Abrufen von InvalidArgumentError in Tensorflow

Question

Ich habe ein CNN für die Vorhersage der Beschriftungen eines Bildes erstellt. Ich habe es trainiert. Jetzt möchte ich mein Modell für die Vorhersage der Etiketten für neues Bild verwenden. Mein Code für CNN ist dies: -

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") 

Jetzt habe ich einige Bilder aus dem Internet heruntergeladen und wollte die Etiketten für sie vorhersagen. Der Code für die Verarbeitung der Bilder und deren Umwandlung zu numpy.ndarray wie folgt: -

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])) 

Hier i ich brauche die Etiketten für diese Bilder zur Vorhersage des gespeicherten Modells. Mein Code für das gleiche ist wie folgt: -

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"]()]] 

Mein Datentyp für feed_dict ist nur schwimmen. Von den letzten 3 Tagen habe ich Mühe, dies ohne Erfolg zu debuggen. Ihre Hilfe wird sehr geschätzt.

Answer 1

was ist, wenn Sie die Form in keep_prob

keep_prob = tf.placeholder(dtype=tf.float32, shape=(1)) 

Answer 2

Sie erhalten eine ValueError definieren. Laut der Tensorflow-Dokumentation erhalten Sie diesen Fehler "Wenn Abrufe oder feed_dict Schlüssel ungültig sind oder sich auf einen Tensor beziehen, der nicht existiert" (siehe Tensorflow Session Documentation).

Der Tensor, den Sie nicht benötigen, ist x = tf.placeholder(dtype=tf.float32,shape=(None,32,32,3)) kurz vor Ihrer Tensorflow-Sitzung. Entfernen Sie es und Sie sollten den Fehler verschwinden.