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tensorboard使用步骤
①指定面板图上显示的变量
②训练过程中将这些变量计算出来,输出到文件中
③文件解析
tensorboard --logdir=文件名
在vggnet的代码上进行添加tensorboard语句
源代码
import tensorflow as tfimport osimport pickleimport numpy as npCIFAR_DIR = "dataset/cifar-10-batches-py"print(os.listdir(CIFAR_DIR))def load_data(filename): """read data from data file.""" with open(filename, 'rb') as f: data = pickle.load(f, encoding='bytes') return data[b'data'], data[b'labels']# tensorflow.Dataset.class CifarData: def __init__(self, filenames, need_shuffle): all_data = [] all_labels = [] for filename in filenames: data, labels = load_data(filename) all_data.append(data) all_labels.append(labels) self._data = np.vstack(all_data) self._data = self._data / 127.5 - 1 self._labels = np.hstack(all_labels) print(self._data.shape) print(self._labels.shape) self._num_examples = self._data.shape[0] self._need_shuffle = need_shuffle self._indicator = 0 if self._need_shuffle: self._shuffle_data() def _shuffle_data(self): # [0,1,2,3,4,5] -> [5,3,2,4,0,1] p = np.random.permutation(self._num_examples) self._data = self._data[p] self._labels = self._labels[p] def next_batch(self, batch_size): """return batch_size examples as a batch.""" end_indicator = self._indicator + batch_size if end_indicator > self._num_examples: if self._need_shuffle: self._shuffle_data() self._indicator = 0 end_indicator = batch_size else: raise Exception("have no more examples") if end_indicator > self._num_examples: raise Exception("batch size is larger than all examples") batch_data = self._data[self._indicator: end_indicator] batch_labels = self._labels[self._indicator: end_indicator] self._indicator = end_indicator return batch_data, batch_labelstrain_filenames = [os.path.join(CIFAR_DIR, 'data_batch_%d' % i) for i in range(1, 6)]test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]train_data = CifarData(train_filenames, True)test_data = CifarData(test_filenames, False)x = tf.placeholder(tf.float32, [None, 3072])y = tf.placeholder(tf.int64, [None])# [None], eg: [0,5,6,3]x_image = tf.reshape(x, [-1, 3, 32, 32])# 32*32x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])# conv1: 神经元图, feature_map, 输出图像conv1_1 = tf.layers.conv2d(x_image, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_1')conv1_2 = tf.layers.conv2d(conv1_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_2')# 16 * 16pooling1 = tf.layers.max_pooling2d(conv1_2, (2, 2), # kernel size (2, 2), # stride name = 'pool1')conv2_1 = tf.layers.conv2d(pooling1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_1')conv2_2 = tf.layers.conv2d(conv2_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_2')# 8 * 8pooling2 = tf.layers.max_pooling2d(conv2_2, (2, 2), # kernel size (2, 2), # stride name = 'pool2')conv3_1 = tf.layers.conv2d(pooling2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_1')conv3_2 = tf.layers.conv2d(conv3_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_2')# 4 * 4 * 32pooling3 = tf.layers.max_pooling2d(conv3_2, (2, 2), # kernel size (2, 2), # stride name = 'pool3')# [None, 4 * 4 * 32]flatten = tf.layers.flatten(pooling3)y_ = tf.layers.dense(flatten, 10)loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)# y_ -> sofmax# y -> one_hot# loss = ylogy_# indicespredict = tf.argmax(y_, 1)# [1,0,1,1,1,0,0,0]correct_prediction = tf.equal(predict, y)accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))with tf.name_scope('train_op'): train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)init = tf.global_variables_initializer()batch_size = 20train_steps = 10000test_steps = 100# train 10k: 73.4%with tf.Session() as sess: sess.run(init) for i in range(train_steps): batch_data, batch_labels = train_data.next_batch(batch_size) loss_val, acc_val, _ = sess.run( [loss, accuracy, train_op], feed_dict={ x: batch_data, y: batch_labels}) if (i+1) % 100 == 0: print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' % (i+1, loss_val, acc_val)) if (i+1) % 1000 == 0: test_data = CifarData(test_filenames, False) all_test_acc_val = [] for j in range(test_steps): test_batch_data, test_batch_labels \ = test_data.next_batch(batch_size) test_acc_val = sess.run( [accuracy], feed_dict = { x: test_batch_data, y: test_batch_labels }) all_test_acc_val.append(test_acc_val) test_acc = np.mean(all_test_acc_val) print('[Test ] Step: %d, acc: %4.5f' % (i+1, test_acc))
指定面板图上显示的变量
因为是标量,所以用tf.summary.scalar
所有跟tensorboard有关的函数都在tf.summary下面
可以这样理解,tf.summary.scalar是给loss节点另外一个名字,将节点中间过程训练的值聚合起来的一个标识符
在文件中可能是这样去存储的 'loss':<10,1.1>,<20,1.08>第10次训练的时候loss是1.1,第20次训练loss是1.08
解析的时候就生成这样的图
图像用tf.summary.image
现在我们有了3个summary,loss_summary、accurancy_summary、inputs_summary, 如果在运行过程中每个都需要输入到sess.run中进行计算是很麻烦的。在tensorboard中有个函数可以将它们merge起来,就是merge_all()
另一种方法是直接显示地指定把那些summary合并到一起,tf.summary.merge([loss_summary, accurancy_summary])
这样是因为我们在训练和测试的时候可能关注的变量并不完全一样。比如训练的时候可能会关注所有的变量如loss、accurancy和图像,而测试的时候可能只会关注accurancy/loss
添加后的代码
import tensorflow as tfimport osimport pickleimport numpy as npCIFAR_DIR = "dataset/cifar-10-batches-py"print(os.listdir(CIFAR_DIR))def load_data(filename): """read data from data file.""" with open(filename, 'rb') as f: data = pickle.load(f, encoding='bytes') return data[b'data'], data[b'labels']# tensorflow.Dataset.class CifarData: def __init__(self, filenames, need_shuffle): all_data = [] all_labels = [] for filename in filenames: data, labels = load_data(filename) all_data.append(data) all_labels.append(labels) self._data = np.vstack(all_data) self._data = self._data / 127.5 - 1 self._labels = np.hstack(all_labels) print(self._data.shape) print(self._labels.shape) self._num_examples = self._data.shape[0] self._need_shuffle = need_shuffle self._indicator = 0 if self._need_shuffle: self._shuffle_data() def _shuffle_data(self): # [0,1,2,3,4,5] -> [5,3,2,4,0,1] p = np.random.permutation(self._num_examples) self._data = self._data[p] self._labels = self._labels[p] def next_batch(self, batch_size): """return batch_size examples as a batch.""" end_indicator = self._indicator + batch_size if end_indicator > self._num_examples: if self._need_shuffle: self._shuffle_data() self._indicator = 0 end_indicator = batch_size else: raise Exception("have no more examples") if end_indicator > self._num_examples: raise Exception("batch size is larger than all examples") batch_data = self._data[self._indicator: end_indicator] batch_labels = self._labels[self._indicator: end_indicator] self._indicator = end_indicator return batch_data, batch_labelstrain_filenames = [os.path.join(CIFAR_DIR, 'data_batch_%d' % i) for i in range(1, 6)]test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]train_data = CifarData(train_filenames, True)test_data = CifarData(test_filenames, False)x = tf.placeholder(tf.float32, [None, 3072])y = tf.placeholder(tf.int64, [None])# [None], eg: [0,5,6,3]x_image = tf.reshape(x, [-1, 3, 32, 32])# 32*32x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])# conv1: 神经元图, feature_map, 输出图像conv1_1 = tf.layers.conv2d(x_image, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_1')conv1_2 = tf.layers.conv2d(conv1_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_2')# 16 * 16pooling1 = tf.layers.max_pooling2d(conv1_2, (2, 2), # kernel size (2, 2), # stride name = 'pool1')conv2_1 = tf.layers.conv2d(pooling1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_1')conv2_2 = tf.layers.conv2d(conv2_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_2')# 8 * 8pooling2 = tf.layers.max_pooling2d(conv2_2, (2, 2), # kernel size (2, 2), # stride name = 'pool2')conv3_1 = tf.layers.conv2d(pooling2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_1')conv3_2 = tf.layers.conv2d(conv3_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_2')# 4 * 4 * 32pooling3 = tf.layers.max_pooling2d(conv3_2, (2, 2), # kernel size (2, 2), # stride name = 'pool3')# [None, 4 * 4 * 32]flatten = tf.layers.flatten(pooling3)y_ = tf.layers.dense(flatten, 10)loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)# y_ -> sofmax# y -> one_hot# loss = ylogy_# indicespredict = tf.argmax(y_, 1)# [1,0,1,1,1,0,0,0]correct_prediction = tf.equal(predict, y)accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))with tf.name_scope('train_op'): train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)loss_summary = tf.summary.scalar('loss',loss)accuracy_summary = tf.summary.scalar('accuracy',accuracy)#x_image在程序中被归一化成了(-1,1)的值,但是tf.summary.image用的图是0-255之间,是像素值#如果直接用的话会出问题,所以要先逆归一化一下source_image = (x_image + 1)*127.5inputs_summary = tf.summary.image('inputs_summary', source_image)merged_summary = tf.summary.merge_all()merged_summary_test = tf.summary.merge([loss_summary, accuracy_summary])init = tf.global_variables_initializer()batch_size = 20train_steps = 10000test_steps = 100# train 10k: 73.4%with tf.Session() as sess: sess.run(init) for i in range(train_steps): batch_data, batch_labels = train_data.next_batch(batch_size) loss_val, acc_val, _ = sess.run( [loss, accuracy, train_op], feed_dict={ x: batch_data, y: batch_labels}) if (i+1) % 100 == 0: print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' % (i+1, loss_val, acc_val)) if (i+1) % 1000 == 0: test_data = CifarData(test_filenames, False) all_test_acc_val = [] for j in range(test_steps): test_batch_data, test_batch_labels \ = test_data.next_batch(batch_size) test_acc_val = sess.run( [accuracy], feed_dict = { x: test_batch_data, y: test_batch_labels }) all_test_acc_val.append(test_acc_val) test_acc = np.mean(all_test_acc_val) print('[Test ] Step: %d, acc: %4.5f' % (i+1, test_acc))
在训练过程中将变量计算出来,输出到文件中
不需要指定到具体的文件名,只指定到文件夹就可以了,它会在文件夹下自动地命名
创建文件夹
建立writer
就是文件句柄,有了文件句柄我们就可以做写或读的操作
可以加上sess.graph,也可以不加
要把summary输出出来的话,首先要计算出来它
把merged_summary和merged_summary_test可以看作是一个变量,加进去就可以计算出来了
但是计算会比较耗时,所以我们每隔100步进行一次计算
然后会把原来的代码进行一些重写
注意在test的时候,我们一般是希望它test的数据集是固定的,所以我们就生成一个fixed的batch data和batch labels
添加后的代码
import tensorflow as tfimport osimport pickleimport numpy as npCIFAR_DIR = "dataset/cifar-10-batches-py"print(os.listdir(CIFAR_DIR))def load_data(filename): """read data from data file.""" with open(filename, 'rb') as f: data = pickle.load(f, encoding='bytes') return data[b'data'], data[b'labels']# tensorflow.Dataset.class CifarData: def __init__(self, filenames, need_shuffle): all_data = [] all_labels = [] for filename in filenames: data, labels = load_data(filename) all_data.append(data) all_labels.append(labels) self._data = np.vstack(all_data) self._data = self._data / 127.5 - 1 self._labels = np.hstack(all_labels) print(self._data.shape) print(self._labels.shape) self._num_examples = self._data.shape[0] self._need_shuffle = need_shuffle self._indicator = 0 if self._need_shuffle: self._shuffle_data() def _shuffle_data(self): # [0,1,2,3,4,5] -> [5,3,2,4,0,1] p = np.random.permutation(self._num_examples) self._data = self._data[p] self._labels = self._labels[p] def next_batch(self, batch_size): """return batch_size examples as a batch.""" end_indicator = self._indicator + batch_size if end_indicator > self._num_examples: if self._need_shuffle: self._shuffle_data() self._indicator = 0 end_indicator = batch_size else: raise Exception("have no more examples") if end_indicator > self._num_examples: raise Exception("batch size is larger than all examples") batch_data = self._data[self._indicator: end_indicator] batch_labels = self._labels[self._indicator: end_indicator] self._indicator = end_indicator return batch_data, batch_labelstrain_filenames = [os.path.join(CIFAR_DIR, 'data_batch_%d' % i) for i in range(1, 6)]test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]train_data = CifarData(train_filenames, True)test_data = CifarData(test_filenames, False)x = tf.placeholder(tf.float32, [None, 3072])y = tf.placeholder(tf.int64, [None])# [None], eg: [0,5,6,3]x_image = tf.reshape(x, [-1, 3, 32, 32])# 32*32x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])# conv1: 神经元图, feature_map, 输出图像conv1_1 = tf.layers.conv2d(x_image, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_1')conv1_2 = tf.layers.conv2d(conv1_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_2')# 16 * 16pooling1 = tf.layers.max_pooling2d(conv1_2, (2, 2), # kernel size (2, 2), # stride name = 'pool1')conv2_1 = tf.layers.conv2d(pooling1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_1')conv2_2 = tf.layers.conv2d(conv2_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_2')# 8 * 8pooling2 = tf.layers.max_pooling2d(conv2_2, (2, 2), # kernel size (2, 2), # stride name = 'pool2')conv3_1 = tf.layers.conv2d(pooling2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_1')conv3_2 = tf.layers.conv2d(conv3_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_2')# 4 * 4 * 32pooling3 = tf.layers.max_pooling2d(conv3_2, (2, 2), # kernel size (2, 2), # stride name = 'pool3')# [None, 4 * 4 * 32]flatten = tf.layers.flatten(pooling3)y_ = tf.layers.dense(flatten, 10)loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)# y_ -> sofmax# y -> one_hot# loss = ylogy_# indicespredict = tf.argmax(y_, 1)# [1,0,1,1,1,0,0,0]correct_prediction = tf.equal(predict, y)accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))with tf.name_scope('train_op'): train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)loss_summary = tf.summary.scalar('loss',loss)accuracy_summary = tf.summary.scalar('accuracy',accuracy)#x_image在程序中被归一化成了(-1,1)的值,但是tf.summary.image用的图是0-255之间,是像素值#如果直接用的话会出问题,所以要先逆归一化一下source_image = (x_image + 1)*127.5inputs_summary = tf.summary.image('inputs_summary', source_image)merged_summary = tf.summary.merge_all()merged_summary_test = tf.summary.merge([loss_summary, accuracy_summary])LOG_DIR = '.'run_label = 'run_vgg_tensorboard'run_dir = os.path.join(LOG_DIR, run_label)if not os.path.exists(run_dir): os.mkdir(run_dir)train_log_dir = os.path.join(run_dir,'train')test_log_dir = os.path.join(run_dir,'test')if not os.path.exists(train_log_dir): os.mkdir(train_log_dir)init = tf.global_variables_initializer()batch_size = 20train_steps = 10000test_steps = 100output_summary_every_steps = 100# train 10k: 73.4%with tf.Session() as sess: sess.run(init) #训练集和测试集都分别进行输出,建立2个writer train_writer = tf.summary.FileWriter(train_log_dir, sess.graph) test_writer = tf.summary.FileWriter(test_log_dir) fixed_test_batch_data, fixed_test_batch_labels = test_data.next_batch(batch_size) for i in range(train_steps): batch_data, batch_labels = train_data.next_batch(batch_size) eval_ops = [loss,accuracy,train_op] shoud_output_summary = ((i+1)%output_summary_every_steps == 0) if shoud_output_summary: eval_ops.append(merged_summary) eval_ops_results = sess.run( eval_ops, feed_dict={ x: batch_data, y: batch_labels}) loss_val, acc_val = eval_ops_results[0:2] if shoud_output_summary: train_summary_str = eval_ops_results[-1] train_writer.add_summary(train_summary_str,i+1) test_summary_str = sess.run([merged_summary_test], feed_dict={ x:fixed_test_batch_data, y:fixed_test_batch_labels, })[0] test_writer.add_summary(test_summary_str,i+1) if (i+1) % 100 == 0: print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' % (i+1, loss_val, acc_val)) if (i+1) % 1000 == 0: test_data = CifarData(test_filenames, False) all_test_acc_val = [] for j in range(test_steps): test_batch_data, test_batch_labels \ = test_data.next_batch(batch_size) test_acc_val = sess.run( [accuracy], feed_dict = { x: test_batch_data, y: test_batch_labels }) all_test_acc_val.append(test_acc_val) test_acc = np.mean(all_test_acc_val) print('[Test ] Step: %d, acc: %4.5f' % (i+1, test_acc))
写函数多打印一些变量的值到tensorboard中去
包括均值、方差、最小值、最大值、直方图
可以看到tf中的很多函数都加了reduce前缀,意思是能把多维的tensor求出了一个最小值,或者均值,所以叫reduce什么什么
将各个卷积层的输出都输出到tensorboard中
import tensorflow as tfimport osimport pickleimport numpy as npCIFAR_DIR = "dataset/cifar-10-batches-py"print(os.listdir(CIFAR_DIR))def load_data(filename): """read data from data file.""" with open(filename, 'rb') as f: data = pickle.load(f, encoding='bytes') return data[b'data'], data[b'labels']# tensorflow.Dataset.class CifarData: def __init__(self, filenames, need_shuffle): all_data = [] all_labels = [] for filename in filenames: data, labels = load_data(filename) all_data.append(data) all_labels.append(labels) self._data = np.vstack(all_data) self._data = self._data / 127.5 - 1 self._labels = np.hstack(all_labels) print(self._data.shape) print(self._labels.shape) self._num_examples = self._data.shape[0] self._need_shuffle = need_shuffle self._indicator = 0 if self._need_shuffle: self._shuffle_data() def _shuffle_data(self): # [0,1,2,3,4,5] -> [5,3,2,4,0,1] p = np.random.permutation(self._num_examples) self._data = self._data[p] self._labels = self._labels[p] def next_batch(self, batch_size): """return batch_size examples as a batch.""" end_indicator = self._indicator + batch_size if end_indicator > self._num_examples: if self._need_shuffle: self._shuffle_data() self._indicator = 0 end_indicator = batch_size else: raise Exception("have no more examples") if end_indicator > self._num_examples: raise Exception("batch size is larger than all examples") batch_data = self._data[self._indicator: end_indicator] batch_labels = self._labels[self._indicator: end_indicator] self._indicator = end_indicator return batch_data, batch_labelstrain_filenames = [os.path.join(CIFAR_DIR, 'data_batch_%d' % i) for i in range(1, 6)]test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]train_data = CifarData(train_filenames, True)test_data = CifarData(test_filenames, False)x = tf.placeholder(tf.float32, [None, 3072])y = tf.placeholder(tf.int64, [None])# [None], eg: [0,5,6,3]x_image = tf.reshape(x, [-1, 3, 32, 32])# 32*32x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])# conv1: 神经元图, feature_map, 输出图像conv1_1 = tf.layers.conv2d(x_image, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_1')conv1_2 = tf.layers.conv2d(conv1_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_2')# 16 * 16pooling1 = tf.layers.max_pooling2d(conv1_2, (2, 2), # kernel size (2, 2), # stride name = 'pool1')conv2_1 = tf.layers.conv2d(pooling1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_1')conv2_2 = tf.layers.conv2d(conv2_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_2')# 8 * 8pooling2 = tf.layers.max_pooling2d(conv2_2, (2, 2), # kernel size (2, 2), # stride name = 'pool2')conv3_1 = tf.layers.conv2d(pooling2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_1')conv3_2 = tf.layers.conv2d(conv3_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_2')# 4 * 4 * 32pooling3 = tf.layers.max_pooling2d(conv3_2, (2, 2), # kernel size (2, 2), # stride name = 'pool3')# [None, 4 * 4 * 32]flatten = tf.layers.flatten(pooling3)y_ = tf.layers.dense(flatten, 10)loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)# y_ -> sofmax# y -> one_hot# loss = ylogy_# indicespredict = tf.argmax(y_, 1)# [1,0,1,1,1,0,0,0]correct_prediction = tf.equal(predict, y)accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))with tf.name_scope('train_op'): train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)#name是指定命名空间,为了防止冲突def variable_summary(var,name): with tf.name_scope(name): mean = tf.reduce_mean(var)#均值 with tf.name_scope('stdddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var-mean))) tf.summary.scalar('mean',mean) tf.summary.scalar('stddev',stddev) tf.summary.scalar('min',tf.reduce_min(var)) tf.summary.scalar('max',tf.reduce_max(var)) tf.summary.histogram('histogram',var)#直方图with tf.name_scope('summary'): variable_summary(conv1_1, 'conv1_1') variable_summary(conv1_2, 'conv1_2') variable_summary(conv2_1, 'conv2_1') variable_summary(conv2_2, 'conv2_2') variable_summary(conv3_1, 'conv3_1') variable_summary(conv3_2, 'conv3_2') #后面的merge_all会把我们写的这些都汇总起来loss_summary = tf.summary.scalar('loss',loss)accuracy_summary = tf.summary.scalar('accuracy',accuracy)#x_image在程序中被归一化成了(-1,1)的值,但是tf.summary.image用的图是0-255之间,是像素值#如果直接用的话会出问题,所以要先逆归一化一下source_image = (x_image + 1)*127.5inputs_summary = tf.summary.image('inputs_summary', source_image)merged_summary = tf.summary.merge_all()merged_summary_test = tf.summary.merge([loss_summary, accuracy_summary])LOG_DIR = '.'run_label = 'run_vgg_tensorboard'run_dir = os.path.join(LOG_DIR, run_label)if not os.path.exists(run_dir): os.mkdir(run_dir)train_log_dir = os.path.join(run_dir,'train')test_log_dir = os.path.join(run_dir,'test')if not os.path.exists(train_log_dir): os.mkdir(train_log_dir)init = tf.global_variables_initializer()batch_size = 20train_steps = 10000test_steps = 100output_summary_every_steps = 100# train 10k: 73.4%with tf.Session() as sess: sess.run(init) #训练集和测试集都分别进行输出,建立2个writer train_writer = tf.summary.FileWriter(train_log_dir, sess.graph) test_writer = tf.summary.FileWriter(test_log_dir) fixed_test_batch_data, fixed_test_batch_labels = test_data.next_batch(batch_size) for i in range(train_steps): batch_data, batch_labels = train_data.next_batch(batch_size) eval_ops = [loss,accuracy,train_op] shoud_output_summary = ((i+1)%output_summary_every_steps == 0) if shoud_output_summary: eval_ops.append(merged_summary) eval_ops_results = sess.run( eval_ops, feed_dict={ x: batch_data, y: batch_labels}) loss_val, acc_val = eval_ops_results[0:2] if shoud_output_summary: train_summary_str = eval_ops_results[-1] train_writer.add_summary(train_summary_str,i+1) test_summary_str = sess.run([merged_summary_test], feed_dict={ x:fixed_test_batch_data, y:fixed_test_batch_labels, })[0] test_writer.add_summary(test_summary_str,i+1) if (i+1) % 100 == 0: print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' % (i+1, loss_val, acc_val)) if (i+1) % 1000 == 0: test_data = CifarData(test_filenames, False) all_test_acc_val = [] for j in range(test_steps): test_batch_data, test_batch_labels \ = test_data.next_batch(batch_size) test_acc_val = sess.run( [accuracy], feed_dict = { x: test_batch_data, y: test_batch_labels }) all_test_acc_val.append(test_acc_val) test_acc = np.mean(all_test_acc_val) print('[Test ] Step: %d, acc: %4.5f' % (i+1, test_acc))
log文件解析
可以同时打开多个文件夹,但这就需要给每个文件夹定义个名字
tensorboard --logdir=train:'train',test:'test'打开的两个,可以用鼠标点击显示与不显示
images
graphs
distribution
histograms
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