我创建了一个CNN来预测图像的标签。我训练了它。现在我想使用我的模型来预测新图像的标签。我的CNN代码是： -
def LeNet(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
# SOLUTION: Layer 1: Convolutional. Input = 32x32x3. Output = 28x28x6.
conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 3, 6), mean = mu, stddev = sigma))
conv1_b = tf.Variable(tf.zeros(6))
conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
# SOLUTION: Activation.
conv1 = tf.nn.relu(conv1)
# SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
conv2_b = tf.Variable(tf.zeros(16))
conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
# SOLUTION: Activation.
conv2 = tf.nn.relu(conv2)
# SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Flatten. Input = 5x5x16. Output = 400.
fc0 = flatten(conv2)
# SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.
fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.matmul(fc0, fc1_W) + fc1_b
# SOLUTION: Activation.
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1,0.6)
# SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.matmul(fc1, fc2_W) + fc2_b
# SOLUTION: Activation.
fc2 = tf.nn.relu(fc2)
fc2 = tf.nn.dropout(fc2,0.7)
# SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43.
fc3_W = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
fc3_b = tf.Variable(tf.zeros(43))
logits = tf.matmul(fc2, fc3_W) + fc3_b
return logits
x = tf.placeholder(tf.float32, (None, 32, 32, 3))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 43)
rate = 0.001
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=one_hot_y)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
for i in range(EPOCHS):
X_train, y_train = shuffle(X_train, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_train[offset:end]
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})
training_accuracy = evaluate(X_train,y_train)
validation_accuracy = evaluate(X_valid, y_valid)
print("EPOCH {} ...".format(i+1))
print("training Accuracy = {:.3f}".format(training_accuracy))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
saver.save(sess, './lenet')
print("Model saved")
现在我从互联网上下载了一些图片，并希望预测它们的标签。处理图像并将它们转换为numpy.ndarray的代码如下： -
from os import listdir
from PIL import Image as PImage
from matplotlib import pyplot as plt
def loadImages(path):
# return array of images
imagesList = listdir(path)
loadedImages = []
basewidth = 32
hsize = 32
for image in imagesList:
img = PImage.open(path + image)
img = img.resize((basewidth,hsize),PIL.Image.ANTIALIAS)
loadedImages.append(img)
return loadedImages
path = "C:\\Users\\che\\CarND-Traffic-Sign-Classifier-Project\\images\\"
image_in_pixels = []
for image in imgs:
image = np.array(image.getdata(),np.float32).reshape(32, 32, 3)
image_in_pixels.append(image)
print(type(image_in_pixels))
test_images = np.array(image_in_pixels)
print(test_images.shape)
print(type(test_images[0]))
现在我在这里需要使用保存的模型预测这些图像的标签。 我的相同代码如下： -
x = tf.placeholder(dtype=tf.float32,shape=(None,32,32,3))
keep_prob = tf.placeholder(dtype=tf.float32)
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
vals = sess.run(logits,feed_dict={x:test_images,keep_prob: 1.})
print (vals)
I am getting the following error.
InvalidArgumentError Traceback (most recent call last)
C:\ProgramData\Anaconda3\envs\carnd-term1\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:\ProgramData\Anaconda3\envs\carnd-term1\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:\ProgramData\Anaconda3\envs\carnd-term1\lib\contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
C:\ProgramData\Anaconda3\envs\carnd-term1\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:
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
[[Node: Placeholder_2 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-213-6e880af91901> in <module>()
4 with tf.Session() as sess:
5 saver.restore(sess, tf.train.latest_checkpoint('.'))
----> 6 vals = sess.run(logits,feed_dict={x:test_images,keep_prob: 1.})
7 print (vals)
C:\ProgramData\Anaconda3\envs\carnd-term1\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:\ProgramData\Anaconda3\envs\carnd-term1\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:\ProgramData\Anaconda3\envs\carnd-term1\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:\ProgramData\Anaconda3\envs\carnd-term1\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):
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
[[Node: Placeholder_2 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'Placeholder_2', defined at:
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\ipykernel\__main__.py", line 3, in <module>
app.launch_new_instance()
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\ipykernel\kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\tornado\ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\ipykernel\kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\ipykernel\kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\ipykernel\kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\ipykernel\zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\IPython\core\interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "C:\ProgramData\Anaconda3\envs\carnd-term1\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-17-55707f3825d1>", line 1, in <module>
x = tf.placeholder(tf.float32, (None, 32, 32, 3))
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1502, in placeholder
name=name)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 2149, in _placeholder
name=name)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\carnd-term1\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:\ProgramData\Anaconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\framework\ops.py", line 1226, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
[[Node: Placeholder_2 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
我对feed_dict的数据类型只是浮点数。从过去3天开始，我一直在努力调试，但没有取得任何成功。非常感谢您的帮助。
答案 0 :(得分：0)
如果在keep_prob中定义形状
，该怎么办？keep_prob = tf.placeholder(dtype=tf.float32, shape=(1))
答案 1 :(得分：0)
你得到ValueError。根据tensorflow文档，您会收到此错误“如果fetches或feed_dict键无效或引用不存在的Tensor”（请参阅Tensorflow Session Documentation）。
在Tensorflow会话之前，您不需要的张量是x = tf.placeholder(dtype=tf.float32,shape=(None,32,32,3))。删除它，你的错误应该消失。