# -*- coding=UTF-8 -*- import os import tensorflow as tf import numpy as np import time from PIL import Image w = 64 h = 64 c = 3 TOTAL_TYPE = 5 path = 'D:/tensorflow/imgaes/' model_path='D:/tensorflow/saver/model.ckpt' def read_source(): imgs,labels = [],[] for dir in os.listdir(path): idx = int(dir) folder = os.path.join(path,dir) zeros = np.zeros(TOTAL_TYPE) zeros[idx]=1 print("folder :%s"%(folder)) total = 0 for f in os.listdir(folder): file = os.path.join(folder,f) image = Image.open(file).convert('RGB').resize((w, h), Image.ANTIALIAS) arr = np.asarray(image) imgs.append(arr) labels.append(zeros) total += 1 print(total) return np.asarray(imgs,np.float32),np.asarray(labels,np.int32) data,label=read_source() 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 = 0.8 s = 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---------------------- x = tf.placeholder(tf.float32, shape=[None, w, h, 3], name='x') y_ = tf.placeholder(tf.float32, shape=[None,5 ], name='y_') def inference(input_tensor, train, regularizer): with tf.variable_scope('layer1-conv1'): conv1_weights = tf.get_variable("weight", [5, 5, 3, 64], initializer=tf.truncated_normal_initializer(stddev=0.1)) conv1_biases = tf.get_variable("bias", [64], 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)) relu1 = tf.layers.batch_normalization(relu1,training=train) with tf.name_scope("layer2-pool1"): pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") with tf.variable_scope("layer3-conv2"): conv2_weights = tf.get_variable("weight", [3, 3, 64, 128], initializer=tf.truncated_normal_initializer(stddev=0.1)) conv2_biases = tf.get_variable("bias", [128], 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)) relu2 = tf.layers.batch_normalization(relu2, training=train) with tf.name_scope("layer4-pool2"): pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') with tf.variable_scope("layer5-conv3"): conv3_weights = tf.get_variable("weight", [3, 3, 128, 256], initializer=tf.truncated_normal_initializer(stddev=0.1)) conv3_biases = tf.get_variable("bias", [256], 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)) relu3 = tf.layers.batch_normalization(relu3, training=train) with tf.name_scope("layer8-pool4"): pool4 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') norm4 = tf.nn.lrn(pool4, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) nodes = 8 * 8* 256 reshaped = tf.reshape(norm4, [-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, 0.8) 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, 0.8) 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tensor b = 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_) # loss = -tf.reduce_sum(y_*tf.log(logits)) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,labels=y_)) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) correct_prediction = tf.equal(tf.argmax(logits, 1),tf.argmax(y_,1)) 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 = 200 batch_size = 64 saver = 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()