【发布时间】：2019-01-12 04:09:15
【问题描述】：
您好，希望你们身体健康。我正在研究 tensorflow，并在我正在研究 kaggle 的 cifar 10 数据集上训练我的神经网络。我是这个领域的新手，有很多复杂的问题。寻求您的帮助，谢谢。
这是我的代码
import tensorflow as tf
import pandas as pd
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
from six.moves import cPickle as pickle
import os
import platform
from subprocess import check_output
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
x=tf.placeholder('float',[None,3072])
y=tf.placeholder('float')
%matplotlib inline
def load_pickle(f):
version = platform.python_version_tuple()
if version[0] == '2':
return pickle.load(f)
elif version[0] == '3':
return pickle.load(f, encoding='latin1')
raise ValueError("invalid python version: {}".format(version))
def load_CIFAR_batch(filename):
""" load single batch of cifar """
with open(filename, 'rb') as f:
datadict = load_pickle(f)
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000,3072)
Y = np.array(Y)
return X, Y
def load_CIFAR10(ROOT):
""" load all of cifar """
xs = []
ys = []
for b in range(1,6):
f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
X, Y = load_CIFAR_batch(f)
xs.append(X)
ys.append(Y)
Xtr = np.concatenate(xs)
Ytr = np.concatenate(ys)
del X, Y
Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
return Xtr, Ytr, Xte, Yte
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
# Load the raw CIFAR-10 data
cifar10_dir = '../input/cifar-10-batches-py/'
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
# Subsample the data
mask = range(num_training, num_training + num_validation)
X_val = X_train[mask]
y_val = y_train[mask]
mask = range(num_training)
X_train = X_train[mask]
y_train = y_train[mask]
mask = range(num_test)
X_test = X_test[mask]
y_test = y_test[mask]
# Normalize the data: subtract the mean image
return X_train, y_train, X_val, y_val, X_test, y_test
# Invoke the above function to get our data.
x_train, y_train, x_val, y_val, x_test, y_test = get_CIFAR10_data()
print('Train data shape: ', x_train.shape)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', x_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', x_test.shape)
print('Test labels shape: ', y_test.shape)
layer1_neuron=500
layer2_neuron=500
layer3_neuron=500
number_of_class=10
batch_size=200
#my neural network
def neural_network(x_train):
hidden_layer_1={
'weights':tf.Variable(tf.random_normal([3072,layer1_neuron])),
'biases': tf.Variable(tf.random_normal([layer1_neuron]))
}
hidden_layer_2={
'weights':tf.Variable(tf.random_normal([layer1_neuron,layer2_neuron])),
'biases':tf.Variable(tf.random_normal([layer2_neuron]))
}
hidden_layer_3={
'weights':tf.Variable(tf.random_normal([layer2_neuron,layer3_neuron])),
'biases':tf.Variable(tf.random_normal([layer3_neuron]))
}
output={
'weights':tf.Variable(tf.random_normal([layer3_neuron,number_of_class])),
'biases':tf.Variable(tf.random_normal([number_of_class]))
}
l1=tf.add(tf.matmul(x_train,hidden_layer_1['weights']),hidden_layer_1['biases'])
l1=tf.nn.relu(l1)
l2=tf.add(tf.matmul(l1,hidden_layer_2['weights']),hidden_layer_2['biases'])
l2=tf.nn.relu(l2)
l3=tf.add(tf.matmul(l2,hidden_layer_3['weights']),hidden_layer_3['biases'])
l3=tf.nn.relu(l3)
output=tf.add(tf.matmul(l3,output['weights']),output['biases'])
return output
# for splitting out batches of data
def next_batch(num, data, labels):
idx = np.arange(0 , len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[ i] for i in idx]
labels_shuffle = [labels[ i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
def traning_neuralNetwork(x_train,y_train):
total_epochs=10
total_loss=0
epoch_loss=0
batch_size=200
num_batch = int(np.ceil(49000/batch_size))
prediction=neural_network(x)
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
optimizer=tf.train.AdamOptimizer().minimize(cost)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range (total_epochs):
total_loss=0
for _ in range (num_batch):
x_train,y_train=next_batch(batch_size,x_train,y_train)
_,epoch_loss=sess.run([optimizer,cost],feed_dict={x:x_train,y:y_train})
total_loss+=epoch_loss
print('Epoch ',epoch, " loss = ",total_loss)
print("Traning Complete!")
correct=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
accuracy=tf.reduce_mean(tf.cast(correct,'float'))
print('accuracy',accuracy.eval({x:x_test,y :y_test}))
traning_neuralNetwork(x_train,y_train)
**我面临成本函数的logits和标签形状不同的错误**
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1329 try:
-> 1330 return fn(*args)
1331 except errors.OpError as e:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1314 return self._call_tf_sessionrun(
-> 1315 options, feed_dict, fetch_list, target_list, run_metadata)
1316
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1422 self._session, options, feed_dict, fetch_list, target_list,
-> 1423 status, run_metadata)
1424
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
515 compat.as_text(c_api.TF_Message(self.status.status)),
--> 516 c_api.TF_GetCode(self.status.status))
517 # Delete the underlying status object from memory otherwise it stays alive
InvalidArgumentError: logits and labels must be same size: logits_size=[200,10] labels_size=[1,200]
[[Node: softmax_cross_entropy_with_logits_sg_3 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](softmax_cross_entropy_with_logits_sg_3/Reshape, softmax_cross_entropy_with_logits_sg_3/Reshape_1)]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-17-aeb4ef85487e> in <module>()
----> 1 traning_neuralNetwork(x_train,y_train)
<ipython-input-16-a54e1136abe5> in traning_neuralNetwork(x_train, y_train)
67 for _ in range (num_batch):
68 x_train,y_train=next_batch(batch_size,x_train,y_train)
---> 69 _,epoch_loss=sess.run([optimizer,cost],feed_dict={x:x_train,y:y_train})
70 total_loss+=epoch_loss
71 print('Epoch ',epoch, " loss = ",total_loss)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
906 try:
907 result = self._run(None, fetches, feed_dict, options_ptr,
--> 908 run_metadata_ptr)
909 if run_metadata:
910 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1141 if final_fetches or final_targets or (handle and feed_dict_tensor):
1142 results = self._do_run(handle, final_targets, final_fetches,
-> 1143 feed_dict_tensor, options, run_metadata)
1144 else:
1145 results = []
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1322 if handle is None:
1323 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1324 run_metadata)
1325 else:
1326 return self._do_call(_prun_fn, handle, feeds, fetches)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1341 except KeyError:
1342 pass
-> 1343 raise type(e)(node_def, op, message)
1344
1345 def _extend_graph(self):
InvalidArgumentError: logits and labels must be same size: logits_size=[200,10] labels_size=[1,200]
[[Node: softmax_cross_entropy_with_logits_sg_3 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](softmax_cross_entropy_with_logits_sg_3/Reshape, softmax_cross_entropy_with_logits_sg_3/Reshape_1)]]
Caused by op 'softmax_cross_entropy_with_logits_sg_3', defined at:
File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes
if self.run_code(code, result):
File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-17-aeb4ef85487e>", line 1, in <module>
traning_neuralNetwork(x_train,y_train)
File "<ipython-input-16-a54e1136abe5>", line 59, in traning_neuralNetwork
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 250, in new_func
return func(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1957, in softmax_cross_entropy_with_logits
labels=labels, logits=logits, dim=dim, name=name)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1871, in softmax_cross_entropy_with_logits_v2
precise_logits, labels, name=name)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 7142, in softmax_cross_entropy_with_logits
name=name)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3306, in create_op
op_def=op_def)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1669, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[200,10] labels_size=[1,200]
[[Node: softmax_cross_entropy_with_logits_sg_3 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](softmax_cross_entropy_with_logits_sg_3/Reshape, softmax_cross_entropy_with_logits_sg_3/Reshape_1)]]
【问题讨论】：
-
logits 的形状是 [batch_size, num_classes] 而你的标签是 [1, batch_size]。我想，你需要一些所谓的 one-hot 编码标签，这样标签的形状也是 [batch_size, num_classes]。
标签： tensorflow neural-network deep-learning