tensorflow(二十五)：Tensorboard可视化

一、概况

 

 

 

 

• 其中visdom显示界面可以看出更加的丰富，可以画各种各样的图像。

二、安装与工作原理

pip install tensorboard

cpu运行一个程序，有一个tensor在这里流动；它会磁盘的某一个目录写数据；比如写在目录logs，写在这个目录下面以后，这个目录对应的文件格式就会被更新掉，这里面包含了一些要监听的数据（比如一些loss的最新数据写到这个文件夹下面去），然后另外一个监听器叫listener,你把目录告诉它，它会监听磁盘下的这个目录，然后这个目录有变化的话，它会把这个数据更新一下，这样的话打开一个web浏览器，这个web浏览器就是一个UI界面，这个界面会从listener中取数据。这样就可以在远程或者本地， 通过浏览器监听你的数据变化。

 三、使用步骤

 

 

 

 

 

 

 

 

 

 

 

 

 

 

• 显示方式如下：不会组合成一个图片；怎么组合呢？tensorflow中没有，我们自己写了一个函数。image_grid()函数：

 

 四、代码示例

import  tensorflow as tf
from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
import  datetime
from    matplotlib import pyplot as plt
import  io

def preprocess(x, y):
    x = tf.cast(x, dtype=tf.float32) / 255.
    y = tf.cast(y, dtype=tf.int32)
    return x,y

def plot_to_image(figure):
  """Converts the matplotlib plot specified by 'figure' to a PNG image and
  returns it. The supplied figure is closed and inaccessible after this call."""
  # Save the plot to a PNG in memory.
  buf = io.BytesIO()
  plt.savefig(buf, format='png')
  # Closing the figure prevents it from being displayed directly inside
  # the notebook.
  plt.close(figure)
  buf.seek(0)
  # Convert PNG buffer to TF image
  image = tf.image.decode_png(buf.getvalue(), channels=4)
  # Add the batch dimension
  image = tf.expand_dims(image, 0)
  return image

def image_grid(images):
  """Return a 5x5 grid of the MNIST images as a matplotlib figure."""
  # Create a figure to contain the plot.
  figure = plt.figure(figsize=(10,10))
  for i in range(25):
    # Start next subplot.
    plt.subplot(5, 5, i + 1, title='name')
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(images[i], cmap=plt.cm.binary)
  
  return figure

batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())

db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz, drop_remainder=True) 

network = Sequential([layers.Dense(256, activation='relu'),
                     layers.Dense(128, activation='relu'),
                     layers.Dense(64, activation='relu'),
                     layers.Dense(32, activation='relu'),
                     layers.Dense(10)])
network.build(input_shape=(None, 28*28))
network.summary()

optimizer = optimizers.Adam(lr=0.01)

current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
log_dir = 'logs/' + current_time
summary_writer = tf.summary.create_file_writer(log_dir) 

# get x from (x,y)
sample_img = next(iter(db))[0]
# get first image instance
sample_img = sample_img[0]
sample_img = tf.reshape(sample_img, [1, 28, 28, 1])
with summary_writer.as_default():
    tf.summary.image("Training sample:", sample_img, step=0)

for step, (x,y) in enumerate(db):

    with tf.GradientTape() as tape:
        # [b, 28, 28] => [b, 784]
        x = tf.reshape(x, (-1, 28*28))
        # [b, 784] => [b, 10]
        out = network(x)
        # [b] => [b, 10]
        y_onehot = tf.one_hot(y, depth=10) 
        # [b]
        loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, out, from_logits=True))
    grads = tape.gradient(loss, network.trainable_variables)
    optimizer.apply_gradients(zip(grads, network.trainable_variables))
    if step % 100 == 0:
        print(step, 'loss:', float(loss))
        with summary_writer.as_default(): 
            tf.summary.scalar('train-loss', float(loss), step=step) 

    # evaluate
    if step % 500 == 0:
        total, total_correct = 0., 0

        for _, (x, y) in enumerate(ds_val):  
            # [b, 28, 28] => [b, 784]
            x = tf.reshape(x, (-1, 28*28))
            # [b, 784] => [b, 10]
            out = network(x) 
            # [b, 10] => [b] 
            pred = tf.argmax(out, axis=1) 
            pred = tf.cast(pred, dtype=tf.int32)
            # bool type 
            correct = tf.equal(pred, y)
            # bool tensor => int tensor => numpy
            total_correct += tf.reduce_sum(tf.cast(correct, dtype=tf.int32)).numpy()
            total += x.shape[0]
        print(step, 'Evaluate Acc:', total_correct/total)  
        # print(x.shape) 
        val_images = x[:25]
        val_images = tf.reshape(val_images, [-1, 28, 28, 1])
        with summary_writer.as_default():
            tf.summary.scalar('test-acc', float(total_correct/total), step=step)
            tf.summary.image("val-onebyone-images:", val_images, max_outputs=25, step=step)
            
            val_images = tf.reshape(val_images, [-1, 28, 28])
            figure  = image_grid(val_images)
            tf.summary.image('val-images:', plot_to_image(figure), step=step)