def test(image_arr):
with tf.Graph().as_default():
image = tf.cast(image_arr, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 64, 64, 3])
# print(image.shape)
p = deep_CNN(image, 1, N_CLASSES)
logits = tf.nn.softmax(p)
x = tf.placeholder(tf.float32, shape=[64, 64, 3])
saver = tf.train.Saver()
sess = tf.Session()
ckpt = tf.train.get_checkpoint_state(log_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
# 调用saver.restore()函数，加载训练好的网络模型
print('Loading success')
prediction = sess.run(logits, feed_dict={x: image_arr})
max_index = np.argmax(prediction)
print('预测的标签为：', max_index, lists[max_index])
print('预测的结果为：', prediction)
def test(image_arr):
with tf.Graph().as_default():
image = tf.cast(image_arr, tf.float32)
# image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 28, 28, 1])
# print(image.shape)
p = deep_CNN(image, 1, 17)
logits = tf.nn.softmax(p)
x = tf.placeholder(tf.float32, shape=[28, 28]) # , 1])
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(log_dir)
if ckpt and ckpt.model_checkpoint_path:
# print(ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
# 调用saver.restore()函数，加载训练好的网络模型
#print('Loading success')
prediction = sess.run(logits, feed_dict={x: image_arr})
max_index = np.argmax(prediction)
# print('预测的标签为：', max_index, lists[max_index])
# print('预测的结果为：', prediction)
return max_index
N_CLASSES = 17
IMG_W = 28 # resize图像，太大的话训练时间久
IMG_H = 28
BATCH_SIZE = 40 # 每个batch要放多少张图片
CAPACITY = 20 # 一个队列最大多少
MAX_STEP = 25000
learning_rate = 0.00001 # 一般小于0.0001
# 获取批次batch
train_dir = './DataSet' # 训练样本的读入路径
logs_train_dir = './final' #logs存储路径
train, train_label = get_file(train_dir)
train_batch, train_label_batch = get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
train_logits = deep_CNN(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = losses(train_logits, train_label_batch)
train_op = trainning(train_loss, learning_rate)
train_acc = evaluation(train_logits, train_label_batch)
summary_op = tf.summary.merge_all()
sess = tf.Session()
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator() # 设置多线程协调器
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# 进行batch的训练
try:
# 执行MAX_STEP步的训练，一步一个batch
