如何将张量对象保存到numpy数组？

问题描述 投票：0回答：2

我已经在我的自定义图像数据上实现了自动编码器以进行手语识别。现在我想将输出图层的张量对象保存到numpy数组中。我尝试了Session.run（张量）和tensor.eval（）。这是我的代码。

#define model
x= tf.placeholder(tf.float32,[None,784])
y_=tf.placeholder(tf.float32,[None,6])
k=190
l=180
m=150
n=130
o=100
num_of_epoch=10
w1=tf.Variable(tf.truncated_normal([784,k],stddev=0.1))
b1=tf.Variable(tf.zeros([k]))
w2=tf.Variable(tf.truncated_normal([k,l],stddev=0.1))
b2=tf.Variable(tf.zeros([l]))
w3=tf.Variable(tf.truncated_normal([l,m],stddev=0.1))
b3=tf.Variable(tf.zeros([m]))
w4=tf.Variable(tf.truncated_normal([m,n],stddev=0.1))
b4=tf.Variable(tf.zeros([n]))
w5=tf.Variable(tf.truncated_normal([n,o],stddev=0.1))
b5=tf.Variable(tf.zeros([o]))
w6=tf.Variable(tf.truncated_normal([o,6],stddev=0.1))
b6=tf.Variable(tf.zeros([6]))
y1=tf.nn.relu(tf.matmul(x,w1)+b1)
y2=tf.nn.relu(tf.matmul(y1,w2)+b2)
y3=tf.nn.relu(tf.matmul(y2,w3)+b3)
y4=tf.nn.relu(tf.matmul(y3,w4)+b4)
y5=tf.nn.relu(tf.matmul(y4,w5)+b5)
y=tf.nn.softmax(tf.matmul(y5,w6)+b6)
cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),
reduction_indices=[1]))
train_step=tf.train.GradientDescentOptimizer(0.03).minimize(cross_entropy)
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for i in range(num_of_epoch):    
        train_data = {x:x_train,y_:y_train}
        sess.run(train_step,feed_dict=train_data)
    currect_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
    accuracy=tf.reduce_mean(tf.cast(currect_prediction,tf.float32))
    sess.run(accuracy,feed_dict={x:x_train,y_:y_train})
    currect_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
    accuracy=tf.reduce_mean(tf.cast(currect_prediction,tf.float32))
    sess.run(accuracy,feed_dict=   {x:x_test,y_:y_test})
    y_p = tf.argmax(y, 1).eval() #this line shows me the error
    print(y_p)

我收到以下错误。如何修复此错误并将张量数据保存到numpy数组？

Traceback (most recent call last):
File "<ipython-input-45-5e38490a3e8e>", line 1, in <module>
runfile('C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder   
/autoencoderreconstruction.py',
wdir='C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder')
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils 
\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils
\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder
/autoencoderreconstruction.py", line 112, in <module>
y_p = tf.argmax(y, 1).eval()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\ops.py", line 606, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\ops.py", line 3928, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 789, in run
run_metadata_ptr)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
InvalidArgumentError: Shape [-1,784] has negative dimensions
[[Node: Placeholder_62 = Placeholder[dtype=DT_FLOAT, shape=[?,784], 
_device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'Placeholder_62', defined at:
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils\ipython
\start_kernel.py", line 231, in <module>
main()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils\ipython
\start_kernel.py", line 227, in main
kernel.start()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel\kernelapp.py",   
line 477, in start
ioloop.IOLoop.instance().start()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tornado\ioloop.py", line   
888, in start
handler_func(fd_obj, events)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tornad
\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tornado
\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel
\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel
\kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel
\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\RIFAT\Anaconda3\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\RIFAT\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", 
line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\IPython
\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\IPython
\core\interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\IPython
\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-45-5e38490a3e8e>", line 1, in <module>
runfile('C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder
/autoencoderreconstruction.py', wdir='C:/Users/RIFAT/PycharmProjects
/tensorflow_autoencoder')
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils
\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils
\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder
/autoencoderreconstruction.py", line 62, in <module>
x= tf.placeholder(tf.float32,[None,784])
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\ops\array_ops.py", line 1530, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\ops\gen_array_ops.py", line 1954, in _placeholder
name=name)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python  
\framework\ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\ops.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Shape [-1,784] has 
negative dimensions
[[Node: Placeholder_62 = Placeholder[dtype=DT_FLOAT, shape=[?,784], 
_device="/job:localhost/replica:0/task:0/cpu:0"]()]]
python tensorflow tensor
2个回答
3
投票

这是因为y是图中的张量而不是变量。当您在变量上运行.eval()时，它会为您提供该变量在该会话中保持的当前值，但如果您在张量上运行.eval()而不是tf.argmax(y, 1).eval()，那么张量流将图形运行到该节点以获取该值的值节点。因为在你的情况下，它在运行图形时没有得到占位符x和y_的值，它给出了错误。解决此错误的一种方法是在eval调用中传递占位符的值，如下所示：

tf.argmax(y, 1).eval(feed_dict= {x:x_test,y_:y_test})

但是，更优选的方法是将会话的上下文赋予eval调用，在这种情况下，它将返回张量的值。例如：

tf.argmax(y, 1).eval(session = sess)

0
投票

你的问题不是100％明确。但是您看到的错误是由于您尝试在没有Feed dict的情况下运行图形的事实。要查看预测的输出（即argmax（y，1）存在），您只需运行：

y_p = sess.run(tf.argmax(y, 1), feed_dict=train_data)
print(y_p)

但是，这将为您提供实际预测值（在列车数据上，因为它被馈送，以获得测试数据，只需在test_data中进行测试）。为了获得你没有argmax的概率：

y_p = sess.run(y, feed_dict=train_data)
print(y_p)
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