我有以下代码：
import numpy as np
import matplotlib.pyplot as plt
import cifar_tools
import tensorflow as tf
data, labels = cifar_tools.read_data('C:\\Users\\abc\\Desktop\\temp')
x = tf.placeholder(tf.float32, [None, 24 * 24])
y = tf.placeholder(tf.float32, [None, 2])
w1 = tf.Variable(tf.random_normal([5, 5, 1, 64]))
b1 = tf.Variable(tf.random_normal([64]))
w2 = tf.Variable(tf.random_normal([5, 5, 64, 64]))
b2 = tf.Variable(tf.random_normal([64]))
w3 = tf.Variable(tf.random_normal([6*6*64, 1024]))
b3 = tf.Variable(tf.random_normal([1024]))
w_out = tf.Variable(tf.random_normal([1024, 2]))
b_out = tf.Variable(tf.random_normal([2]))
def conv_layer(x,w,b):
conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
conv_with_b = tf.nn.bias_add(conv,b)
conv_out = tf.nn.relu(conv_with_b)
return conv_out
def maxpool_layer(conv,k=2):
return tf.nn.max_pool(conv, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')
def model():
x_reshaped = tf.reshape(x, shape=[-1,24,24,1])
conv_out1 = conv_layer(x_reshaped, w1, b1)
maxpool_out1 = maxpool_layer(conv_out1)
norm1 = tf.nn.lrn(maxpool_out1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
conv_out2 = conv_layer(x_reshaped, w2, b2)
norm2 = tf.nn.lrn(maxpool_out2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
maxpool_out2 = maxpool_layer(conv_out2)
maxpool_reshaped = tf.reshape(maxpool_out2, [-1,w3.get_shape().as_list()[0]])
local = tf.add(tf.matmul(maxpool_reshaped, w3), b3)
local_out = tf.nn.relu(local)
out = tf.add(tf.matmul(local_out, w_out), b_out)
return out
model_op = model()
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_legits(model_op, y))
train_op = tf.train.AdamOptimizer(learning_rate-0.001).minimize(cost)
current_pred = tf.equal(tf.argmax(model_op, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
onehot_labels = tf.one_hot(labels, 2, on_value=1.,off_value=0.,axis=-1)
onehot_vals = sess.run(onehot_labels)
batch_size = len(data) / 200
print('batch size', batch_size)
for j in range(0, 1000):
print('EPOCH', j)
for j in range(0, len(data), batch_size):
batch_data = data[i:i+batch_size, :]
batch_onehot_vals = onehot_vals[i,i+batch_size, :]
_, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: batch_data, y: batch_onehot_vals})
if i % 1000 == 0:
print(i, accuracy_val)
print('DONE WITH EPOCH')
当我运行代码时，出现以下错误，我不确定如何修复：
Traceback (most recent call last):
File "C:\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 670, in _call_cpp_shape_fn_impl
status)
File "C:\Python35\lib\contextlib.py", line 66, in __exit__
next(self.gen)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 1 and 64 for 'Conv2D_1' (op: 'Conv2D') with input shapes: [?,24,24,1], [5,5,64,64].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "cnn.py", line 50, in <module>
model_op = model()
File "cnn.py", line 39, in model
conv_out2 = conv_layer(x_reshaped, w2, b2)
File "cnn.py", line 24, in conv_layer
conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
File "C:\Python35\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 396, in conv2d
data_format=data_format, name=name)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 759, in apply_op
op_def=op_def)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2242, in create_op
set_shapes_for_outputs(ret)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1617, in set_shapes_for_outputs
shapes = shape_func(op)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1568, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "C:\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:\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 675, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimensions must be equal, but are 1 and 64 for 'Conv2D_1' (op: 'Conv2D') with input shapes: [?,24,24,1], [5,5,64,64].
您是否了解我为何会收到此错误，以及如何解决此错误？
感谢。
答案 0 :(得分：1)
在您的代码中，您还对原始数据应用第二个卷积
conv_out2 = conv_layer(x_reshaped, w2, b2)
然后通道数不匹配（原始数据中的1个通道，w2中的64个输入通道）。
如果要将其应用于第一层的输出，请将其替换为maxpool_out1，即：
conv_out2 = conv_layer(maxpool_out1, w2, b2)
然后通道数应该匹配，因为w1有64个输出通道。
此外，在您的代码的以下部分
conv_out2 = conv_layer(x_reshaped, w2, b2)
norm2 = tf.nn.lrn(maxpool_out2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
maxpool_out2 = maxpool_layer(conv_out2)
我认为你应该颠倒最后两行的顺序。因为您在定义它之前使用maxpool_out2。