from PIL import Image
import matplotlib.pyplot as plt
#
def get_one_image(train):
'''Randomly pick one image from training data
Return: ndarray
'''
n = len(train)
ind = np.random.randint(0, n)
img_dir = train[ind]
image = Image.open(img_dir)
plt.imshow(image)
image = image.resize([64, 64])
image = np.array(image)
return image
def evaluate_one_image():
'''Test one image against the saved models and parameters
'''
# you need to change the directories to yours.
train_dir = '/Users/Desktop/cd/cd/train/' #存放验证的图片
train, train_label = input_data.get_files(train_dir)
image_array = get_one_image(train)
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 2
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 64, 64, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[64, 64, 3])
# you need to change the directories to yours.
logs_train_dir = '/Users/Desktop/cd/cd/logs' #数据集
saver = tf.train.Saver()
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
if max_index==0:
print('This is a car with possibility %.6f' %prediction[:, 0])
else:
print('This is a not_car with possibility %.6f' %prediction[:, 1])
