program Files\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "D:\Program Files\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 4, in
kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\layers\convolutional.py", line 608, in conv2d
return layer.apply(inputs)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py", line 671, in apply
return self.call(inputs, *args, **kwargs)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py", line 575, in call
outputs = self.call(inputs, *args, **kwargs)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\layers\convolutional.py", line 167, in call
outputs = self._convolution_op(inputs, self.kernel)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 835, in call
return self.conv_op(inp, filter)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 499, in call
return self.call(inp, filter)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 187, in call
name=self.name)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 630, in conv2d
data_format=data_format, name=name)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2956, in create_op
op_def=op_def)
File "D:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1470, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[10000,32,32,64]
[[Node: conv2d_13/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](_arg_inputs__2_0_0/_15, conv2d_12/kernel/read)]]
[[Node: accuracy_6/_17 = _Recvclient_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_81_accuracy_6", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

how should I correct it .
thanks for you help!

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