問題描述

我的數據集才一千多個，是不是用深度神經網絡的模型，不夠，容易欠擬合

問題出現的環境背景及自己嘗試過哪些方法

我之前的訓練參照了兩層的CIFAR卷積層測試了

用1000次迭代 每次10batch_size

結果

相關代碼

// 請把代碼文本粘貼到下方(請勿用圖片代替代碼)

import cv2

import numpy as np

import os

import random

import tensorflow as tf

import sklearn.utils

def read_and_decode(filename, testing = False):

filename_queue = tf.train.string_input_producer([filename])

reader = tf.TFRecordReader()

_, serialized_example = reader.read(filename_queue)

if testing == False:

features = tf.parse_single_example(serialized_example,

features={

'label': tf.FixedLenFeature([], tf.int64),

'img_raw' : tf.FixedLenFeature([], tf.string),

})

img = tf.decode_raw(features['img_raw'], tf.uint8)

img = tf.reshape(img, [600, 328, 1])

img = tf.cast(img, tf.float32) * (1. / 255) - 0.5

label = tf.cast(features['label'], tf.int32)

return img, label

else:

features = tf.parse_single_example(serialized_example,

features={

'label_test': tf.FixedLenFeature([], tf.int64),

'img_raw_test' : tf.FixedLenFeature([], tf.string),

})

img = tf.decode_raw(features['img_raw_test'], tf.uint8)

img = tf.reshape(img, [600, 328, 1])

img = tf.cast(img, tf.float32) * (1. / 255) - 0.5

label = tf.cast(features['label_test'], tf.int32)

return img, label

if name == '__main__':

img, label = read_and_decode("train.tfrecords")

img_train, label_train = tf.train.shuffle_batch([img, label],

batch_size=10, capacity=2000,

min_after_dequeue=1000)

img_raw_test, label_test = read_and_decode("test.tfrecords", testing = True)

img_test, label_test = tf.train.shuffle_batch([img_raw_test, label_test],

batch_size=10, capacity=2000,

min_after_dequeue=1000)

print("begin")

print("begin data")

def weight_variable(shape):

initial = tf.truncated_normal(shape, stddev = 0.1)

return tf.Variable(initial)

def bias_variable(shape):

initial = tf.constant(0.1, shape = shape)

return tf.Variable(initial)

def conv2d(x, W):

return tf.nn.conv2d(x, W, strides = [1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):

return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')

def avg_pool_82x150(x):

return tf.nn.avg_pool(x, ksize = [1, 150, 82, 1], strides = [1, 150, 82, 1], padding = 'SAME')

x = tf.placeholder(tf.float32, [None, 600, 328, 1])

y = tf.placeholder(tf.float32, [None, 6])

W_conv1 = weight_variable([5, 5, 1, 64])

b_conv1 = bias_variable([64])

x_image = tf.reshape(x, [-1, 600, 328, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 64, 64])

b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

h_pool2 = max_pool_2x2(h_conv2)

W_conv3 = weight_variable([5, 5, 64, 6])

b_conv3 = bias_variable([6])

h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)

# 經過兩層池化后 圖片變成82*150

nt_hpool3 = avg_pool_82x150(h_conv3)

nt_hpool3_flat = tf.reshape( nt_hpool3 , [-1, 6])

y_conv = tf.nn.softmax(nt_hpool3_flat)

cross_entropy = -tf.reduce_sum(y*tf.log(y_conv))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

#開始會話訓練

sess = tf.Session()

sess.run(tf.global_variables_initializer())

tf.train.start_queue_runners(sess = sess)

for i in range(1000):

image_batch, label_batch = sess.run([img_train, label_train])

label_b = np.eye(6, dtype =float)[label_batch]

train_step.run(feed_dict = {x:image_batch, y:label_b},session = sess)

if i%20 == 0:

train_accuracy = accuracy.eval(feed_dict = {x:image_batch, y:label_b}, session = sess)

print("step %d, training accuracy %g" %(i, train_accuracy))

image_batch, label_batch = sess.run([img_test, label_test])

label_b = np.eye(6, dtype = float)[label_batch]

print("finished!test accuracy %g" %accuracy.eval(feed_dict = {x: image_batch, y:label_b}, session = sess))

你期待的結果是什么？實際看到的錯誤信息又是什么？

可以看到 這里的泛化能力還是挺弱的

是不是欠擬合了

還有數據集是不是個硬傷

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