我试图在张量流中建立卷积神经网络。 我已经训练了它并用
保存了模型saver = tf.tain.Saver()
saver.save(sess, "model.ckpt")
然后我用
恢复了模型saver.restore(sess, "model.ckpt")
当我尝试预测一张图片的标签时，没有问题。 但是当我试图预测两个或多个图像的标签时，网络只预测第一个，然后我得到一个错误。
以下是培训网络的代码：
import tensorflow as tf
import pickle
import numpy as np
with open('X_train.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
X_train = u.load()
with open('X_test.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
X_test = u.load()
X_test = np.array(X_test).reshape(-1, 2500)
with open('y_train.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
y_train = u.load()
with open('y_test.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
y_test = u.load()
n_classes = 3
batch_size = 100
x = tf.placeholder('float', [None, 2500])
y = tf.placeholder('float')
keep_rate = 0.8
keep_prob = tf.placeholder(tf.float32)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def maxpool2d(x):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def convolutional_neural_network(x):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([13*13*64,1024])),
'out':tf.Variable(tf.random_normal([1024, n_classes]))}
biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([1024])),
'out':tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(x, shape=[-1, 50, 50, 1])
conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1'])
conv1 = maxpool2d(conv1)
conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2'])
conv2 = maxpool2d(conv2)
fc = tf.reshape(conv2,[-1, 13*13*64])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])
fc = tf.nn.dropout(fc, keep_rate)
output = tf.matmul(fc, weights['out'])+biases['out']
return output
def train_neural_network(x):
prediction = convolutional_neural_network(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits = prediction, labels = y) )
optimizer = tf.train.AdamOptimizer().minimize(cost)
saver = tf.train.Saver()
hm_epochs = 3
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(hm_epochs):
if epoch != 0:
saver.restore(sess, "model.ckpt")
epoch_loss = 0
i = 0
while i < len(X_train):
start = 1
end = i + batch_size
batch_x = np.array(X_train[start:end]).reshape(-1, 2500)
batch_y = np.array(y_train[start:end])
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
epoch_loss += c
i += batch_size
print(i)
saver.save(sess, "model.ckpt")
print('Epoch', epoch + 1, 'completed out of',hm_epochs,'loss:',epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:X_test, y:y_test}))
以下是使用网络的代码：
import tensorflow as tf
import matplotlib.pyplot as plt
import pickle
import numpy as np
with open('X_train.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
X_train = u.load()
with open('X_test.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
X_test = u.load()
X_test = np.array(X_test).reshape(-1, 2500)
with open('y_train.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
y_train = u.load()
with open('y_test.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
y_test = u.load()
n_classes = 3
batch_size = 100
x = tf.placeholder('float', [None, 2500])
y = tf.placeholder('float')
keep_rate = 0.8
keep_prob = tf.placeholder(tf.float32)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def maxpool2d(x):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def convolutional_neural_network(x):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([13*13*64,1024])),
'out':tf.Variable(tf.random_normal([1024, n_classes]))}
biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([1024])),
'out':tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(x, shape=[-1, 50, 50, 1])
conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1'])
conv1 = maxpool2d(conv1)
conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2'])
conv2 = maxpool2d(conv2)
fc = tf.reshape(conv2,[-1, 13*13*64])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])
fc = tf.nn.dropout(fc, keep_rate)
output = tf.matmul(fc, weights['out'])+biases['out']
return output
def use_neural_network(input_data):
prediction = convolutional_neural_network(x)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, "model.ckpt")
result = (sess.run(tf.argmax(prediction.eval(feed_dict={x:[input_data]}),1)))
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:X_test, y:y_test}))
return result
sample1 = X_train[432].reshape(2500)
res1 =use_neural_network(sample1)
if res == [0]: print('Go straight')
elif res == [1]: print('Turn right')
else: print('Turn left')
img = sample.reshape(50,50)
plt.imshow(img)
plt.show()
#till now there is no problem
sample2 = X_train[1222].reshape(2500)
res =use_neural_network(sample2)
#ERROR
if res2 == [0]: print('Go straight')
elif res2 == [1]: print('Turn right')
else: print('Turn left')
img2 = sample2.reshape(50,50)
plt.imshow(img2)
plt.show()
这是错误：
2017-07-24 14:22:21.277633: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_10 not found in checkpoint
2017-07-24 14:22:21.278563: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_11 not found in checkpoint
2017-07-24 14:22:21.278577: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_12 not found in checkpoint
2017-07-24 14:22:21.278995: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_9 not found in checkpoint
2017-07-24 14:22:21.279316: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_13 not found in checkpoint
2017-07-24 14:22:21.279325: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_14 not found in checkpoint
2017-07-24 14:22:21.279997: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_15 not found in checkpoint
2017-07-24 14:22:21.284003: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key Variable_8 not found in checkpoint
Traceback (most recent call last):
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1139, in _do_call
return fn(*args)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1121, in _run_fn
status, run_metadata)
File "/anaconda/lib/python3.6/contextlib.py", line 89, in __exit__
next(self.gen)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.NotFoundError: Key Variable_10 not found in checkpoint
[[Node: save_1/RestoreV2_2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save_1/Const_0_0, save_1/RestoreV2_2/tensor_names, save_1/RestoreV2_2/shape_and_slices)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "USE_NEURAL_NETWORK.py", line 103, in <module>
res =use_neural_network(sample)
File "USE_NEURAL_NETWORK.py", line 80, in use_neural_network
saver.restore(sess, "model.ckpt")
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1548, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Key Variable_10 not found in checkpoint
[[Node: save_1/RestoreV2_2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save_1/Const_0_0, save_1/RestoreV2_2/tensor_names, save_1/RestoreV2_2/shape_and_slices)]]
Caused by op 'save_1/RestoreV2_2', defined at:
File "USE_NEURAL_NETWORK.py", line 103, in <module>
res =use_neural_network(sample)
File "USE_NEURAL_NETWORK.py", line 75, in use_neural_network
saver = tf.train.Saver()
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1139, in __init__
self.build()
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1170, in build
restore_sequentially=self._restore_sequentially)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 691, in build
restore_sequentially, reshape)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 407, in _AddRestoreOps
tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 247, in restore_op
[spec.tensor.dtype])[0])
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gen_io_ops.py", line 640, in restore_v2
dtypes=dtypes, name=name)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
self._traceback = _extract_stack()
NotFoundError (see above for traceback): Key Variable_10 not found in checkpoint
[[Node: save_1/RestoreV2_2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save_1/Const_0_0, save_1/RestoreV2_2/tensor_names, save_1/RestoreV2_2/shape_and_slices)]]
如何避免此错误并预测尽可能多的图像？
答案 0 :(得分：1)
当您在prediction = convolutional_neural_network(x)中use_neural_network()时，您可以创建网络，所有变量都具有特定名称。
如果你这样做两次，你将创建2个网络，每个点都相同，但变量名称相同，第二个网络将有其他变量名称（因为2个变量不能有相同的名称）
然后，当您使用保护程序恢复模型时会出现问题，因为它会尝试查找图表中每个变量的值，但是您的检查点将只包含第一个网络中变量的值，因为它们已正确命名
Saver引发异常，因为它无法恢复所有变量。
因此，您的错误是每次要处理新图像时都要恢复模型。
您应该有两个功能，一个用于恢复，另一个用于处理。