wehub-resource-sync
58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
import logging
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from mla.datasets import load_mnist
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from mla.metrics import accuracy
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from mla.neuralnet import NeuralNet
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from mla.neuralnet.layers import (
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Activation,
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Convolution,
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MaxPooling,
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Flatten,
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Dropout,
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Parameters,
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)
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from mla.neuralnet.layers import Dense
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from mla.neuralnet.optimizers import Adadelta
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from mla.utils import one_hot
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logging.basicConfig(level=logging.DEBUG)
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# Load MNIST dataset
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X_train, X_test, y_train, y_test = load_mnist()
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# Normalize data
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X_train /= 255.0
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X_test /= 255.0
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y_train = one_hot(y_train.flatten())
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y_test = one_hot(y_test.flatten())
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print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
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# Approx. 15-20 min. per epoch
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model = NeuralNet(
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layers=[
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Convolution(n_filters=32, filter_shape=(3, 3), padding=(1, 1), stride=(1, 1)),
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Activation("relu"),
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Convolution(n_filters=32, filter_shape=(3, 3), padding=(1, 1), stride=(1, 1)),
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Activation("relu"),
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MaxPooling(pool_shape=(2, 2), stride=(2, 2)),
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Dropout(0.5),
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Flatten(),
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Dense(128),
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Activation("relu"),
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Dropout(0.5),
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Dense(10),
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Activation("softmax"),
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],
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loss="categorical_crossentropy",
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optimizer=Adadelta(),
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metric="accuracy",
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batch_size=128,
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max_epochs=3,
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)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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print(accuracy(y_test, predictions))
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