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Overview
Code Example
from datetime import datetime
import os
import tensorflow
from tensorflow.keras.datasets import mnist
from tensorflow import keras
from tensorflow.keras import layers
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
model = keras.Sequential([
layers.Dense(512, activation="relu"),
layers.Dense(10, activation="softmax")
])
model.compile(optimizer="rmsprop",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype("float32") / 255
test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype("float32") / 255
# Create a TensorBoard callback
logs = "logs/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir = logs,
histogram_freq = 1,
profile_batch = '10,20')
model.fit(train_images,
train_labels,
epochs=10,
batch_size=128,
callbacks = [tboard_callback])
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