模型训练
from skimage import io, transform import glob import os import tensorflow as tf import numpy as np import time # 数据集地址 # path = '/home/zhang/input_data_s/' #path = 'D:/zhang/input_data/' path='F:/shuye/input_data/' # 模型保存地址 # model_path = '/home/zhang/save1/model.ckpt' model_path = 'F:/shuye/save1/model.ckpt' # 将所有的图片resize成100*100 w = 100 h = 100 c = 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 = 0.8 s = np.int(num_example * ratio) x_train = data y_train = label x_val = data y_val = label # -----------------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_) 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 = 50 batch_size = 64 summary_op = tf.summary.merge_all() # 产生一个会话 sess = tf.Session() # 产生一个writer来写log文件 train_writer = tf.summary.FileWriter('logs/', sess.graph) 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()
生成.pb模型
import tensorflow as tf import numpy as np import PIL.Image as Image from skimage import io, transform def recognize(jpg_path, pb_file_path): with tf.Graph().as_default(): output_graph_def = tf.GraphDef() with open(pb_file_path, "rb") as f: output_graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(output_graph_def, name="") with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) input_x = sess.graph.get_tensor_by_name("input:0") print (input_x) out_softmax = sess.graph.get_tensor_by_name("softmax_linear:0") print (out_softmax) out_label = sess.graph.get_tensor_by_name("output:0") print (out_label) img = io.imread(jpg_path) img = transform.resize(img, (60, 60, 3)) img_out_softmax = sess.run(out_softmax, feed_dict={input_x:np.reshape(img, [1, 60, 60, 3])}) print ("img_out_softmax:",img_out_softmax) prediction_labels = np.argmax(img_out_softmax, axis=1) print ("label:",prediction_labels) recognize("/home/zhang/input_data/tulips/3202130001. ", "/home/zhang/Downloads/model/expert-graph.pb")
