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Dies ist mein Code-Snippet, wie ich alle Zugbilder verarbeite (links und rechts und Maske separat). In den Variablen l sind r Tensoren mit der Form [4, ?, ?, 3] zugeordnet.InvalidArgumentError: ConcatOp: Die Abmessungen der Eingaben sollten übereinstimmen: shape [0] = [1,246,381,3] vs. shape [1] = [1,252,367,3]
with tf.Session() as session:
l_train = [x.l_img for x in images][:4]
r_train = [x.r_img for x in images][:4]
m_train = [x.mask for x in images][:4]
l = tf.concat(l_train, 0)
r = tf.concat(r_train, 0)
m = tf.concat(m_train, 0)
l.eval()
Bei der Verwendung von eval() habe ich diesen Fehler?
Traceback (most recent call last):
File "/home/test/anaconda2/envs/tensorflow/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-5-f78dccf94f7f>", line 1, in <module>
l.eval()
File "/home/test/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 606, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/home/test/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3928, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "/home/test/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/home/test/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/home/test/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/home/test/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,246,381,3] vs. shape[1] = [1,252,367,3]
[[Node: concat = ConcatV2[N=4, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](Reading/reshape_t_left/_1, Reading/reshape_t_left_1/_3, Reading/reshape_t_left_2/_5, Reading/reshape_t_left_3/_7, concat/axis)]]
[[Node: concat/_9 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_370_concat", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Wie trainiere ich mein Training mit dynamischen Patchgrößen? In der Zwischenzeit überstreiche ich meine Bilder und füttere meinen CNN mit einem Bild nach dem anderen.
_, summary_str, costs = sess.run([optimizer, merged_summary_op, cost_function],
feed_dict={t_im0: l.eval(), t_im1: r.eval(),
t_label: m.eval()})