tensorflow 用自己的数据集训练CNN模型

转载：https://blog.csdn.net/m0_37167788/article/details/79066487
<https://blog.csdn.net/m0_37167788/article/details/79066487>


最近用tensorflow训练自己的模型的时候发现，tensorflow官网上所给的例子，都是用处理好数据格式的mnist数据或者其他格式的数据，所以在训练自己的模型的时候的第一步就卡住了。所以上网搜索了相关的资料之后便得出了相关的解决方案（有好几种，这里只说明一种，另外有TFRecord的格式的网上很多教程，将不在这叙述）….

 
import os import glob import time import numpy as np import tensorflow as tf
from skimage import io, transform # os.environ["TF_CPP_MIN_LOG_LEVEL"] = '1' #
这是默认的显示等级，显示所有信息 # os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2' # 只显示 warning 和
Error os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3' # 只显示 Error # 读取图片 def
read_img(path, w, h): cate = [path + x for x in os.listdir(path) if
os.path.isdir(path + x)] # print(cate) imgs = [] labels = [] print('Start read
the image ...') for index, folder in enumerate(cate): # print(index, folder)
for im in glob.glob(folder + '/*.jpg'): # print('Reading The Image: %s' % im)
img = io.imread(im) img = transform.resize(img, (w, h)) imgs.append(img)
labels.append(index) print('Finished ...') return np.asarray(imgs, np.float32),
np.asarray(labels, np.float32) # 打乱顺序 def messUpOrder(data, label): num_example
= data.shape[0] arr = np.arange(num_example) np.random.shuffle(arr) data =
data[arr] label = label[arr] return data, label # 将所有数据分为训练集和验证集 def
segmentation(data, label, ratio=0.8): num_example = data.shape[0] s =
np.int(num_example * ratio) x_train = data[:s] y_train = label[:s] x_val =
data[s:] y_val = label[s:] return x_train, y_train, x_val, y_val # 构建网络 def
buildCNN(w, h, c): # 占位符 x = tf.placeholder(tf.float32, shape=[None, w, h, c],
name='x') y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_') # 第一个卷积层 +
池化层（100——>50) conv1 = tf.layers.conv2d( inputs=x, filters=32, kernel_size=[5,
5], padding="same", activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool1 =
tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # 第二个卷积层 +
池化层 (50->25) conv2 = tf.layers.conv2d( inputs=pool1, filters=64,
kernel_size=[5, 5], padding="same", activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool2 =
tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # 第三个卷积层 +
池化层 (25->12) conv3 = tf.layers.conv2d( inputs=pool2, filters=128,
kernel_size=[3, 3], padding="same", activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool3 =
tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2) # 第四个卷积层 +
池化层 (12->6) conv4 = tf.layers.conv2d( inputs=pool3, filters=128,
kernel_size=[3, 3], padding="same", activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool4 =
tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2) re1 =
tf.reshape(pool4, [-1, 6 * 6 * 128]) # 全连接层 dense1 =
tf.layers.dense(inputs=re1, units=1024, activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003)) dense2 =
tf.layers.dense(inputs=dense1, units=512, activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003)) logits =
tf.layers.dense(inputs=dense2, units=20, activation=None,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003)) return logits, x,
y_ # 返回损失函数的值，准确值等参数 def accCNN(logits, y_): loss =
tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=logits) train_op =
tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) correct_prediction =
tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_) acc =
tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) return loss, train_op,
correct_prediction, acc # 定义一个函数，按批次取数据 def minibatches(inputs=None,
targets=None, batch_size=None, shuffle=False): assert len(inputs) ==
len(targets) if shuffle: indices = np.arange(len(inputs))
np.random.shuffle(indices) for start_idx in range(0, len(inputs) - batch_size +
1, batch_size): if shuffle: excerpt = indices[start_idx:start_idx + batch_size]
else: excerpt = slice(start_idx, start_idx + batch_size) yield inputs[excerpt],
targets[excerpt] def runable(x_train, y_train, train_op, loss, acc, x, y_,
x_val, y_val): # 训练和测试数据，可将n_epoch设置更大一些 n_epoch = 50 batch_size = 64 sess =
tf.InteractiveSession() sess.run(tf.global_variables_initializer()) for epoch
in range(n_epoch): # training train_loss, train_acc, n_batch = 0, 0, 0 for
x_train_a, y_train_a in minibatches(x_train, y_train, batch_size,
shuffle=True): _, err, ac = sess.run([train_op, loss, acc], feed_dict={x:
x_train_a, y_: y_train_a}) train_loss += err train_acc += ac n_batch += 1
print("train loss: %f" % (train_loss / n_batch)) print("train acc: %f" %
(train_acc / n_batch)) # validation val_loss, val_acc, n_batch = 0, 0, 0 for
x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False): err,
ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a}) val_loss += err
val_acc += ac n_batch += 1 print("validation loss: %f" % (val_loss / n_batch))
print("validation acc: %f" % (val_acc / n_batch)) print('*' * 50) sess.close()
if __name__ == '__main__': imgpath = '../dataset/classify/' w = 100 h = 100 c =
3 ratio = 0.8 # 选取训练集的比例 data, label = read_img(path=imgpath, w=w, h=h) data,
label = messUpOrder(data=data, label=label) x_train, y_train, x_val, y_val =
segmentation(data=data, label=label, ratio=ratio) logits, x, y_ = buildCNN(w=w,
h=h, c=c) loss, train_op, correct_prediction, acc = accCNN(logits=logits,
y_=y_) runable(x_train=x_train, y_train=y_train, train_op=train_op, loss=loss,
acc=acc, x=x, y_=y_, x_val=x_val, y_val=y_val)
 

• « 上一篇：ArcGIS 要素融合工具（dissolve）使用方法
• » 下一篇：计算机操作系统第三章测试题及答案