Les dimensions doivent être égales, mais sont 64 et 32 ​​pour 'Conv2D_2' (op: 'Conv2D') avec des formes d'entrée: [?, 7,7,64], [5,5,32,64]

Question

Ceci est un 5 Code couches convolutionnel-:

# Template program with one convolution layer and one fully connected hidden layer, 
    # dropout and Adam Optimizer 
    from tensorflow.examples.tutorials.mnist import input_data 
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

    import tensorflow as tf 
    sess = tf.InteractiveSession() 

    # xi is an image of size n. yi is the N labels of the image 
    # X is mxn. Row xi of X is an image 
    # Y is mxN. Row yi of Y is the labels of xi 
    X = tf.placeholder(tf.float32, shape=[None, 784]) 
    Y = tf.placeholder(tf.float32, shape=[None, 10]) 


    def weight_variable(shape): 
     initial = tf.truncated_normal(shape, stddev=0.1) 
     return tf.Variable(initial) 

    def bias_variable(shape): 
     initial = tf.constant(0.1, shape=shape) 
     return tf.Variable(initial) 

    def conv2d(X, W): 
     return tf.nn.conv2d(X, W, strides=[1, 1, 1, 1], padding='SAME') 

    def max_pool_2x2(X): 
     return tf.nn.max_pool(X, ksize=[1, 2, 2, 1], 
          strides=[1, 2, 2, 1], padding='SAME') 

    # First Max Pool layer -- to resize the image to half of the image size 
    orig_image = tf.reshape(X, [-1,28,28,1]) 
    h_pool0 = max_pool_2x2(orig_image) 
    ### End of first max pool layer ### 

    # First Convolutional Layer 

    W_conv1 = weight_variable([5, 5, 1, 32]) 
    b_conv1 = bias_variable([32]) 

    h_conv1 = tf.nn.relu(conv2d(h_pool0, W_conv1) + b_conv1) 
    h_pool1 = max_pool_2x2(h_conv1) 

    # Second Convolutional Layer 

    W_conv2 = weight_variable([5, 5, 32, 64]) 
    b_conv2 = bias_variable([64]) 

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 

    # Third Convolutional Layer 

    W_conv3 = weight_variable([5, 5, 64, 128]) 
    b_conv3 = bias_variable([128]) 

    h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2) 

    # Fourth Convolutional Layer 

    W_conv3 = weight_variable([5, 5, 128, 256]) 
    b_conv3 = bias_variable([256]) 

    h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2) 

    # Fifth Convolutional Layer 

    W_conv3 = weight_variable([5, 5, 256, 512]) 
    b_conv3 = bias_variable([512]) 

    h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2) 

    # Densely Connected Layer 

    W_fc1 = weight_variable([7 * 7 * 512, 4096]) 
    b_fc1 = bias_variable([4096]) 

    h_pool2_flat = tf.reshape(h_conv3, [-1, 7*7*512]) 
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 

    # Dropout 

    keep_rate = 0.8 
    keep_prob = tf.placeholder(tf.float32) 
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_rate) 

    # Readout Layer 

    W_fc2 = weight_variable([4096, 10]) 
    b_fc2 = bias_variable([10]) 

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 

    cross_entropy = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=y_conv)) 
    #train_step = tf.train.GradientDescentOptimizer(0.005).minimize(cross_entropy) 
    train_step = tf.train.AdamOptimizer().minimize(cross_entropy) 

    correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(Y,1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    sess.run(tf.global_variables_initializer()) 
    for i in range(1000): 
     batch = mnist.train.next_batch(100) 
     if i%100 == 0: 
     train_accuracy = accuracy.eval(feed_dict={ 
      X:batch[0], Y: batch[1], keep_prob: 1.0}) 
     print("step %d, training accuracy %g"%(i, train_accuracy)) 
     train_step.run(feed_dict={X: batch[0], Y: batch[1], keep_prob: 0.5}) 

    print("test accuracy %g"%accuracy.eval(feed_dict={ 
     X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1.0})) 

Et je ne sais pas pourquoi, mais je reçois ce message d'erreur à chaque fois que je le lance:

Traceback (most recent call last): 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 671, in _call_cpp_shape_fn_impl 
    input_tensors_as_shapes, status) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\contextlib.py", line 66, in __exit__ 
    next(self.gen) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\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.InvalidArgumentError: Dimensions must be equal, but are 64 and 32 for 'Conv2D_2' (op: 'Conv2D') with input shapes: [?,7,7,64], [5,5,32,64]. 

During handling of the above exception, another exception occurred: 

Traceback (most recent call last): 
    File "C:/Users/Inshal/Desktop/Inshal Haq/UTD/Spring 2017/Computer Vision/Project2/mnist3.py", line 57, in <module> 
    h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2) 
    File "C:/Users/Inshal/Desktop/Inshal Haq/UTD/Spring 2017/Computer Vision/Project2/mnist3.py", line 25, in conv2d 
    return tf.nn.conv2d(X, W, strides=[1, 1, 1, 1], padding='SAME') 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 403, in conv2d 
    data_format=data_format, name=name) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op 
    op_def=op_def) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2338, in create_op 
    set_shapes_for_outputs(ret) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1719, in set_shapes_for_outputs 
    shapes = shape_func(op) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1669, in call_with_requiring 
    return call_cpp_shape_fn(op, require_shape_fn=True) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 610, in call_cpp_shape_fn 
    debug_python_shape_fn, require_shape_fn) 
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 676, in _call_cpp_shape_fn_impl 
    raise ValueError(err.message) 
ValueError: Dimensions must be equal, but are 64 and 32 for 'Conv2D_2' (op: 'Conv2D') with input shapes: [?,7,7,64], [5,5,32,64]. 

est-ce que quelqu'un sait quel est le problème avec mon code, et comment puis-je le réparer?

Nous vous remercions de votre participation!

Answer 1

Vous avez un problème avec votre taille de filtre dans chaque couche, vous devez imprimer la forme de chaque couche de suivi et de débogage, utilisez get_shape() après chaque opération comme:

h_conv1 = tf.nn.relu(conv2d(h_pool0, W_conv1) + b_conv1) 
print h_conv1.get_shape() 
h_pool1 = max_pool_2x2(h_conv1) 
print h_pool1.get_shape() 

# Second Convolutional Layer 
W_conv2 = weight_variable([5, 5, 32, 64]) 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 
print h_conv2.get_shape() 

Ensuite, vous pouvez réaliser ce qui se passe mal .

Answer 2

La réponse d'Ali est un bon moyen de déboguer le problème. Votre problème spécifique est que vous avez une faute de frappe dans votre code --- vous utilisez les mauvais poids dans la troisième couche convolutionnelle.

# Third Convolutional Layer 

W_conv3 = weight_variable([5, 5, 64, 128]) 
b_conv3 = bias_variable([128]) 

# You wrote 
# h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2) 
# when you probably meant: 
h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3) + b_conv3) 

Espérons que ça aide!