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handwring_recognition.py
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handwring_recognition.py
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import tensorflow as tf
from os import path, getcwd, chdir
path = f"{getcwd()}/../tmp2/mnist.npz"
def train_mnist():
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs):
if logs["acc"] > 0.99:
print ('\nReached 99% accuracy so cancelling training!')
self.model.stop_training = True
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data(path=path)
# YOUR CODE SHOULD START HERE
(x_train, x_test) = (x_train / 255.0, x_test / 255.0)
callbacks = myCallback()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
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 = [callbacks]
)
# model fitting
return history.epoch, history.history['acc'][-1]
train_mnist()