我正在尝试总结以下神经网络的训练过程。
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(".\MNIST",one_hot=True)
# Create the model
def train_and_test(hidden1,hidden2, learning_rate, epochs, batch_size):
with tf.name_scope("first_layer"):
input_data = tf.placeholder(tf.float32, [batch_size, 784], name = "input")
weights1 = tf.Variable(
tf.random_normal(shape =[784, hidden1],stddev=0.1),name = "weights")
bias = tf.Variable(tf.constant(0.0,shape =[hidden1]), name = "bias")
activation = tf.nn.relu(
tf.matmul(input_data, weights1) + bias, name = "relu_act")
tf.summary.histogram("first_activation", activation)
with tf.name_scope("second_layer"):
weights2 = tf.Variable(
tf.random_normal(shape =[hidden1, hidden2],stddev=0.1),
name = "weights")
bias2 = tf.Variable(tf.constant(0.0,shape =[hidden2]), name = "bias")
activation2 = tf.nn.relu(
tf.matmul(activation, weights2) + bias2, name = "relu_act")
tf.summary.histogram("second_activation", activation2)
with tf.name_scope("output_layer"):
weights3 = tf.Variable(
tf.random_normal(shape=[hidden2, 10],stddev=0.5), name = "weights")
bias3 = tf.Variable(tf.constant(1.0, shape =[10]), name = "bias")
output = tf.add(
tf.matmul(activation2, weights3, name = "mul"), bias3, name = "output")
tf.summary.histogram("output_activation", output)
y_ = tf.placeholder(tf.float32, [batch_size, 10])
with tf.name_scope("loss"):
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=output))
tf.summary.scalar("cross_entropy", cross_entropy)
with tf.name_scope("train"):
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
with tf.name_scope("tests"):
correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
summary_op = tf.summary.merge_all()
sess = tf.InteractiveSession()
writer = tf.summary.FileWriter("./data", sess.graph)
tf.global_variables_initializer().run()
# Train
for i in range(epochs):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
_, summary = sess.run([train_step,summary_op], feed_dict={input_data: batch_xs, y_: batch_ys})
writer.add_summary(summary)
if i % 10 ==0:
test_xs, test_ys = mnist.train.next_batch(batch_size)
test_accuracy = sess.run(accuracy, feed_dict = {input_data : test_xs, y_ : test_ys})
writer.close()
return test_accuracy
if __name__ =="__main__":
print(train_and_test(500, 200, 0.001, 10000, 100))
我每隔10步用随机批次的测试数据测试模型。 问题出在夏季作家身上。 for循环中的sess.run（）会抛出以下错误。
Traceback (most recent call last):
File "<ipython-input-18-78c88c8e6471>", line 1, in <module>
runfile('C:/Users/Suman
Nepal/Documents/Projects/MNISTtensorflow/mnist.py', wdir='C:/Users/Suman
Nepal/Documents/Projects/MNISTtensorflow')
File "C:\Users\Suman Nepal\Anaconda3\lib\site-
packages\spyder\utils\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-
packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 68, in <module>
print(train_and_test(500, 200, 0.001, 100, 100))
File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 58, in train_and_test
_, summary = sess.run([train_step,summary_op], feed_dict={input_data: batch_xs, y_: batch_ys})
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 789, in run
run_metadata_ptr)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "C:\Users\Suman Nepal\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: first_layer_5/input = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'first_layer_5/input', defined at:
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 231, in <module>
main()
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 227, in main
kernel.start()
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tornado\ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\Suman Nepal\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\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\Suman Nepal\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-8-78c88c8e6471>", line 1, in <module>
runfile('C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py', wdir='C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow')
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 86, in <module>
File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 12, in train_and_test
input_data = tf.placeholder(tf.float32, [None, 784], name = "input")
File "C:\Users\Suman Nepal\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\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 1954, in _placeholder
name=name)
File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Users\Suman Nepal\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\Suman Nepal\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: first_layer_5/input = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
如果我删除了所有摘要编写器和摘要，则模型运行正常。 你能帮我发现这个问题吗？我试图操纵张量的形状，但没有任何地方。
答案 0 :(得分：3)
从删除的答案的一条评论，从原始海报：
我实际上在
with tf.Graph() as g下构建了一个神经网络。我删除了交互式会话，并以with tf.Session(g) as sess开始了会话。它解决了这个问题。
图表g未被标记为默认图表，因此会话（原始代码中的tf.InteractiveSession）将使用其他图表。
请注意，由于相同的错误消息，我偶然发现了这里。在我的情况下，我偶然发生了这样的事情：
input_data = tf.placeholder(tf.float32, shape=(None, 50))
input_data = tf.tanh(input_data)
session.run(..., feed_dict={input_data: ...})
即。我没有给占位符喂食。似乎某些其他张量操作可能会导致这个令人困惑的错误，因为内部未定义的维度表示为-1。
答案 1 :(得分：1)
我也遇到了这个问题。搜索基本共识是检查代码中其他地方的问题。
为我修复的是我正在做sess.run(summary_op)而没有为我的占位符提供数据。
Tensorflow对于占位符似乎有点奇怪，如果你试图评估独立于它们的图形部分，它们通常不会介意你不喂它们。不过，确实如此。
答案 2 :(得分：0)
这可能与InteractiveSession初始化有关。
我在开始时初始化它然后它工作 - 然后在会话中初始化全局变量。
我无法使用旧代码重现错误，这使得某些地方无法预测或缓存设置。
import tensorflow as tf
sess = tf.InteractiveSession()
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W)+b)
y_ = tf.placeholder(tf.float32, [None,10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
sess.run(tf.global_variables_initializer())
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
#print batch_xs.shape, batch_ys.shape
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})