卷积神经网络部分，每训练到2500步就报错，什么原因?
作者：互联网
import tensorflow as tf
import os
import pickle
import numpy as np
CIFAR_DIR = "C:/Users/tianr/Desktop/cifar-10-batches-py"
print(os.listdir(CIFAR_DIR))
def load_data(filename):
"""read data from data file"""
with open(filename,'rb') as f:
data = pickle.load(f,encoding='bytes')
return data[b'data'],data[b'labels']
# API：tesorflow.Dataset
class CifarData:
def __init__(self,filenames,need_shuffle):
all_data = []
all_labels = []
for filename in filenames:
data,labels = load_data(filename)
all_data.append(data)
all_labels.append(labels)
self._data = np.vstack(all_data)
self._data = self._data / 127.5 - 1# 归一化将像素缩放到[-1,1]
self._labels = np.hstack(all_labels)
print(self._data.shape)
print(self._labels.shape)
self._num_examples = self._data.shape[0]
self._need_shuffle = need_shuffle
self._indicator = 0 #定义数据起始位置
if self._need_shuffle:
self._shuffle_data()
def _shuffle_data(self):
# [0,1,2,3,4,5] -> [3,1,2,5,0,4]
p = np.random.permutation(self._num_examples)
self._data = self._data[p]
self._labels = self._labels[p]
def next_batch(self,batch_size):
"""return batch_size examples as a batch"""
end_indicator = self._indicator + batch_size
if end_indicator > self._num_examples:
if self._need_shuffle:
self._shuffle_data()
end_indicator = batch_size
else:
raise Exception("have no more examples")
if end_indicator > self._num_examples:
raise Exception("batch size is larger than all examples")
batch_data = self._data[self._indicator: end_indicator]
batch_labels = self._labels[self._indicator: end_indicator]
self._indicator = end_indicator
return batch_data,batch_labels
train_filenames = [os.path.join(CIFAR_DIR,'data_batch_%d' % i) for i in range(1,6)]
test_filenames = [os.path.join(CIFAR_DIR,'test_batch')]
train_data = CifarData(train_filenames,True)
test_data = CifarData(test_filenames,False)
x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])
# [None], eg: [0,5,6,3]
x_image = tf.reshape(x, [-1, 3, 32, 32])
# 32*32
x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])
# conv1: 神经元图， feature_map, 输出图像
conv1 = tf.layers.conv2d(x_image,
32, # output channel number
(3,3), # kernel size
padding = 'same',
activation = tf.nn.relu,
name = 'conv1')
# 16 * 16
pooling1 = tf.layers.max_pooling2d(conv1,
(2, 2), # kernel size
(2, 2), # stride
name = 'pool1')
conv2 = tf.layers.conv2d(pooling1,
32, # output channel number
(3,3), # kernel size
padding = 'same',
activation = tf.nn.relu,
name = 'conv2')
# 8 * 8
pooling2 = tf.layers.max_pooling2d(conv2,
(2, 2), # kernel size
(2, 2), # stride
name = 'pool2')
conv3 = tf.layers.conv2d(pooling2,
32, # output channel number
(3,3), # kernel size
padding = 'same',
activation = tf.nn.relu,
name = 'conv3')
# 4 * 4 * 32
pooling3 = tf.layers.max_pooling2d(conv3,
(2, 2), # kernel size
(2, 2), # stride
name = 'pool3')
# [None, 4 * 4 * 32]
flatten = tf.layers.flatten(pooling3)
y_ = tf.layers.dense(flatten, 10)
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
# y_ -> sofmax
# y -> one_hot
# loss = ylogy_
# indices
predict = tf.argmax(y_, 1)
# [1,0,1,1,1,0,0,0]
correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
with tf.name_scope('train_op'):
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
init = tf.global_variables_initializer()
batch_size = 20
train_steps = 10000
test_steps = 100
with tf.Session() as sess:
sess.run(init)
for i in range(train_steps):
batch_data, batch_labels = train_data.next_batch(batch_size)
loss_val, acc_val, _ = sess.run(
[loss, accuracy, train_op],
feed_dict={
x: batch_data,
y: batch_labels})
if (i+1) % 50 == 0:
print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' % (i+1, loss_val, acc_val))
if (i+1) % 500 == 0:
test_data = CifarData(test_filenames, False)
all_test_acc_val = []
for j in range(test_steps):
test_batch_data, test_batch_labels \
= test_data.next_batch(batch_size)
test_acc_val = sess.run(
[accuracy],
feed_dict = {
x: test_batch_data,
y: test_batch_labels
})
all_test_acc_val.append(test_acc_val)
test_acc = np.mean(all_test_acc_val)
print('[Test ] Step: %d, acc: %4.5f' % (i+1, test_acc))
一、报错截图1：
二、报错截图2：
三、InvalidArgumentError:
Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero
[[Node: flatten/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](pool3/MaxPool, flatten/Reshape/shape)]]
四、解决办法：
训练集有50000张图片，程序中batch size是20, 2500步正是数据集遍历一次的时候。所以检查了一下next_batch方法，发现了问题, 看注释：
def next_batch(self,batch_size):
"""return batch_size examples as a batch"""
end_indicator = self._indicator + batch_size
if end_indicator > self._num_examples:
if self._need_shuffle:
self._shuffle_data()
# 这里少了self._indicator = 0
end_indicator = batch_size
else:
raise Exception("have no more examples")
if end_indicator > self._num_examples:
raise Exception("batch size is larger than all examples")
batch_data = self._data[self._indicator: end_indicator]
batch_labels = self._labels[self._indicator: end_indicator]
self._indicator = end_indicator
return batch_data,batch_labels
标签：indicator,卷积,self,batch,报错,._,tf,data,2500 来源： https://blog.csdn.net/qq_40108803/article/details/100519147