一、CNN训练模型
模型尺寸分析：卷积层全都采用了补0，所以经过卷积层长和宽不变，只有深度加深。池化层全都没有补0，所以经过池化层长和宽均减小，深度不变。http://download.tensorflow.org/example_images/flower_photos.tgz
模型尺寸变化：100×100×3->100×100×32->50×50×32->50×50×64->25×25×64->25×25×128->12×12×128->12×12×128->6×6×128
CNN训练代码如下：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | from skimage import io,transformimport globimport osimport tensorflow as tfimport numpy as npimport time#数据集地址path='E:/data/datasets/flower_photos/'#模型保存地址model_path='E:/data/model/flower/model.ckpt'#将所有的图片resize成100*100w=100h=100c=3#读取图片def read_img(path): cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)] imgs=[] labels=[] for idx,folder in enumerate(cate): for im in glob.glob(folder+'/*.jpg'): print('reading the images:%s'%(im)) img=io.imread(im) img=transform.resize(img,(w,h)) imgs.append(img) labels.append(idx) return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)data,label=read_img(path)#打乱顺序num_example=data.shape[0]arr=np.arange(num_example)np.random.shuffle(arr)data=data[arr]label=label[arr]#将所有数据分为训练集和验证集ratio=0.8s=np.int(num_example*ratio)x_train=data[:s]y_train=label[:s]x_val=data[s:]y_val=label[s:]#-----------------构建网络----------------------#占位符x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x')y_=tf.placeholder(tf.int32,shape=[None,],name='y_')def inference(input_tensor, train, regularizer): with tf.variable_scope('layer1-conv1'): conv1_weights = tf.get_variable("weight",[5,5,3,32],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0)) conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases)) with tf.name_scope("layer2-pool1"): pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="VALID") with tf.variable_scope("layer3-conv2"): conv2_weights = tf.get_variable("weight",[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0)) conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases)) with tf.name_scope("layer4-pool2"): pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') with tf.variable_scope("layer5-conv3"): conv3_weights = tf.get_variable("weight",[3,3,64,128],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0)) conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME') relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases)) with tf.name_scope("layer6-pool3"): pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') with tf.variable_scope("layer7-conv4"): conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0)) conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME') relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases)) with tf.name_scope("layer8-pool4"): pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') nodes = 6*6*128 reshaped = tf.reshape(pool4,[-1,nodes]) with tf.variable_scope('layer9-fc1'): fc1_weights = tf.get_variable("weight", [nodes, 1024], initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights)) fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1)) fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases) if train: fc1 = tf.nn.dropout(fc1, 0.5) with tf.variable_scope('layer10-fc2'): fc2_weights = tf.get_variable("weight", [1024, 512], initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights)) fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1)) fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases) if train: fc2 = tf.nn.dropout(fc2, 0.5) with tf.variable_scope('layer11-fc3'): fc3_weights = tf.get_variable("weight", [512, 5], initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights)) fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1)) logit = tf.matmul(fc2, fc3_weights) + fc3_biases return logit#---------------------------网络结束---------------------------regularizer = tf.contrib.layers.l2_regularizer(0.0001)logits = inference(x,False,regularizer)#(小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensorb = tf.constant(value=1,dtype=tf.float32)logits_eval = tf.multiply(logits,b,name='logits_eval') loss=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)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))#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]#训练和测试数据，可将n_epoch设置更大一些n_epoch=10 batch_size=64saver=tf.train.Saver()sess=tf.Session() sess.run(tf.global_variables_initializer())for epoch in range(n_epoch): start_time = time.time() #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" % (np.sum(train_loss)/ n_batch)) print(" train acc: %f" % (np.sum(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" % (np.sum(val_loss)/ n_batch)) print(" validation acc: %f" % (np.sum(val_acc)/ n_batch))saver.save(sess,model_path)sess.close() |
二、调用模型进行预测
调用模型进行花卉的预测，代码如下：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | from skimage import io,transformimport tensorflow as tfimport numpy as nppath1 = "E:/data/datasets/flower_photos/daisy/5547758_eea9edfd54_n.jpg"path2 = "E:/data/datasets/flower_photos/dandelion/7355522_b66e5d3078_m.jpg"path3 = "E:/data/datasets/flower_photos/roses/394990940_7af082cf8d_n.jpg"path4 = "E:/data/datasets/flower_photos/sunflowers/6953297_8576bf4ea3.jpg"path5 = "E:/data/datasets/flower_photos/tulips/10791227_7168491604.jpg"flower_dict = {0:'dasiy',1:'dandelion',2:'roses',3:'sunflowers',4:'tulips'}w=100h=100c=3def read_one_image(path): img = io.imread(path) img = transform.resize(img,(w,h)) return np.asarray(img)with tf.Session() as sess: data = [] data1 = read_one_image(path1) data2 = read_one_image(path2) data3 = read_one_image(path3) data4 = read_one_image(path4) data5 = read_one_image(path5) data.append(data1) data.append(data2) data.append(data3) data.append(data4) data.append(data5) saver = tf.train.import_meta_graph('E:/data/model/flower/model.ckpt.meta') saver.restore(sess,tf.train.latest_checkpoint('E:/data/model/flower/')) graph = tf.get_default_graph() x = graph.get_tensor_by_name("x:0") feed_dict = {x:data} logits = graph.get_tensor_by_name("logits_eval:0") classification_result = sess.run(logits,feed_dict) #打印出预测矩阵 print(classification_result) #打印出预测矩阵每一行最大值的索引 print(tf.argmax(classification_result,1).eval()) #根据索引通过字典对应花的分类 output = [] output = tf.argmax(classification_result,1).eval() for i in range(len(output)): print("第",i+1,"朵花预测:"+flower_dict[output[i]]) |
运行结果：
1 2 3 4 5 6 7 8 9 10 11 | [[ 5.76620245 3.18228579 -3.89464641 -2.81310582 1.40294015] [ -1.01490593 3.55570269 -2.76053429 2.93104005 -3.47138596] [ -8.05292606 -7.26499033 11.70479774 0.59627819 2.15948296] [ -5.12940931 2.18423128 -3.33257103 9.0591135 5.03963232] [ -4.25288343 -0.95963973 -2.33347392 1.54485476 5.76069307]][0 1 2 3 4]第 1 朵花预测:dasiy第 2 朵花预测:dandelion第 3 朵花预测:roses第 4 朵花预测:sunflowers第 5 朵花预测:tulips |
预测结果和调用模型代码中的五个路径相比较是完全准确的。
本文的模型对于花卉的分类准确率大概在70%左右，采用迁移学习调用Inception-v3模型对本文中的花卉数据集分类准确率在95%左右。主要的原因在于本文的CNN模型较于简单，而且花卉数据集本身就比mnist手写数字数据集分类难度就要大一点，同样的模型在mnist手写数字的识别上准确率要比花卉数据集准确率高不少。
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