如何用Tensorflow训练模型成pb文件和和如何加载已经训练好的模型文件
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这篇薄荷主要是讲了如何用tensorflow去训练好一个模型，然后生成相应的pb文件。最后会将如何重新加载这个pb文件。
首先先放出PO主的github：
https://github.com/ppplinday/tensorflow-vgg16-train-and-test
其中的pitcute文件是狗和猫的图片分别15张一共30（别吐槽，只是为了练手学习的233333）， train那个就是训练的文件，test这个就是测试的文件。
接着PO主会慢慢讲解相应的步骤。
！！！ps：由于PO主也是新手，所以难免会出现一点（很多）小错误，希望大婶看了能够提出来让PO主好好学习233333。
- train
首先说一下train。一开始当然是读图片啦。
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 image: %s' % (im)) img = io.imread(im) img = transform.resize(img, (w, h, c)) imgs.append(img) labels.append(idx) return np.asarray(imgs, np.float32), np.asarray(labels, np.int32)data, label = read_img(path)用io.imread来读取每一张图片，然后resize成vgg的输入的大小（224，224，3），最后分别放入了data和label中。
num_example = data.shape[0]arr = np.arange(num_example)np.random.shuffle(arr)data = data[arr]label = label[arr]这里是把图片的顺序打乱，先生成一个等差数列，然后打乱，最后赋值回原来的data和label
ratio = 0.8s = np.int(num_example * ratio)x_train = data[:s]y_train = label[:s]x_val = data[s:]y_val = label[s:]全部的数据中百分之80的用来train，剩下20的用来test（虽然一共才30张图片。。。。。）
def build_network(height, width, channel): x = tf.placeholder(tf.float32, shape=[None, height, width, channel], name='input') y = tf.placeholder(tf.int64, shape=[None, 2], name='labels_placeholder')开始build相应的vgg model，这一步不难，但是每一层最好都给上相应的name。上面的x和y是相应的输入和相应的标签。
finaloutput = tf.nn.softmax(output_fc8, name="softmax") cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=finaloutput, labels=y)) optimize = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost) prediction_labels = tf.argmax(finaloutput, axis=1, name="output") read_labels = y correct_prediction = tf.equal(prediction_labels, read_labels) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) correct_times_in_batch = tf.reduce_sum(tf.cast(correct_prediction, tf.int32)) return dict( x=x, y=y, optimize=optimize, correct_prediction=correct_prediction, correct_times_in_batch=correct_times_in_batch, cost=cost,)在build的最后，是需要进行误差计算。finaloutput是最后的输出，cost是计算误差，optimize是定义训练时候安什么方式，也注意一下最后的return。
接着是训练过程。
def train_network(graph, batch_size, num_epochs, pb_file_path): init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) epoch_delta = 2 for epoch_index in range(num_epochs): for i in range(12): sess.run([graph['optimize']], feed_dict={ graph['x']: np.reshape(x_train[i], (1, 224, 224, 3)), graph['y']: ([[1, 0]] if y_train[i] == 0 else [[0, 1]])})其实训练的代码就这些，定好了batchsize和numepoch进行训练。下面的代码主要是为了看每几次相应的正确率。
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ["output"]) with tf.gfile.FastGFile(pb_file_path, mode='wb') as f:f.write(constant_graph.SerializeToString())这两句是重要的代码，用来把训练好的模型保存为pb文件。运行完之后就会发现应该的文件夹多出了一个pb文件。
- test
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="")打开相应的pb文件。
img = io.imread(jpg_path) img = transform.resize(img, (224, 224, 3)) img_out_softmax = sess.run(out_softmax, feed_dict={input_x:np.reshape(img, [-1, 224, 224, 3])})读取图片文件，resize之后放入模型的输入位置，之后img_out_softmax就是相应输出的结果。
这大概就是整个流程。目的是为了练练手，PO主应该有挺多小错误，希望大家能够提出来让PO主好好学习哈哈哈！！！
最后放出整个的train和test的代码：
train
from PIL import Imageimport numpy as npimport matplotlib.pyplot as pltimport matplotlib.image as mpimgimport tensorflow as tfimport osimport globfrom skimage import io, transformfrom tensorflow.python.framework import graph_utilimport collectionspath = '/home/zhoupeilin/vgg16/picture/'w = 224h = 224c = 3def 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 image: %s' % (im)) img = io.imread(im) img = transform.resize(img, (w, h, c)) imgs.append(img) labels.append(idx) return np.asarray(imgs, np.float32), np.asarray(labels, np.int32)data, label = read_img(path)num_example = data.shape[0]arr = np.arange(num_example)np.random.shuffle(arr)data = data[arr]label = label[arr]ratio = 0.8s = np.int(num_example * ratio)x_train = data[:s]y_train = label[:s]x_val = data[s:]y_val = label[s:]def build_network(height, width, channel): x = tf.placeholder(tf.float32, shape=[None, height, width, channel], name='input') y = tf.placeholder(tf.int64, shape=[None, 2], name='labels_placeholder') def weight_variable(shape, name="weights"): initial = tf.truncated_normal(shape, dtype=tf.float32, stddev=0.1) return tf.Variable(initial, name=name) def bias_variable(shape, name="biases"): initial = tf.constant(0.1, dtype=tf.float32, shape=shape) return tf.Variable(initial, name=name) def conv2d(input, w): return tf.nn.conv2d(input, w, [1, 1, 1, 1], padding='SAME') def pool_max(input): return tf.nn.max_pool(input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') def fc(input, w, b): return tf.matmul(input, w) + b # conv1 with tf.name_scope('conv1_1') as scope: kernel = weight_variable([3, 3, 3, 64]) biases = bias_variable([64]) output_conv1_1 = tf.nn.relu(conv2d(x, kernel) + biases, name=scope) with tf.name_scope('conv1_2') as scope: kernel = weight_variable([3, 3, 64, 64]) biases = bias_variable([64]) output_conv1_2 = tf.nn.relu(conv2d(output_conv1_1, kernel) + biases, name=scope) pool1 = pool_max(output_conv1_2) # conv2 with tf.name_scope('conv2_1') as scope: kernel = weight_variable([3, 3, 64, 128]) biases = bias_variable([128]) output_conv2_1 = tf.nn.relu(conv2d(pool1, kernel) + biases, name=scope) with tf.name_scope('conv2_2') as scope: kernel = weight_variable([3, 3, 128, 128]) biases = bias_variable([128]) output_conv2_2 = tf.nn.relu(conv2d(output_conv2_1, kernel) + biases, name=scope) pool2 = pool_max(output_conv2_2) # conv3 with tf.name_scope('conv3_1') as scope: kernel = weight_variable([3, 3, 128, 256]) biases = bias_variable([256]) output_conv3_1 = tf.nn.relu(conv2d(pool2, kernel) + biases, name=scope) with tf.name_scope('conv3_2') as scope: kernel = weight_variable([3, 3, 256, 256]) biases = bias_variable([256]) output_conv3_2 = tf.nn.relu(conv2d(output_conv3_1, kernel) + biases, name=scope) with tf.name_scope('conv3_3') as scope: kernel = weight_variable([3, 3, 256, 256]) biases = bias_variable([256]) output_conv3_3 = tf.nn.relu(conv2d(output_conv3_2, kernel) + biases, name=scope) pool3 = pool_max(output_conv3_3) # conv4 with tf.name_scope('conv4_1') as scope: kernel = weight_variable([3, 3, 256, 512]) biases = bias_variable([512]) output_conv4_1 = tf.nn.relu(conv2d(pool3, kernel) + biases, name=scope) with tf.name_scope('conv4_2') as scope: kernel = weight_variable([3, 3, 512, 512]) biases = bias_variable([512]) output_conv4_2 = tf.nn.relu(conv2d(output_conv4_1, kernel) + biases, name=scope) with tf.name_scope('conv4_3') as scope: kernel = weight_variable([3, 3, 512, 512]) biases = bias_variable([512]) output_conv4_3 = tf.nn.relu(conv2d(output_conv4_2, kernel) + biases, name=scope) pool4 = pool_max(output_conv4_3) # conv5 with tf.name_scope('conv5_1') as scope: kernel = weight_variable([3, 3, 512, 512]) biases = bias_variable([512]) output_conv5_1 = tf.nn.relu(conv2d(pool4, kernel) + biases, name=scope) with tf.name_scope('conv5_2') as scope: kernel = weight_variable([3, 3, 512, 512]) biases = bias_variable([512]) output_conv5_2 = tf.nn.relu(conv2d(output_conv5_1, kernel) + biases, name=scope) with tf.name_scope('conv5_3') as scope: kernel = weight_variable([3, 3, 512, 512]) biases = bias_variable([512]) output_conv5_3 = tf.nn.relu(conv2d(output_conv5_2, kernel) + biases, name=scope) pool5 = pool_max(output_conv5_3) #fc6 with tf.name_scope('fc6') as scope: shape = int(np.prod(pool5.get_shape()[1:])) kernel = weight_variable([shape, 4096]) biases = bias_variable([4096]) pool5_flat = tf.reshape(pool5, [-1, shape]) output_fc6 = tf.nn.relu(fc(pool5_flat, kernel, biases), name=scope) #fc7 with tf.name_scope('fc7') as scope: kernel = weight_variable([4096, 4096]) biases = bias_variable([4096]) output_fc7 = tf.nn.relu(fc(output_fc6, kernel, biases), name=scope) #fc8 with tf.name_scope('fc8') as scope: kernel = weight_variable([4096, 2]) biases = bias_variable([2]) output_fc8 = tf.nn.relu(fc(output_fc7, kernel, biases), name=scope) finaloutput = tf.nn.softmax(output_fc8, name="softmax") cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=finaloutput, labels=y)) optimize = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost) prediction_labels = tf.argmax(finaloutput, axis=1, name="output") read_labels = y correct_prediction = tf.equal(prediction_labels, read_labels) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) correct_times_in_batch = tf.reduce_sum(tf.cast(correct_prediction, tf.int32)) return dict( x=x, y=y, optimize=optimize, correct_prediction=correct_prediction, correct_times_in_batch=correct_times_in_batch, cost=cost, )def train_network(graph, batch_size, num_epochs, pb_file_path): init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) epoch_delta = 2 for epoch_index in range(num_epochs): for i in range(12): sess.run([graph['optimize']], feed_dict={ graph['x']: np.reshape(x_train[i], (1, 224, 224, 3)), graph['y']: ([[1, 0]] if y_train[i] == 0 else [[0, 1]]) }) if epoch_index % epoch_delta == 0: total_batches_in_train_set = 0 total_correct_times_in_train_set = 0 total_cost_in_train_set = 0. for i in range(12): return_correct_times_in_batch = sess.run(graph['correct_times_in_batch'], feed_dict={ graph['x']: np.reshape(x_train[i], (1, 224, 224, 3)), graph['y']: ([[1, 0]] if y_train[i] == 0 else [[0, 1]]) }) mean_cost_in_batch = sess.run(graph['cost'], feed_dict={ graph['x']: np.reshape(x_train[i], (1, 224, 224, 3)), graph['y']: ([[1, 0]] if y_train[i] == 0 else [[0, 1]]) }) total_batches_in_train_set += 1 total_correct_times_in_train_set += return_correct_times_in_batch total_cost_in_train_set += (mean_cost_in_batch * batch_size) total_batches_in_test_set = 0 total_correct_times_in_test_set = 0 total_cost_in_test_set = 0. for i in range(3): return_correct_times_in_batch = sess.run(graph['correct_times_in_batch'], feed_dict={ graph['x']: np.reshape(x_val[i], (1, 224, 224, 3)), graph['y']: ([[1, 0]] if y_val[i] == 0 else [[0, 1]]) }) mean_cost_in_batch = sess.run(graph['cost'], feed_dict={ graph['x']: np.reshape(x_val[i], (1, 224, 224, 3)), graph['y']: ([[1, 0]] if y_val[i] == 0 else [[0, 1]]) }) total_batches_in_test_set += 1 total_correct_times_in_test_set += return_correct_times_in_batch total_cost_in_test_set += (mean_cost_in_batch * batch_size) acy_on_test = total_correct_times_in_test_set / float(total_batches_in_test_set * batch_size) acy_on_train = total_correct_times_in_train_set / float(total_batches_in_train_set * batch_size) print('Epoch - {:2d}, acy_on_test:{:6.2f}%({}/{}),loss_on_test:{:6.2f}, acy_on_train:{:6.2f}%({}/{}),loss_on_train:{:6.2f}'.format(epoch_index, acy_on_test*100.0,total_correct_times_in_test_set, total_batches_in_test_set * batch_size, total_cost_in_test_set, acy_on_train * 100.0, total_correct_times_in_train_set, total_batches_in_train_set * batch_size, total_cost_in_train_set)) constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ["output"]) with tf.gfile.FastGFile(pb_file_path, mode='wb') as f: f.write(constant_graph.SerializeToString())def main(): batch_size = 12 num_epochs = 50 pb_file_path = "vggs.pb" g = build_network(height=224, width=224, channel=3) train_network(g, batch_size, num_epochs, pb_file_path)main()test
import tensorflow as tfimport numpy as npimport PIL.Image as Imagefrom skimage import io, transformdef 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: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, (224, 224, 3)) img_out_softmax = sess.run(out_softmax, feed_dict={input_x:np.reshape(img, [-1, 224, 224, 3])}) print "img_out_softmax:",img_out_softmax prediction_labels = np.argmax(img_out_softmax, axis=1) print "label:",prediction_labelsrecognize("vgg16/picture/dog/dog3.jpg", "vgg16/vggs.pb")阅读全文
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