这个问题已经被问了好几次，但我似乎无法使以前的解决方案适应我的代码。因此，我对如何解决这个问题提出任何建议。我已经尝试过使用pdb并在问题出现之前设置一个跟踪点，这并没有给我很多信息。 我正在根据我的问题调整本教程： https://www.oreilly.com/ideas/visualizing-convolutional-neural-networks
数据形态：
x_train.shape: (1161, 68, 68, 1)
x_test.shape: (216, 68, 68, 1)
y_test.shape: (216,)
y_train.shape: (1161,)
发生错误的地方：
#Train the Model
steps = int(x_train.shape[0]/batchSize)
for i in range(numEpochs):
print(i)
accHist = []
accHist2 = []
#x_train, y_train = imf.shuffle(x_train, y_train)
for j in range(steps):
print(j)
#Calculate our current step
step = i * steps + j
#Feed forward batch of train images into graph and log accuracy
acc = sess.run([accuracy], feed_dict={X: x_train[(j*batchSize):((j+1)*batchSize),:,:,:], Y_: np.array(y_train[(j*batchSize):((j+1)*batchSize)]).reshape(1,30), keepRate1: 1, keepRate2: 1})
print(accHist)
accHist.append(acc)
#Back propigate using adam optimizer to update weights and biases.
sess.run(train_step, feed_dict={X: x_train[(j*batchSize):((j+1)*batchSize),:,:,:], Y_: np.array(y_train[(j*batchSize):((j+1)*batchSize)]).reshape(1,30), keepRate1: 0.2, keepRate2: 0.5})
print("success")
print('Epoch number {} Training Accuracy: {}'.format(i+1, np.mean(accHist)))
#Feed forward all test images into graph and log accuracy
for k in range(int(x_test.shape[0]/batchSize)):
acc = sess.run(accuracy, feed_dict={X: x_test[(k*batchSize):((k+1)*batchSize),:,:,:], Y_: np.array(y_test[(k*batchSize):((k+1)*batchSize)]).reshape(1,30), keepRate1: 1, keepRate2: 1})
accHist2.append(acc)
print("Test Set Accuracy: {}".format(np.mean(accHist2)))
我收到以下错误消息：
InvalidArgumentError: logits and labels must be same size: logits_size=[30,30] labels_size=[1,30]
[[Node: cross_entropy_7/SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](cross_entropy_7/Reshape, cross_entropy_7/Reshape_1)]]
按照教程，我认为登录设置在这里：
#FULLY CONNECTED 3 & SOFTMAX OUTPUT
with tf.name_scope('softmax') as scope:
fc2w = tf.Variable(tf.truncated_normal([512, classes], dtype=tf.float32,
stddev=1e-1), name='weights3_2')
fc2b = tf.Variable(tf.constant(1.0, shape=[classes], dtype=tf.float32),
trainable=True, name='biases3_2')
Ylogits = tf.nn.bias_add(tf.matmul(fc1_drop, fc2w), fc2b)
Y = tf.nn.softmax(Ylogits)
print（Ylogits.shape）在这里给了我：（？，30）。类设置为30，所以这似乎是有道理的。
这似乎是不起作用的功能，所以我打印了形状：
with tf.name_scope('cross_entropy'):
print(Ylogits.shape)
print(Y.shape)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)
loss = tf.reduce_mean(cross_entropy)
哪位给了我：
(?, 30)
(?, 30)
执行上面的反向传播线时虽然这似乎不起作用。有人可以帮忙吗？
回应评论（这基本上是上面提到的链接中的教程代码）：
持有人：
classes = 30
X = tf.placeholder(tf.float32, name="X-placeholder", shape=(None, 68, 68, 1))
Y_ = tf.placeholder(tf.float32, [None, classes], name="Y_-placeholder")
keepRate1 = tf.placeholder(tf.float32, name="keepRate1-placeholder")
keepRate2 = tf.placeholder(tf.float32, name="keepRate2-placeholder")
型号：
# CONVOLUTION 1 - 1
with tf.name_scope('conv1_1'):
filter1_1 = tf.Variable(tf.truncated_normal([3, 3, 1, 32], dtype=tf.float32,
stddev=1e-1), name='weights1_1')
stride = [1,1,1,1]
conv = tf.nn.conv2d(X, filter1_1, stride, padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[32], dtype=tf.float32),
trainable=True, name='biases1_1')
out = tf.nn.bias_add(conv, biases)
conv1_1 = tf.nn.relu(out)
# CONVOLUTION 1 - 2
with tf.name_scope('conv1_2'):
filter1_2 = tf.Variable(tf.truncated_normal([3, 3, 32, 32], dtype=tf.float32,
stddev=1e-1), name='weights1_2')
conv = tf.nn.conv2d(conv1_1, filter1_2, [1,1,1,1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[32], dtype=tf.float32),
trainable=True, name='biases1_2')
out = tf.nn.bias_add(conv, biases)
conv1_2 = tf.nn.relu(out)
# POOL 1
with tf.name_scope('pool1'):
pool1_1 = tf.nn.max_pool(conv1_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1_1')
pool1_1_drop = tf.nn.dropout(pool1_1, keepRate1)
# CONVOLUTION 2 - 1
with tf.name_scope('conv2_1'):
filter2_1 = tf.Variable(tf.truncated_normal([3, 3, 32, 64], dtype=tf.float32,
stddev=1e-1), name='weights2_1')
conv = tf.nn.conv2d(pool1_1_drop, filter2_1, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases2_1')
out = tf.nn.bias_add(conv, biases)
conv2_1 = tf.nn.relu(out)
# CONVOLUTION 2 - 2
with tf.name_scope('conv2_2'):
filter2_2 = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,
stddev=1e-1), name='weights2_2')
conv = tf.nn.conv2d(conv2_1, filter2_2, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases2_2')
out = tf.nn.bias_add(conv, biases)
conv2_2 = tf.nn.relu(out)
# POOL 2
with tf.name_scope('pool2'):
pool2_1 = tf.nn.max_pool(conv2_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2_1')
pool2_1_drop = tf.nn.dropout(pool2_1, keepRate1)
#FULLY CONNECTED 1
with tf.name_scope('fc1') as scope:
shape = int(np.prod(pool2_1_drop.get_shape()[1:]))
fc1w = tf.Variable(tf.truncated_normal([shape, 512], dtype=tf.float32,
stddev=1e-1), name='weights3_1')
fc1b = tf.Variable(tf.constant(1.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases3_1')
pool2_flat = tf.reshape(pool2_1_drop, [-1, shape])
out = tf.nn.bias_add(tf.matmul(pool2_flat, fc1w), fc1b)
fc1 = tf.nn.relu(out)
fc1_drop = tf.nn.dropout(fc1, keepRate2)
#FULLY CONNECTED 3 & SOFTMAX OUTPUT
with tf.name_scope('softmax') as scope:
fc2w = tf.Variable(tf.truncated_normal([512, classes], dtype=tf.float32,
stddev=1e-1), name='weights3_2')
fc2b = tf.Variable(tf.constant(1.0, shape=[classes], dtype=tf.float32),
trainable=True, name='biases3_2')
Ylogits = tf.nn.bias_add(tf.matmul(fc1_drop, fc2w), fc2b)
Y = tf.nn.softmax(Ylogits)
numEpochs = 400
batchSize = 30
alpha = 1e-5
with tf.name_scope('cross_entropy'):
print(Ylogits.shape)
print(Y.shape)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)
loss = tf.reduce_mean(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(learning_rate=alpha).minimize(loss)
#Create Session and insert variables
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
答案 0 :(得分：0)
张量形状（？，30）表示未设置批量大小，因此您可以将任何批量大小的数据提供给图形，问题是那时您可能遇到这些类型的问题，需要保持追踪你头脑中的张量形状。
你需要解决的问题是：要么你在一个批次中有30个图像，而在一个批次中只有1个标签需要修复，因为你不能计算只有一个标签的30个图像的损失，你需要要将图像数量减少到1或将标签批量大小增加到30，也可能是某些地方正在重新调整张量误差。
我会查看您在哪里读取数据，然后批量处理，最有可能出现问题的地方，或者在您正在重塑数据的地方。
发布您的整个代码，会更有帮助。