我的程序是训练一个基于Alexnet的网络判断卫星图像中是否有车辆 我自己尝试过但不会编写利用已保存的网络进行测试的程序 应该不是特别困难希望有大佬能帮帮忙 qq微信都是850504687
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import time
import creat_and_read_TFReacord as reader
import os
x_train,y_train=reader.get_file('dogs_vs_cats')
image_batch,label_batch=reader.get_batch(x_train,y_train,227,227,50,2048)
#Batch_Normalization正则化
def batch_norm(inputs,is_train,is_conv_out=True,decay=0.999):
scale=tf.Variable(tf.ones([inputs.get_shape()[-1]]))
beta = tf.Variable(tf.zeros([inputs.get_shape()[-1]]))
pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), trainable=False)
pop_var = tf.Variable(tf.ones([inputs.get_shape()[-1]]), trainable=False)
if is_train:
if is_conv_out:
batch_mean, batch_var = tf.nn.moments(inputs, [0, 1, 2])
else:
batch_mean, batch_var = tf.nn.moments(inputs, [0])
train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))
train_var = tf.assign(pop_var, pop_var * decay + batch_var * (1 - decay))
with tf.control_dependencies([train_mean, train_var]):
return tf.nn.batch_normalization(inputs,
batch_mean, batch_var, beta, scale, 0.001)
else:
return tf.nn.batch_normalization(inputs,
pop_mean, pop_var, beta, scale, 0.001)
with tf.device('/gpu:0'):
# 模型参数
learning_rate = 1e-4
training_iters = 200
batch_size = 32
display_step = 5
n_classes = 2
n_fc1 = 4096
n_fc2 = 2048
# 构建模型
x = tf.placeholder(tf.float32, [None, 227, 227, 3])
y = tf.placeholder(tf.float32, [None, n_classes])
W_conv = {
'conv1': tf.Variable(tf.truncated_normal([11, 11, 3, 96], stddev=0.0001)),
'conv2': tf.Variable(tf.truncated_normal([5, 5, 96, 256], stddev=0.01)),
'conv3': tf.Variable(tf.truncated_normal([3, 3, 256, 384], stddev=0.01)),
'conv4': tf.Variable(tf.truncated_normal([3, 3, 384, 384], stddev=0.01)),
'conv5': tf.Variable(tf.truncated_normal([3, 3, 384, 256], stddev=0.01)),
'fc1': tf.Variable(tf.truncated_normal([6 * 6 * 256, n_fc1], stddev=0.1)),
'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)),
'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=0.1))
}
b_conv = {
'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[96])),
'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[256])),
'conv3': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[384])),
'conv4': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[384])),
'conv5': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[256])),
'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])),
'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc2])),
'fc3': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[n_classes]))
}
x_image = tf.reshape(x, [-1, 227, 227, 3])
# 卷积层 1
conv1 = tf.nn.conv2d(x_image, W_conv['conv1'], strides=[1, 4, 4, 1], padding='VALID')
conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])
conv1 = batch_norm(conv1, True)
conv1 = tf.nn.relu(conv1)
# 池化层 1
pool1 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
norm1 = tf.nn.lrn(pool1, 5, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
# 卷积层 2
conv2 = tf.nn.conv2d(pool1, W_conv['conv2'], strides=[1, 1, 1, 1], padding='SAME')
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = batch_norm(conv2, True)
conv2 = tf.nn.relu(conv2)
# 池化层 2
pool2 = tf.nn.avg_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
# 卷积层3
conv3 = tf.nn.conv2d(pool2, W_conv['conv3'], strides=[1, 1, 1, 1], padding='SAME')
conv3 = tf.nn.bias_add(conv3, b_conv['conv3'])
conv3 = batch_norm(conv3, True)
conv3 = tf.nn.relu(conv3)
# 卷积层4
conv4 = tf.nn.conv2d(conv3, W_conv['conv4'], strides=[1, 1, 1, 1], padding='SAME')
conv4 = tf.nn.bias_add(conv4, b_conv['conv4'])
conv4 = batch_norm(conv4, True)
conv4 = tf.nn.relu(conv4)
# 卷积层5
conv5 = tf.nn.conv2d(conv4, W_conv['conv5'], strides=[1, 1, 1, 1], padding='SAME')
conv5 = tf.nn.bias_add(conv5, b_conv['conv5'])
conv5 = batch_norm(conv5, True)
conv5 = tf.nn.relu(conv5)
# 池化层5
pool5 = tf.nn.avg_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
reshape = tf.reshape(pool5, [-1, 6 * 6 * 256])
fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv['fc1'])
fc1 = batch_norm(fc1, True, False)
fc1 = tf.nn.relu(fc1)
# 全连接层 2
fc2 = tf.add(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2'])
fc2 = batch_norm(fc2, True, False)
fc2 = tf.nn.relu(fc2)
fc3 = tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3'])
# 定义损失
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=fc3))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
# 评估模型
correct_pred = tf.equal(tf.argmax(fc3,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
def onehot(labels):
'''one-hot 编码'''
n_sample = len(labels)
n_class = max(labels) + 1
onehot_labels = np.zeros((n_sample, n_class))
onehot_labels[np.arange(n_sample), labels] = 1
return onehot_labels
save_model = ".//model//AlexNetModel.ckpt"
def train(opech):
with tf.Session() as sess:
sess.run(init)
train_writer = tf.summary.FileWriter(".//log", sess.graph) # 输出日志的地方
saver = tf.train.Saver()
c = []
start_time = time.time()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
step = 0
for i in range(opech):
step = i
image, label = sess.run([image_batch, label_batch])
labels = onehot(label)
acc=[]
sess.run(optimizer, feed_dict={x: image, y: labels})
loss_record = sess.run(loss, feed_dict={x: image, y: labels})
acc=sess.run(accuracy,feed_dict={x:image,y:labels})
print("now the loss is %f " % loss_record)
print("now the accuracy is %f "%acc)
c.append(loss_record)
end_time = time.time()
print('time: ', (end_time - start_time))
start_time = end_time
print("---------------%d onpech is finished-------------------" % i)
print("Optimization Finished!")
# checkpoint_path = os.path.join(".//model", 'model.ckpt') # 输出模型的地方
saver.save(sess, save_model)
print("Model Save Finished!")
coord.request_stop()
coord.join(threads)
plt.plot(c)
plt.xlabel('Iter')
plt.ylabel('loss')
plt.title('lr=%f, ti=%d, bs=%d' % (learning_rate, training_iters, batch_size))
plt.tight_layout()
plt.savefig('cat_and_dog_AlexNet.jpg', dpi=200)
train(training_iters)
我的训练主程序在上面 想要一个用于利用已经训练好的模型进行卫星图像中是否有车的预测 希望有大佬能帮忙
https://blog.csdn.net/zryowen123/article/details/79889988 这篇文档不知道能不能解决你的问题
你好，请明确测试任务，是测试准确度吗？最终得到什么结果
@天空skyang 测试任务就是得出 图片1：（有车的概率，没有车的概率）图片2：（有车的概率，没有车的概率）……
或者直接输出概率大的那种结果 两种都想要
Keras框架可以吗，这个不难啊，网上能找到
测试：1，model = model.load() # 加载模型
2，acc = model。predicte（img，lab） # 测试图像
大佬能根据我的程序给出一个直接能用的代码不 直接改个文件路径就能用的那种
请问有大佬能根据我的程序帮我写一个直接能用来测试的代码吗 改个文件路径就能用的那种 万分感谢
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
preValue = sess.run(preValue, feed_dict={x:testPicArr})