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Basic MNIST Hand-written Digits Classification

PythonMachine LearningTensorFlow

The example code was originally from DeepLearning.ai. It shows the basic setup to classify hand-written digits.

python
import tensorflow as tf
# Accuracy callback
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
if logs.get('acc') > 0.99:
self.model.stop_training = True
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train = x_train/255.0
x_test = x_test/255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# model fitting
history = model.fit( x_train, y_train,
epochs=10,
callbacks=[myCallback()]
# result
print( history.epoch, history.history['acc'][-1] )
Code Snippets & Notes by Makzan