I'm trying to find a way to visualize which numbers in the mnist dataset a model was able to correctly identify and which ones it wasn't. What I can't seem to find is if such a visualization is possible in tensorboard or if I would need to use/create something else to achieve it.
I'm currently working from the basic tutorial provided for tensorflow 2.0 with tensorboard added.
import datetime
import tensorflow as tf
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
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
model.fit(x_train,
y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])
model.evaluate(x_test, y_test)
It appears the what-if tool is what I was looking for, it allows you to visually sort testing data depending on whether it was correctly or incorrectly identified by the model.
If you want to test it out here is their demo that I used to get the above image and they have multiple other demos on the tools site.
