57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import numpy as np
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import keras_core
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from keras_core import layers
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from keras_core.utils import to_categorical
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# Model / data parameters
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num_classes = 10
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input_shape = (28, 28, 1)
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# Load the data and split it between train and test sets
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(x_train, y_train), (x_test, y_test) = keras_core.datasets.mnist.load_data()
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# Scale images to the [0, 1] range
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x_train = x_train.astype("float32") / 255
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x_test = x_test.astype("float32") / 255
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# Make sure images have shape (28, 28, 1)
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x_train = np.expand_dims(x_train, -1)
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x_test = np.expand_dims(x_test, -1)
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print("x_train shape:", x_train.shape)
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print(x_train.shape[0], "train samples")
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print(x_test.shape[0], "test samples")
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# convert class vectors to binary class matrices
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y_train = to_categorical(y_train, num_classes)
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y_test = to_categorical(y_test, num_classes)
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batch_size = 128
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epochs = 3
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model = keras_core.Sequential(
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[
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layers.Input(shape=input_shape),
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layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
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layers.MaxPooling2D(pool_size=(2, 2)),
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layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
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layers.MaxPooling2D(pool_size=(2, 2)),
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layers.Flatten(),
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layers.Dropout(0.5),
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layers.Dense(num_classes, activation="softmax"),
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]
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)
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model.summary()
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model.compile(
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loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
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)
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model.fit(
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x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1
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)
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score = model.evaluate(x_test, y_test, verbose=0)
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print("Test loss:", score[0])
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print("Test accuracy:", score[1])
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