alexnet源码：
https://github.com/kratzert/finetune_alexnet_with_tensorflow
上述代码无运行错误，但是无法给出正确的识别结果，解决方案如下：
import os import cv2 import numpy as np import tensorflow as tf import matplotlib.pyplot as plt #mean of imagenet dataset in BGR imagenet_mean = np.array([104., 117., 124.], dtype=np.float32) current_dir = os.getcwd() image_dir = os.path.join(current_dir, 'images') #get list of all images img_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.jpeg')] #load all images imgs = [] for f in img_files: imgs.append(cv2.imread(f)) #plot images fig = plt.figure(figsize=(15,6)) for i, img in enumerate(imgs): fig.add_subplot(1,3,i+1) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.axis('off') from alexnet import AlexNet from caffe_classes import class_names #placeholder for input and dropout rate x = tf.placeholder(tf.float32, [1, 227, 227, 3]) keep_prob = tf.placeholder(tf.float32) #create model with default config ( == no skip_layer and 1000 units in the last layer) model = AlexNet(x, keep_prob, 1000, []) #define activation of last layer as score score = model.fc8 #create op to calculate softmax softmax = tf.nn.softmax(score) with tf.Session() as sess: # Initialize all variables sess.run(tf.global_variables_initializer()) # Load the pretrained weights into the model #**********请注意一下代码************ ''' 执行以下代码才能真正的完成变量的赋值 ''' sess.run(model.load_initial_weights(sess)) # Loop over all images for i, image in enumerate(imgs): # Convert image to float32 and resize to (227x227) img = cv2.resize(image.astype(np.float32), (227,227)) # Subtract the ImageNet mean img -= imagenet_mean # Reshape as needed to feed into model img = img.reshape((1,227,227,3)) # Run the session and calculate the class probability probs = sess.run(softmax, feed_dict={x: img, keep_prob: 1}) # Get the class name of the class with the highest probability class_name = class_names[np.argmax(probs)]
