我正在将此代码用于卷积神经网络。
learning_rate = 0.001
X = tf.placeholder(tf.float32, [None, 50*50])
X_img = tf.reshape(X, [-1, 50, 50, 3])
Y = tf.placeholder(tf.float32, [None, 26])
W1 = tf.Variable(tf.random_normal([5, 5, 3, 32]))
L1 = tf.nn.conv2d(X_img, W1, strides=[1, 1, 1, 1], padding='SAME')
L1 = tf.nn.relu(L1)
L1 = tf.nn.max_pool(L1, ksize=[1, 5, 5, 1], strides=[1, 5, 5, 1], padding='SAME')
W2 = tf.Variable(tf.random_normal([5, 5, 32, 64]))
L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
L2 = tf.nn.relu(L2)
L2 = tf.nn.max_pool(L2, ksize=[1, 5, 5, 1], strides=[1, 5, 5, 1], padding='SAME')
L2_flat = tf.reshape(L2, [-1, 5*5*64])
W3 = tf.get_variable("W3", shape=[5*5*64, 26], initializer=tf.contrib.layers.xavier_initializer())
print(W3)
b = tf.Variable(tf.random_normal([26]))
logits = tf.matmul(L2_flat, W3) + b
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
training_epochs = 15
batch_size = 14
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print('Learning started. It takes sometime.')
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(len(train_input) / batch_size)
for i in range(total_batch):
start = ((i+1) * batch_size) - batch_size
end = ((i+1) * batch_size)
batch_xs = train_input[start:end]
batch_ys = train_label[start:end]
feed_dict = {X: batch_xs, Y: batch_ys}
c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
avg_cost += c / total_batch
print('Epoch:', '%04d' % (epoch +1), 'cost = ', '{:.9f}'.format(avg_cost))
print('Learning Finished')
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy:', sess.run(accuracy, feed_dict={
X: test_input, Y: test_label}))
我正在读取50x50 RGB图像。我有406张图片和26个标签。
我正在使用5x5滤镜，并且火车输入的形状出现了
print(train_input.shape) (1218, 2500)
我不明白为什么会出现“ 1218”以及这个数字的含义。
而且也不理解以下错误。
InvalidArgumentError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1321 try:
-> 1322 return fn(*args)
1323 except errors.OpError as e:
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1306 return self._call_tf_sessionrun(
-> 1307 options, feed_dict, fetch_list, target_list, run_metadata)
1308
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1408 self._session, options, feed_dict, fetch_list, target_list,
-> 1409 run_metadata)
1410 else:
InvalidArgumentError: Input to reshape is a tensor with 35000 values, but the requested shape requires a multiple of 7500
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_0_0, Reshape/shape)]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-12-e97c33c78b58> in <module>()
16 batch_ys = train_label[start:end]
17 feed_dict = {X: batch_xs, Y: batch_ys}
---> 18 c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
19 avg_cost += c / total_batch
20 print('Epoch:', '%04d' % (epoch +1), 'cost = ', '{:.9f}'.format(avg_cost))
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
898 try:
899 result = self._run(None, fetches, feed_dict, options_ptr,
--> 900 run_metadata_ptr)
901 if run_metadata:
902 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1133 if final_fetches or final_targets or (handle and feed_dict_tensor):
1134 results = self._do_run(handle, final_targets, final_fetches,
-> 1135 feed_dict_tensor, options, run_metadata)
1136 else:
1137 results = []
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1314 if handle is None:
1315 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1316 run_metadata)
1317 else:
1318 return self._do_call(_prun_fn, handle, feeds, fetches)
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1333 except KeyError:
1334 pass
-> 1335 raise type(e)(node_def, op, message)
1336
1337 def _extend_graph(self):
InvalidArgumentError: Input to reshape is a tensor with 35000 values, but the requested shape requires a multiple of 7500
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_0_0, Reshape/shape)]]
Caused by op 'Reshape', defined at:
File "C:\Users\sunghee hong\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\sunghee hong\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 486, in start
self.io_loop.start()
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 127, in start
self.asyncio_loop.run_forever()
File "C:\Users\sunghee hong\Anaconda3\lib\asyncio\base_events.py", line 422, in run_forever
self._run_once()
File "C:\Users\sunghee hong\Anaconda3\lib\asyncio\base_events.py", line 1432, in _run_once
handle._run()
File "C:\Users\sunghee hong\Anaconda3\lib\asyncio\events.py", line 145, in _run
self._callback(*self._args)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 117, in _handle_events
handler_func(fileobj, events)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 450, in _handle_events
self._handle_recv()
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 480, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 432, in _run_callback
callback(*args, **kwargs)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2662, in run_cell
raw_cell, store_history, silent, shell_futures)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2785, in _run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2903, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-11-02a422681da1>", line 4, in <module>
X_img = tf.reshape(X, [-1, 50, 50, 3]) #[batch, width, height, image channel(RGB:3, GRAY:1)], batch size는 가변할 수 있어서 대부분 -1
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 6112, in reshape
"Reshape", tensor=tensor, shape=shape, name=name)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\Users\sunghee hong\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 35000 values, but the requested shape requires a multiple of 7500
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_0_0, Reshape/shape)]]
如何解决此问题？ 谢谢。
答案 0 :(得分：0)
您的问题1。
打印（train_input.shape）（1218，2500）
1218值来自X = tf.placeholder(tf.float32, [None, 50*50])，您正在馈入406个50x50 RGB图像（3个通道）。
或者换句话说，通过这一行代码，您正在请求大小为[406, 3, 50, 50] -> [?, 2500]的输入张量。这样您得到1218 = 406*3。
您的问题2。
输入张量具有35000值有点奇怪，请在X_img = tf.reshape(X, [-1, 50, 50, 3])线周围再检查一下X