参考网站：https://my.oschina.net/yilian/blog/661900
TensorBoard是TensorFlow自带的可视化面板，既可以显示网络结构，又可以显示训练和测试过程中各层参数的变化情况。 1.tensorflow网络可视化，操作，对于每一步op形成的无向图，我们可以用tensorflow可视化，因为tensorflow默认每次回话有一个默认的graph对象graph.ref。 所以：
summary_writer = tf.train.SummaryWriter('/tmp/tensorflowlogs',graph_def = sess.graph_def)如果我们需要记录图标训练时候的参数，我们需要添加相应的op操作，上面的summary_writer操作是将将来训练时候的op过程记录到日志里面，便于后面显示graph。 因为变量很多，所以可以定义scope范围来管理，节点树一样展开查看。 下面介绍几个方法，记录op以及汇总op的方法。 - 4D数据虚拟化 : tensorflow比如CNN每一步生成的特征，也就是用图像虚拟化ndarry的特征，比如我们可以看到每一步cnn得到的特征是什么。比如下图，车子每一步得到的CNN特征，用图像方式虚拟化显示image就是：
images = np.random.randint(256,size=shape).astype(np.uint8) tf.summary.image("Visulize_image",images)展示的样子如下： - 图标变化方式展示tensorflow数据特征:
w_hist = tf.summary.histogram("weights",W) b_hist = tf.summary.histogram("biases",b) y_hist = tf.summary.histogram("y",y)大概下面这个样子： - 以变量批次事件变化的线路图显示表示tensorflow数据： 比如下面记录精度的变化曲线图：
accuracy_summary = tf.summary.scalar("accuracy",accuracy)展示的样子大概如下： 因为我们上面这些op操作各种各样 多个 所以需要有个汇总的op操作 该操作会汇总上面的所有summary
merged = tf.merge_all_summaries()最后在session中执行汇总summary操作op 并使用上面sumarywriter 写入更新到graph 日志中。
summary_all = sess.run(merged_summary_op,feed_dict={x:batch_xs,y:batch_ys}) summary_writer.add_summary(summary_all,i) 举个栗子： import data.input_data as input_data mnist = input_data.read_data_sets("MNIST_data/",one_hot=True) import tensorflow as tf # Parameters learning_rate = 0.001 training_iters = 200000 batch_size = 64 display_step = 20 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) dropout = 0.8 # Dropout, probability to keep units # tf Graph input x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_classes]) keep_prob = tf.placeholder(tf.float32) # dropout (keep probability) # Create custom model def conv2d(name, l_input, w, b): return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input,w,strides=[1,1,1,1],padding='SAME'),b),name = name) def max_pool(name, l_input, k): return tf.nn.max_pool(l_input,ksize=[1,k,k,1],strides=[1,k,k,1],padding='SAME',name=name) def norm(name, l_input, lsize=4): return tf.nn.lrn(l_input,lsize,bias=1.0,alpha=0.001/9.0,beta=0.75,name=name) def customnet(_X, _weights, _biases, _dropout): # Reshape input picture _X = tf.reshape(_X,shape=[-1,28,28,1]) # Convolution Layer conv1 = conv2d('conv1',_X,_weights['wc1'],_biases['bc1']) # Max Pooling (down-sampling) pool1 = max_pool('pool1',conv1,k=2) # Apply Normalization norm1 = norm('norm1', pool1, lsize=4) # Apply Dropout norm1 = tf.nn.dropout(norm1, _dropout) #tf.summary.image("conv1",conv1) # Convolution Layer conv2 = conv2d('conv2',norm1,_weights['wc2'],_biases['bc2']) pool2 = max_pool('pool2', conv2, k=2) norm2 = norm('norm2',pool2,lsize=4) norm2 = tf.nn.dropout(norm2,_dropout) # Convolution Layer conv3 = conv2d('conv3',norm2,_weights['wc3'],_biases['bc3']) # Max Pooling (down-sampling) pool3 = max_pool('pool3', conv3, k=2) # Apply Normalization norm3 = norm('norm3',pool3,lsize = 4) # Apply Dropout norm3 = tf.nn.dropout(norm3, _dropout) #conv4 conv4 = conv2d('conv4',norm3,_weights['wc4'],_biases['bc4']) # Max Pooling (down-sampling) pool4 = max_pool('pool4', conv4, k=2) # Apply Normalization norm4 = norm('norm4',pool4,lsize=4) # Apply Dropout norm4 = tf.nn.dropout(norm4,_dropout) # Fully connected layer dense1 = tf.reshape(norm4,[-1,_weights['wd1'].get_shape().as_list()[0]]) dense1 = tf.nn.relu(tf.matmul(dense1,_weights['wd1'])+_biases['bd1'],name='fc1') dense2 = tf.nn.relu(tf.matmul(dense1,_weights['wd2'])+_biases['bd2'],name='fc2') # Output, class prediction out = tf.matmul(dense2,_weights['out'])+_biases['out'] return out # Store layers weight & bias weights = { 'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])), 'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])), 'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])), 'wc4': tf.Variable(tf.random_normal([2, 2, 256, 512])), 'wd1': tf.Variable(tf.random_normal([2*2*512, 1024])), 'wd2': tf.Variable(tf.random_normal([1024, 1024])), 'out': tf.Variable(tf.random_normal([1024, 10])) } biases = { 'bc1': tf.Variable(tf.random_normal([64])), 'bc2': tf.Variable(tf.random_normal([128])), 'bc3': tf.Variable(tf.random_normal([256])), 'bc4': tf.Variable(tf.random_normal([512])), 'bd1': tf.Variable(tf.random_normal([1024])), 'bd2': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } # Construct model pred = customnet(x, weights, biases, keep_prob) # Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables init = tf.initialize_all_variables() tf.summary.scalar("loss", cost) tf.summary.scalar("accuracy",accuracy) merged_summary_op = tf.summary.merge_all() # Launch the graph with tf.Session() as sess: sess.run(init) step = 1 summary_writer = tf.summary.FileWriter('/tmp/logs',sess.graph) # Keep training until reach max iterations while step * batch_size < training_iters: batch_xs, batch_ys = mnist.train.next_batch(batch_size) # Fit training using batch data sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout}) if step % display_step == 0: # Calculate batch accuracy acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)) summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys,keep_prob:dropout}) summary_writer.add_summary(summary_str, step) step += 1 print ("Optimization Finished!") # Calculate accuracy for 256 mnist test images print ("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))