Tensorflow随笔(二)
原创Tensorflow随笔(二)
原创
When training with methods such as tf.GradientTape(), use tf.summary to log the required information.
Use the same dataset as above, but convert it to tf.data.Dataset to take advantage of batching capabilities:
import tensorflow as tf
import datetime
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
physical_devices = tf.config.list_physical_devices('GPU')
try:
# Disable first GPU
tf.config.set_visible_devices(physical_devices[:1], 'GPU')
logical_devices = tf.config.list_logical_devices('GPU')
# Logical device was not created for first GPU
assert len(logical_devices) == len(physical_devices) - 1
except:
# Invalid device or cannot modify virtual devices once initialized.
pass
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
AUTOTUNE = tf.data.AUTOTUNE
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
train_dataset = train_dataset.shuffle(60000).batch(64)
test_dataset = test_dataset.batch(64)The training code follows the advanced quickstart tutorial, but shows how to log metrics to TensorBoard. Choose loss and optimizer:
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()Create stateful metrics that can be used to accumulate values during training and logged at any point:
# Define our metrics
train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32)
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('train_accuracy')
test_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32)
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('test_accuracy')Define the training and test functions:
def train_step(model, optimizer, x_train, y_train):
with tf.GradientTape() as tape:
predictions = model(x_train, training=True)
loss = loss_object(y_train, predictions)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
train_loss(loss)
train_accuracy(y_train, predictions)
def test_step(model, x_test, y_test):
predictions = model(x_test)
loss = loss_object(y_test, predictions)
test_loss(loss)
test_accuracy(y_test, predictions)Set up summary writers to write the summaries to disk in a different logs directory:
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = 'logs/gradient_tape/' + current_time + '/train'
test_log_dir = 'logs/gradient_tape/' + current_time + '/test'
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
test_summary_writer = tf.summary.create_file_writer(test_log_dir)Start training. Use tf.summary.scalar() to log metrics (loss and accuracy) during training/testing within the scope of the summary writers to write the summaries to disk. You have control over which metrics to log and how often to do it. Other tf.summary functions enable logging other types of data.
model = create_model() # reset our model
EPOCHS = 5
for epoch in range(EPOCHS):
for (x_train, y_train) in train_dataset:
train_step(model, optimizer, x_train, y_train)
with train_summary_writer.as_default():
tf.summary.scalar('loss', train_loss.result(), step=epoch)
tf.summary.scalar('accuracy', train_accuracy.result(), step=epoch)
for (x_test, y_test) in test_dataset:
test_step(model, x_test, y_test)
with test_summary_writer.as_default():
tf.summary.scalar('loss', test_loss.result(), step=epoch)
tf.summary.scalar('accuracy', test_accuracy.result(), step=epoch)
template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print (template.format(epoch+1,
train_loss.result(),
train_accuracy.result()*100,
test_loss.result(),
test_accuracy.result()*100))
# Reset metrics every epoch
train_loss.reset_states()
test_loss.reset_states()
train_accuracy.reset_states()
test_accuracy.reset_states()TensorBoard.dev is a free public service that enables you to upload your TensorBoard logs and get a permalink that can be shared with everyone in academic papers, blog posts, social media, etc. This can enable better reproducibility and collaboration
To use TensorBoard.dev, run the following command:
!tensorboard dev upload \
--logdir logs/fit \
--name "(optional) My latest experiment" \
--description "(optional) Simple comparison of several hyperparameters" \
--one_shot原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。