我遇到了很多错误（OOM，形状问题等），我设法以某种方式修复了这些错误。
但是我无法理解这个错误。我已经搜索了很多，我也尝试了稀疏交叉熵和tensorflow中的logits方法以及tf.squeeze函数，但这也没有帮助我解决这个错误。这是代码的链接（它是整个堆栈跟踪和错误的github要点）。
这是数据集的链接（大约500 Mb）
以下是代码（仅限于案例）：
from PIL import Image
import numpy as np
import glob
from numpy import array
import pandas as pd
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
import h5py
import tensorflow as tf
def loading_saving_image_as_grayscale_train(img):
##combined_path='M:/PycharmProjects/AI+DL+CP/test_img'+img
loading=Image.open(img)
loading=loading.resize((28,28),Image.ANTIALIAS)
loading=loading.convert('L')
#loading.show()
conversion_to_array=np.asarray(loading,dtype=float)
train_data.append(conversion_to_array)
def loading_saving_image_as_grayscale_test(img):
#combined_path = 'M:/PycharmProjects/AI+DL+CP/train_img/' + img
#print(combined_path)
loading=Image.open(img,'r')
loading=loading.resize((28,28),Image.ANTIALIAS)
loading=loading.convert('L')
conversion_to_array=np.asarray(loading,dtype=float)
test_data.append(conversion_to_array)
import os
import requests, zipfile, io
import pandas as pd
#url = requests.get('https://he-s3.s3.amazonaws.com/media/hackathon/deep-learning-challenge-1/identify-the-objects/a0409a00-8-dataset_dp.zip')
#data = zipfile.ZipFile(io.BytesIO(url.content))
#data.extractall()
#os.listdir()
dataframe1=pd.read_csv('test.csv')
dataframe1.index=dataframe1.index+1
only_index=dataframe['image_id']
test_data=[]
train_data=[]
train=glob.glob('train_img/*.png')
test=glob.glob('test_img/*.png')
#other=loading_saving_image_as_grayscale('M:/PycharmProjects/AI+DL+CP/test_img/test_1000b.png')
#print(Image.open('M:/PycharmProjects/AI+DL+CP/test_img/test_1000b.png'))
#print(test)
#loading_sample=Image.open('M:/PycharmProjects/AI+DL+CP/test_img/test_1000b.png')
#loading_sample.show()
#print(train)
#print(test)
for data in train:
#print(data)
loading_saving_image_as_grayscale_train(data)
for item in test:
#print(item)
loading_saving_image_as_grayscale_test(item)
#print(train_data)
#print(test_data)
'''with Image.fromarray(train_data[1]) as img:
width,height=img.size
print(width,height)
'''
def OneHot(label,n_classes):
label=np.array(label).reshape(-1)
label=np.eye(n_classes)[label]
return label
dataframe=pd.read_csv('train.csv')
train_data=np.asarray(train_data)
test_data=np.asarray(test_data)
uni=dataframe['label']
dataframe1=pd.read_csv('test.csv')
dataframe1.index=dataframe1.index+1
only_index=dataframe['image_id']
label=LabelEncoder()
integer_encoding=label.fit_transform(uni)
#del uni
#del dataframe
#print(integer_encoding)
binary=OneHotEncoder(sparse=False)
integer_encoding=integer_encoding.reshape(len(integer_encoding),1)
onehot=binary.fit_transform(integer_encoding)
train_data=np.reshape(train_data,[-1,28,28,1])
test_data=np.reshape(test_data,[-1,28,28,1])
#onehot=np.reshape(onehot,[-1,10])
train_data=np.transpose(train_data,(0,2,1,3))
test_data=np.transpose(test_data,(0,2,1,3))
train_data=train_data.astype(np.float32)
test_data=test_data.astype(np.float32)
print(train_data.shape,test_data.shape,onehot.shape)
graph = tf.Graph()
with graph.as_default():
# placeholders for input data batch_size x 32 x 32 x 3 and labels batch_size x 10
data_placeholder = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
label_placeholder = tf.placeholder(tf.int32, shape=[None, 25])
# defining decaying learning rate
global_step = tf.Variable(0)
decay_rate = tf.train.exponential_decay(1e-4, global_step=global_step, decay_steps=10000, decay_rate=0.97)
layer1_weights = tf.Variable(tf.truncated_normal([3, 3, 1, 64],stddev=0.1))
layer1_biases = tf.Variable(tf.constant(0.1, shape=[64]))
layer2_weights = tf.Variable(tf.truncated_normal([3, 3, 64,32],stddev=0.1))
layer2_biases = tf.Variable(tf.constant(0.1,shape=[32]))
layer3_weights = tf.Variable(tf.truncated_normal([2, 2, 32, 20],stddev=0.1))
layer3_biases = tf.Variable(tf.constant(0.1,shape=[20]))
layer4_weights = tf.Variable(tf.truncated_normal([20,25],stddev=0.1))
layer4_biases = tf.Variable(tf.constant(0.1,shape=[25]))
layer5_weights = tf.Variable(tf.truncated_normal([25, 25], stddev=0.1))
layer5_biases = tf.Variable(tf.constant(0.1, shape=[25]))
def layer_multiplication(data_input_given):
#Convolutional Layer 1
#data_input_given=np.reshape(data_input_given,[-1,64,64,1])
CNN1=tf.nn.relu(tf.nn.conv2d(data_input_given,layer1_weights,strides=[1,1,1,1],padding='SAME')+layer1_biases)
print('CNN1 Done!!')
#Pooling Layer
Pool1=tf.nn.max_pool(CNN1,ksize=[1,4,4,1],strides=[1,4,4,1],padding='SAME')
print('Pool1 DOne')
#second Convolution layer
CNN2=tf.nn.relu(tf.nn.conv2d(Pool1,layer2_weights,strides=[1,1,1,1],padding='SAME'))+layer2_biases
print('CNN2 Done')
#Second Pooling
Pool2 = tf.nn.max_pool(CNN2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
print('pool2 Done')
#Third Convolutional Layer
CNN3 = tf.nn.relu(tf.nn.conv2d(Pool2, layer3_weights, strides=[1, 1, 1, 1], padding='SAME')) + layer3_biases
print('CNN3 Done')
#Third Pooling Layer
Pool3 = tf.nn.max_pool(CNN3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
print('Pool3 DOne')
#Fully Connected Layer
Pool4=tf.nn.max_pool(Pool3,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
FullyCon=tf.reshape(Pool4,[-1,20])
FullyCon=tf.nn.relu(tf.matmul(FullyCon,layer4_weights)+layer4_biases)
print('Fullyconnected Done')
dropout = tf.nn.dropout(FullyCon, 0.4)
dropout=tf.reshape(dropout,[-1,25])
dropout=tf.matmul(dropout,layer5_weights)+layer5_biases
#print(dropout.shape)
return dropout
train_input = layer_multiplication(train_data)
print(train_input.shape)
loss = (tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label_placeholder,logits=train_input))
+ 0.01 * tf.nn.l2_loss(layer1_weights)
+ 0.01 * tf.nn.l2_loss(layer2_weights)
+ 0.01 * tf.nn.l2_loss(layer3_weights)
+ 0.01 * tf.nn.l2_loss(layer4_weights)
)
#other=(tf.squeeze(label_placeholder))
#print(tf.shape())
optimizer = tf.train.GradientDescentOptimizer(name='Stochastic', learning_rate=decay_rate).minimize(loss,global_step=global_step)
#print(train_input.shape)
batch_size = 10
num_steps=10000
prediction=[]
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
print('Initialized')
for i in range(num_steps):
print("in loop")
offset = (i * batch_size) % (onehot.shape[0] - batch_size)
batch_data = train_data[offset:(offset + batch_size), :, :]
batch_labels = onehot[offset:(offset + batch_size), :]
print("training")
feed_dict = {data_placeholder: batch_data, label_placeholder: batch_labels}
_, l, predictions = session.run(
[optimizer, loss, train_input], feed_dict=feed_dict)
print(sess.run(tf.argmax(label_placeholder, 1), feed_dict={x:test_data}))
prediction.append(sess.run(tf.argmax(label_placeholder,1),feed_dict={x:test_data}))
print('Finished')
submit=pd.Dataframe({'image_id':only_index, 'label':prediction})
submit.to_csv('submit.csv',index=False)
我对预测班级标签也有疑问。有人能告诉我，我用来存储预测类标签的方法是否有效？
答案 0 :(得分：1)
重塑操作没有意义：
FullyCon=tf.reshape(Pool4,[-1,20])
这将折叠批量维度和要素维度。
为什么Pool4的输出有20个维度？它有20个内核的事实并不意味着它有20个维度。维度在这个卷积级别上是20 *大小的图像，它将更大（我的猜测是6430）。
它应该是
之类的内容output_shape = Pool4.shape[1] * Pool4.shape[2] * Pool4.shape[3]
FullyCon=tf.reshape(Pool4, [-1, output_shape])
然后你必须相应地改变最后一层（以匹配形状）。
答案 1 :(得分：0)
在正确地重塑所有内容后，错误已得到修复，并且在带有logits部分的softmax中，我必须发送data_placeholder以进行logits。执行此操作后，问题已被清除。