该模块包含几部分：
调用训练好的并且已经保存的CNN模型（仅四层卷积层部分）逐个读取tfrecords文件中的元素，并送入已训练好的CNN中，给每个图片提取128个特征每首歌包含11个图片，即11*128个特征，将每首歌的11*128个特征之间进行余弦相似度计算逐个歌曲计算，返回每个歌曲的最相似的三首歌歌名，以列表的形式
调用训练好的并且已经保存的CNN模型（仅四层卷积层部分）
定义CNN模型的参数lr = tf.Variable(0.001, dtype=tf.float32)x = tf.placeholder(tf.float32, [None, 256, 256, 1],name='x')y_ = tf.placeholder(tf.float32, [None],name='y_')keep_prob = tf.placeholder(tf.float32)
CNN模型结构定义
def weight_variable(shape,name):initial = tf.truncated_normal(shape, stddev=0.1)return tf.Variable(initial,name=name)def bias_variable(shape,name):initial = tf.constant(0.1, shape=shape)return tf.Variable(initial,name=name)with tf.name_scope('conv2d'):def conv2d(x, W):# stride [1, x_movement, y_movement, 1]# Must have strides[0] = strides[3] = 1return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')with tf.name_scope('max_pool_2x2'):def max_pool_2x2(x):# stride [1, x_movement, y_movement, 1]return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')def max_pool_4x4(x):# stride [1, x_movement, y_movement, 1]return tf.nn.max_pool(x, ksize=[1,4,4,1], strides=[1,4,4,1], padding='SAME') def define_predict_y(x):with tf.variable_scope("conv1"):## conv1 layer ##W_conv1 = weight_variable([3,3, 1,64],'W_conv1') # patch 3x3, in size 1, out size 64b_conv1 = bias_variable([64],'b_conv1')h_conv1 = tf.nn.elu(conv2d(x, W_conv1) + b_conv1) # output size 28x28x32h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') # output size 14x14x32with tf.variable_scope("conv2"):## conv2 layer ##W_conv2 = weight_variable([3,3, 64, 128],'W_conv2') # patch 5x5, in size 32, out size 64b_conv2 = bias_variable([128],'b_conv2')h_conv2 = tf.nn.elu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64h_pool2 = max_pool_4x4(h_conv2) with tf.variable_scope("conv3"):## conv3 layer ##W_conv3 = weight_variable([3,3, 128, 256],'W_conv3') # patch 5x5, in size 32, out size 64b_conv3 = bias_variable([256],'b_conv3')h_conv3 = tf.nn.elu(conv2d(h_pool2, W_conv3) + b_conv3) # output size 14x14x64h_pool3 = max_pool_4x4(h_conv3) with tf.variable_scope("conv4"):## conv4 layer ##W_conv4 = weight_variable([3,3, 256, 512],'W_conv4') # patch 5x5, in size 32, out size 64b_conv4 = bias_variable([512],'b_conv4')h_conv4 = tf.nn.elu(conv2d(h_pool3, W_conv4) + b_conv4) # output size 14x14x64h_pool4 = max_pool_4x4(h_conv4) with tf.variable_scope("fc1"):## fc1 layer ##W_fc1 = weight_variable([2*2*512, 128],'W_fc1')b_fc1 = bias_variable([128],'b_fc1')# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]h_pool4_flat = tf.reshape(h_pool4, [-1, 2*2*512])h_fc1 = tf.nn.elu(tf.matmul(h_pool4_flat, W_fc1) + b_fc1)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)# ## fc2 layer ### with tf.variable_scope("fc2"):# W_fc2 = weight_variable([128, 10],'W_fc2')# b_fc2 = bias_variable([10],'b_fc2')# predict_y = tf.matmul(h_fc1_drop, W_fc2) + b_fc2return h_fc1_dropprediction = define_predict_y(x)# 用于保存和载入模型new_saver=tf.train.Saver()
载入已经保存的模型参数
new_saver.restore(sess, tf.train.latest_checkpoint('C:/Users/Administrator/Desktop/ckpt/'))print("导入参数成功！")
逐个读取tfrecords文件中的元素，并送入已训练好的CNN中，给每个图片提取128个特征
1.逐个读取tfrecords文件中的元素
def _parse_record(example_proto):features = {'encoded': tf.FixedLenFeature((), tf.string),'fname': tf.FixedLenFeature((), tf.string),'width': tf.FixedLenFeature((), tf.int64),'height': tf.FixedLenFeature((), tf.int64),'label': tf.FixedLenFeature((), tf.int64),}parsed_features = tf.parse_single_example(example_proto, features=features)return parsed_features###1.....img_vec_list = [] #所有图片的向量，按顺序存的def read_test(input_file):# 用 dataset 读取 tfrecord 文件dataset = tf.data.TFRecordDataset(input_file)dataset = dataset.map(_parse_record)#解析tfrecord文件中的所有记录，使用dataset的map方法#dataset = dataset.repeat(epochs).shuffle(buffer_size).batch(batch_size)iterator = dataset.make_one_shot_iterator()with tf.Session() as sess:try:i =0while iterator.get_next():i = i+1print(i)features = sess.run(iterator.get_next())img_fname = features['fname']img_fname = img_fname.decode()img = tf.decode_raw(features['encoded'], tf.uint8)img = tf.reshape(img, [256, 256, 1])img = tf.cast(img, tf.float32) / 255.0 #将矩阵归一化0-1之间label = tf.cast(features['label'], tf.int32)one = [sess.run(img),img_fname,sess.run(label)]print(one[1])img_vec_list.append(one)except tf.errors.OutOfRangeError:print("..")print("-------------",len(img_vec_list))img_vec_list.sort(key = lambda x:x[1])print("over..")read_test('F:/data/test0.tfrecords') read_test('F:/data/train0.tfrecords') read_test('F:/data/test1.tfrecords') read_test('F:/data/train1.tfrecords') read_test('F:/data/test2.tfrecords') read_test('F:/data/train2.tfrecords') read_test('F:/data/test3.tfrecords') read_test('F:/data/train3.tfrecords') read_test('F:/data/test4.tfrecords') read_test('F:/data/train4.tfrecords') read_test('F:/data/test5.tfrecords') read_test('F:/data/train5.tfrecords') read_test('F:/data/test6.tfrecords') read_test('F:/data/train6.tfrecords') read_test('F:/data/test7.tfrecords') read_test('F:/data/train7.tfrecords') read_test('F:/data/test8.tfrecords') read_test('F:/data/train8.tfrecords') read_test('F:/data/test9.tfrecords') read_test('F:/data/train9.tfrecords')
2.并送入已训练好的CNN中
vector_list = []def get_vector():with tf.Session() as sess:print("there..")# 如果是训练，初始化参数sess.run(tf.global_variables_initializer())print("222")# 创建一个协调器，管理线程coord = tf.train.Coordinator()print("333")# 启动QueueRunner,此时文件名队列已经进队threads = tf.train.start_queue_runners(sess=sess, coord=coord)print("444")new_saver.restore(sess, tf.train.latest_checkpoint('C:/Users/Administrator/Desktop/ckpt/'))print("导入参数成功！")for i in range(len(img_vec_list)):vector = sess.run(prediction,feed_dict={x:np.expand_dims(img_vec_list[i][0],0),y_:np.expand_dims(img_vec_list[i][2],0),keep_prob:0.5})vector_list.append(vector)#print("vector is :",len(vector[0]))get_vector()
每首歌包含11个图片，即11*128个特征，将每首歌的11*128个特征之间进行余弦相似度计算
def cos_sim(vector_a, vector_b):"""计算两个向量之间的余弦相似度:param vector_a: 向量 a :param vector_b: 向量 b:return: sim"""vector_a = np.mat(vector_a)vector_b = np.mat(vector_b)num = float(vector_a * vector_b.T)denom = np.linalg.norm(vector_a) * np.linalg.norm(vector_b)cos = num / denomsim = 0.5 + 0.5 * cosreturn sim##########3....cos_list = []def get_all_vec_cos():for i in range(len(img_vec_list)):max_cos = 0max_index = ifor j in range(len(img_vec_list)):if int(i/11) == int(j/11):continueelse:temp_cos = cos_sim(vector_list[i],vector_list[j])if temp_cos>max_cos:print("temp_cos:",temp_cos,"max_cos",max_cos)max_cos = temp_cosmax_index = int(j/11)cos_list.append([int(i/11),max_index,max_cos])print("cos:",i," ",cos_list[i])print("cos_list:",len(cos_list))get_all_vec_cos()
逐个歌曲计算，返回每个歌曲的最相似的三首歌歌名，以列表的形式
most_video = []#返回的是vidoe序号def get_most_video():#将cos_list分割,每份11个#cos_list = [cos_list[i:i+11] for i in range(0,len(cos_list),11)]print("cos_list:",cos_list)split_cos_list = []for j in range(0,len(cos_list),11):split_cos_list.append(cos_list[j:j+11])print("split_cos_list:",split_cos_list)for i in range(len(split_cos_list)):index = []for item in split_cos_list[i]:index.append(item[1])most_index = Counter(index).most_common(3)most_video.append(most_index)#print("most_video:",len(most_video))get_most_video()#print(most_video)
基于深度学习的音乐推荐系统（三）使用已训练的卷积神经网络提取语谱图特征并计算图像间相似度
