我正在通过TensorFlow实现此目标（https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py）。我的代码如下。
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
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')
if __name__ == '__main__':
mnist = input_data.read_data_sets('data', one_hot=True)
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
sess = tf.InteractiveSession()
x_image = tf.reshape(x, [-1,28,28,1])
W_conv1 = weight_variable([3, 3, 1, 32])
b_conv1 = bias_variable([32])
W_conv2 = weight_variable([3, 3, 32, 32])
b_conv2 = bias_variable([32])
W_fc1 = weight_variable([12*12*32, 128])
b_fc1 = bias_variable([128])
W_fc2 = weight_variable([128, 10])
b_fc2 = bias_variable([10])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)
h_pool = max_pool_2x2(h_conv2)
keep_prob1 = tf.placeholder("float")
h_drop1 = tf.nn.dropout(h_pool, keep_prob1)
h_flat = tf.reshape(h_drop1, [-1, 12*12*32])
h_fc1 = tf.nn.relu(tf.matmul(h_flat, W_fc1) + b_fc1)
keep_prob2 = tf.placeholder("float")
h_drop2 = tf.nn.dropout(h_fc1, keep_prob2)
y_conv = tf.nn.softmax(tf.matmul(h_drop2, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob1: 1.0, keep_prob2: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob1: 0.25, keep_prob2: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob1: 1.0, keep_prob2: 1.0}))
当我跑步时，在展平图层上发生以下错误。我试图匹配输入形状，但错误信息表示＆＃34;一个313600值的张量＆＃34;我不知道它来自哪里。
Traceback (most recent call last):
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 972, in _do_call
return fn(*args)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 954, in _run_fn
status, run_metadata)
File "/usr/local/Cellar/python3/3.5.1/Frameworks/Python.framework/Versions/3.5/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/framework/errors.py", line 463, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors.InvalidArgumentError: Input to reshape is a tensor with 313600 values, but the requested shape requires a multiple of 4608
[[Node: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](dropout/mul, Reshape_1/shape)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "mnist_tensorflow.py", line 60, in <module>
x: batch[0], y_: batch[1], keep_prob1: 1.0, keep_prob2: 1.0})
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 559, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3761, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 717, in run
run_metadata_ptr)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 915, in _run
feed_dict_string, options, run_metadata)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 965, in _do_run
target_list, options, run_metadata)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 985, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.InvalidArgumentError: Input to reshape is a tensor with 313600 values, but the requested shape requires a multiple of 4608
[[Node: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](dropout/mul, Reshape_1/shape)]]
Caused by op 'Reshape_1', defined at:
File "mnist_tensorflow.py", line 44, in <module>
h_flat = tf.reshape(h_drop1, [-1, 12*12*32])
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1977, in reshape
name=name)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op
op_def=op_def)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2380, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Users/username/Projects/projectname/keras/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1298, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 313600 values, but the requested shape requires a multiple of 4608
[[Node: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](dropout/mul, Reshape_1/shape)]]
我已经检查了类似的问题（Error: Tensorflow CNN dimension），我确认我定义了目标图像以进行整形/展平。如果您有任何想法，请告诉我。
答案 0 :(得分：0)
错误非常简单。我发现我应该设置'有效'，而不是＆＃39;相同＆＃39;在conv2中，这样我可以在平整操作之前制作12,12,32形状。谢谢乌龟帮助我。