Created
April 20, 2020 10:09
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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']) | |
model.fit(x_train, y_train, epochs=5) | |
model.evaluate(x_test, y_test) |
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