24 lines
846 B
Python
24 lines
846 B
Python
import tensorflow as tf
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mnist = tf.keras.datasets.mnist
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# Load and prepare the MNIST dataset. Convert the samples from integers to floating-point numbers
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(x_train, y_train), (x_test, y_test) = mnist.load_data(path='/bin/mnist.npz')
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x_train, x_test = x_train / 255.0, x_test / 255.0
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# Build the tf.keras.Sequential model by stacking layers. Choose an optimizer and loss function for training
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(10, activation='softmax')
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])
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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# Train and evaluate model
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model.fit(x_train, y_train, epochs=5)
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model.evaluate(x_test, y_test, verbose=2)
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