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6.10 可视化
工具
- TensorBoard
- Visdom
TensorBoard
- installation
pip install tensorboard
- Curves （accuary， loss）
- images visualization
工作原理
- 监听目录 （listen logdir）
- 建立 summary 实例 （build summary instance）
- 给实例喂数据 （fed data into summary instance）
第一步： run listener
在对应工作目录下监听 logs 文件夹
tensorboard --logdir logs
第二步：build summary
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)
第三步：fed scalar， fed image
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)
with summary_writer.as_default():
tf.summary.scalar('train-loss', float(loss), step=step)
fed multi-images
tensorboard 没有显示 images 接口
方法一： 劣
val_images = x[:25]
val_images = tf.reshape(val_iamges, [-1, 28, 28, 1])
with summary_writer.as_default():
tf.summary.scalar('test-acc', float(loss), step=step)
tf.summary.image('val-onebyone-images:', val_images, max_outputs=25, step=step)
方法二：优
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)
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
完整代码
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
import datetime
from matplotlib import pyplot as plt
import io
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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)