150 lines
6.0 KiB
Python
150 lines
6.0 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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import pytest
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import numpy as np
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np.random.seed(1337)
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from keras.datasets import mnist
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from keras.models import Sequential, Graph
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from keras.layers.core import Dense, Activation
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from keras.utils import np_utils
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nb_classes = 10
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batch_size = 128
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nb_epoch = 15
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weighted_class = 9
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standard_weight = 1
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high_weight = 5
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max_train_samples = 5000
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max_test_samples = 1000
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# the data, shuffled and split between tran and test sets
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(60000, 784)[:max_train_samples]
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X_test = X_test.reshape(10000, 784)[:max_test_samples]
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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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# convert class vectors to binary class matrices
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y_train = y_train[:max_train_samples]
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y_test = y_test[:max_test_samples]
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Y_train = np_utils.to_categorical(y_train, nb_classes)
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Y_test = np_utils.to_categorical(y_test, nb_classes)
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test_ids = np.where(y_test == np.array(weighted_class))[0]
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class_weight = dict([(i, standard_weight) for i in range(nb_classes)])
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class_weight[weighted_class] = high_weight
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sample_weight = np.ones((y_train.shape[0])) * standard_weight
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sample_weight[y_train == weighted_class] = high_weight
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def create_sequential_model():
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model = Sequential()
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model.add(Dense(50, input_shape=(784,)))
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model.add(Activation('relu'))
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model.add(Dense(10))
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model.add(Activation('softmax'))
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return model
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def create_graph_model():
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model = Graph()
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model.add_input(name='input', input_shape=(784,))
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model.add_node(Dense(50, activation='relu'), name='d1', input='input')
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model.add_node(Dense(10, activation='softmax'), name='d2', input='d1')
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model.add_output(name='output', input='d2')
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return model
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def _test_weights_sequential(model, class_weight=None, sample_weight=None):
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if sample_weight is not None:
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model.fit(X_train, Y_train, batch_size=batch_size,
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nb_epoch=nb_epoch // 3, verbose=0,
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class_weight=class_weight, sample_weight=sample_weight)
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model.fit(X_train, Y_train, batch_size=batch_size,
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nb_epoch=nb_epoch // 3, verbose=0,
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class_weight=class_weight, sample_weight=sample_weight,
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validation_split=0.1)
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model.fit(X_train, Y_train, batch_size=batch_size,
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nb_epoch=nb_epoch // 3, verbose=0,
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class_weight=class_weight, sample_weight=sample_weight,
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validation_data=(X_train, Y_train, sample_weight))
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else:
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model.fit(X_train, Y_train, batch_size=batch_size,
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nb_epoch=nb_epoch // 2, verbose=0,
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class_weight=class_weight, sample_weight=sample_weight)
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model.fit(X_train, Y_train, batch_size=batch_size,
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nb_epoch=nb_epoch // 2, verbose=0,
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class_weight=class_weight, sample_weight=sample_weight,
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validation_split=0.1)
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model.train_on_batch(X_train[:32], Y_train[:32],
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class_weight=class_weight,
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sample_weight=sample_weight[:32] if sample_weight is not None else None)
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model.test_on_batch(X_train[:32], Y_train[:32],
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sample_weight=sample_weight[:32] if sample_weight is not None else None)
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score = model.evaluate(X_test[test_ids, :], Y_test[test_ids, :], verbose=0)
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return score
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def _test_weights_graph(model, class_weight=None, sample_weight=None):
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model.fit({'input': X_train, 'output': Y_train},
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batch_size=batch_size, nb_epoch=nb_epoch // 2, verbose=0,
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class_weight={'output': class_weight},
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sample_weight={'output': sample_weight})
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model.fit({'input': X_train, 'output': Y_train},
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batch_size=batch_size, nb_epoch=nb_epoch // 2, verbose=0,
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class_weight={'output': class_weight},
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sample_weight={'output': sample_weight}, validation_split=0.1)
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model.train_on_batch({'input': X_train[:32], 'output': Y_train[:32]},
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class_weight={'output': class_weight},
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sample_weight={'output': sample_weight[:32] if sample_weight is not None else None})
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model.test_on_batch({'input': X_train[:32], 'output': Y_train[:32]},
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sample_weight={'output': sample_weight[:32] if sample_weight is not None else None})
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score = model.evaluate({'input': X_test[test_ids, :],
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'output': Y_test[test_ids, :]},
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verbose=0)
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return score
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def test_sequential():
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for loss in ['mae', 'mse']:
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# no weights: reference point
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model = create_sequential_model()
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model.compile(loss=loss, optimizer='rmsprop')
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standard_score = _test_weights_sequential(model)
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# test class_weight
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model = create_sequential_model()
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model.compile(loss=loss, optimizer='rmsprop')
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score = _test_weights_sequential(model, class_weight=class_weight)
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assert(score < standard_score)
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# test sample_weight
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model = create_sequential_model()
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model.compile(loss=loss, optimizer='rmsprop')
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score = _test_weights_sequential(model, sample_weight=sample_weight)
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assert(score < standard_score)
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def test_graph():
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for loss in ['mae', 'mse']:
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# no weights: reference point
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model = create_graph_model()
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model.compile(loss={'output': loss}, optimizer='rmsprop')
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standard_score = _test_weights_graph(model)
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# test class_weight
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model = create_graph_model()
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model.compile(loss={'output': loss}, optimizer='rmsprop')
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score = _test_weights_graph(model, class_weight=class_weight)
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assert(score < standard_score)
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# test sample_weight
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model = create_graph_model()
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model.compile(loss={'output': loss}, optimizer='rmsprop')
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score = _test_weights_graph(model, sample_weight=sample_weight)
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assert(score < standard_score)
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if __name__ == '__main__':
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pytest.main([__file__])
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