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HandWritten_classification.py
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HandWritten_classification.py
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import tensorflow as tf
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
if(logs.get('acc')>0.99):
self.model.stop_training = True
def train_mnist():
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data(path=path)
import numpy as np
np.set_printoptions(linewidth=200)
plt.imshow(x_train[0])
# YOUR CODE SHOULD START HERE
x_train=x_train/255.0
y_test=y_train/255.0
callbacks = myCallback()
# YOUR CODE SHOULD END HERE
model = tf.keras.models.Sequential([
# YOUR CODE SHOULD START HERE
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)
# YOUR CODE SHOULD END HERE
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# model fitting
history = model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])
# YOUR CODE SHOULD END HERE
# model fitting
return history.epoch, history.history['acc'][-1]