目录
- Tensor Flow
- TensorBoard
- Installation
- Priciple
- Step1.run listener
- Step2.build summary
- Step3.fed scalar
- Step3.fed single Image
- Step3.fed multi-images
- Instance
- Visdom
Tensor Flow
TensorBoard
Installation
Curves
Image Visualization
Installation
pip install tensorboard
Priciple
Listen logdir
build summary instance
fed data into summary instance
Step1.run listener
Step2.build summary
import datetime
import tensorflow as tf
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)
Step3.fed scalar
with summary_writer.as_default():
tf.summary.scalar('loss', float(loss), step=epoch)
tf.summary.scalar('accuracy', float(train_accuracy), step=epoch)
Step3.fed single Image
sample_img = next(iter(db))[0]
sample_img = sample_img[0]
sample_img = tf.reshape(sample_img, [1, 28, 28, 1])
with summary_writer.as_default():
tf.summary.image('Traning sample:', sample_img, step=0)
Step3.fed multi-images
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(loss), step=step)
tf.summary.image('val-onebyone-images:',
val_images,
max_output=25,
step=step)
Instance
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)
datasets: (60000, 28, 28) (60000,) 0 255 Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_5 (Dense) multiple 200960 _________________________________________________________________ dense_6 (Dense) multiple 32896 _________________________________________________________________ dense_7 (Dense) multiple 8256 _________________________________________________________________ dense_8 (Dense) multiple 2080 _________________________________________________________________ dense_9 (Dense) multiple 330 ================================================================= Total params: 244,522 Trainable params: 244,522 Non-trainable params: 0 _________________________________________________________________ 0 loss: 2.3376832008361816 0 Evaluate Acc: 0.18008814102564102 100 loss: 0.48326703906059265 200 loss: 0.25227126479148865 300 loss: 0.1876775473356247 400 loss: 0.1666598916053772 500 loss: 0.1336817890405655 500 Evaluate Acc: 0.9542267628205128 600 loss: 0.12189087271690369 700 loss: 0.1326061487197876 800 loss: 0.19785025715827942 900 loss: 0.06632998585700989 1000 loss: 0.059026435017585754 1000 Evaluate Acc: 0.96875 1100 loss: 0.1200297400355339 1200 loss: 0.20464201271533966 1300 loss: 0.07950295507907867 1400 loss: 0.13028256595134735 1500 loss: 0.0644262284040451 1500 Evaluate Acc: 0.9657451923076923 1600 loss: 0.06169471889734268 1700 loss: 0.04833034425973892 1800 loss: 0.14102090895175934 1900 loss: 0.00526371318846941 2000 loss: 0.03505736589431763 2000 Evaluate Acc: 0.9735576923076923 2100 loss: 0.08948884159326553 2200 loss: 0.035213079303503036 2300 loss: 0.15530908107757568 2400 loss: 0.13484254479408264 2500 loss: 0.17365671694278717 2500 Evaluate Acc: 0.9727564102564102 2600 loss: 0.17384998500347137 2700 loss: 0.06045734882354736 2800 loss: 0.13712377846240997 2900 loss: 0.08388100564479828 3000 loss: 0.05825091525912285 3000 Evaluate Acc: 0.9657451923076923 3100 loss: 0.08653448522090912 3200 loss: 0.06315462291240692 3300 loss: 0.05536603182554245 3400 loss: 0.2064306139945984 3500 loss: 0.043574199080467224 3500 Evaluate Acc: 0.96875 3600 loss: 0.0456567145884037 3700 loss: 0.08570165187120438 3800 loss: 0.021522987633943558 3900 loss: 0.05123775079846382 4000 loss: 0.14489373564720154 4000 Evaluate Acc: 0.9722556089743589 4100 loss: 0.08733823150396347 4200 loss: 0.04572174698114395 4300 loss: 0.06757005304098129 4400 loss: 0.018376709893345833 4500 loss: 0.024091437458992004 4500 Evaluate Acc: 0.9701522435897436 4600 loss: 0.10814780741930008