I've worked through a few other errors that've been thrown, but I've never seen this error before and even after doing some research I'm still not sure exactly what the issue is here or how to solve it.
I'm guessing reshaping the data at some point is required, but what i don't get is why this is an issue, or what the sizes of [1,2] and [1,1] actually mean.
The data input into the script is [128 x 128 x 128 ndarray, binary label]
The code I'm using is:
import tensorflow as tf import numpy as np import os import math # input arrays x = tf.placeholder(tf.float32, [None, 128, 128, 128, 1]) # labels y = tf.placeholder(tf.float32, None) # learning rate lr = tf.placeholder(tf.float32) ##### Code for ConvNet is here ##### # Data INPUT_FOLDER = 'data/cubed_data/pp/labelled' images = os.listdir(INPUT_FOLDER) images.sort() td = [] count = 1 for i in images: im = np.load(INPUT_FOLDER + "/" + i) data = im[0] data = np.reshape(data, (128, 128, 128, 1)) label = im[1] lbd = [data, label] td.append(lbd) test_data = td[:100] train_data = td[100:] cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=fc3l, labels=y) correct_prediction = tf.equal(tf.argmax(probs, 1), tf.argmax(y, 0)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy) # init init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) def training_step(i, update_test_data, update_train_data): for a in range(len(train_data)): batch = train_data[a] batch_x = batch[0] batch_y = batch[1] # learning rate decay max_learning_rate = 0.003 min_learning_rate = 0.0001 decay_speed = 2000.0 learning_rate = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i / decay_speed) if update_train_data: a, c = sess.run([accuracy, cross_entropy], {x: [batch_x], y: [batch_y]}) print(str(i) + ": accuracy:" + str(a) + " loss: " + str(c) + " (lr:" + str(learning_rate) + ")") if update_test_data: a, c = sess.run([accuracy, cross_entropy], {x: [test_data[0]], y: [test_data[1]]}) print(str(i) + ": ********* epoch " + " ********* test accuracy:" + str(a) + " test loss: " + str(c)) sess.run(train_step, {x: [batch_x], y: [batch_y], lr: learning_rate}) for q in range(10000 + 1): training_step(q, q % 100 == 0, q % 20 == 0) ..with:
Invalid argument: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] Traceback (most recent call last): File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 972, in _do_call return fn(*args) File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 954, in _run_fn status, run_metadata) File "/usr/lib/python3.5/contextlib.py", line 66, in __exit__ next(self.gen) File "/home/entelechy/tfenv/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: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] [[Node: Reshape_2/_7 = _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_233_Reshape_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "tfvgg.py", line 293, in <module> training_step(q, q % 100 == 0, q % 20 == 0) File "tfvgg.py", line 282, in training_step a, c = sess.run([accuracy, cross_entropy], {x: [batch_x], y: [batch_y]}) File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 717, in run run_metadata_ptr) File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 915, in _run feed_dict_string, options, run_metadata) File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 965, in _do_run target_list, options, run_metadata) File "/home/entelechy/tfenv/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: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] [[Node: Reshape_2/_7 = _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_233_Reshape_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] Caused by op 'SoftmaxCrossEntropyWithLogits', defined at: File "tfvgg.py", line 254, in <module> cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=fc3l, labels=y) File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 676, in softmax_cross_entropy_with_logits precise_logits, labels, name=name) File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1744, in _softmax_cross_entropy_with_logits features=features, labels=labels, name=name) File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op op_def=op_def) File "/home/entelechy/tfenv/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 "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1298, in __init__ self._traceback = _extract_stack() InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] [[Node: Reshape_2/_7 = _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_233_Reshape_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 1 Answers
Answers 1
After taking a closer look I found that the issue was that the output of the 3rd fully-connected layer was 2 classes, when the labels are binary for a single class. Changed code in last fully connected layer to account for the single class, and this error was solved.
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