157 lines
6.2 KiB
Python
157 lines
6.2 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.utils.test_utils import get_test_data
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from keras.models import Sequential
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from keras.layers import Dense, Activation, RepeatVector, TimeDistributedDense, GRU
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from keras.utils import np_utils
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from keras.utils.test_utils import keras_test
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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 = 5
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standard_weight = 1
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high_weight = 10
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train_samples = 5000
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test_samples = 1000
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timesteps = 3
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input_dim = 10
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loss = 'mse'
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
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nb_test=test_samples,
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input_shape=(input_dim,),
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classification=True,
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nb_class=nb_classes)
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# convert class vectors to binary class matrices
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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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temporal_X_train = np.reshape(X_train, (len(X_train), 1, X_train.shape[1]))
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temporal_X_train = np.repeat(temporal_X_train, timesteps, axis=1)
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temporal_X_test = np.reshape(X_test, (len(X_test), 1, X_test.shape[1]))
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temporal_X_test = np.repeat(temporal_X_test, timesteps, axis=1)
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temporal_Y_train = np.reshape(Y_train, (len(Y_train), 1, Y_train.shape[1]))
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temporal_Y_train = np.repeat(temporal_Y_train, timesteps, axis=1)
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temporal_Y_test = np.reshape(Y_test, (len(Y_test), 1, Y_test.shape[1]))
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temporal_Y_test = np.repeat(temporal_Y_test, timesteps, axis=1)
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temporal_sample_weight = np.reshape(sample_weight, (len(sample_weight), 1))
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temporal_sample_weight = np.repeat(temporal_sample_weight, timesteps, axis=1)
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def create_sequential_model():
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model = Sequential()
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model.add(Dense(32, input_shape=(input_dim,)))
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model.add(Activation('relu'))
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model.add(Dense(nb_classes))
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model.add(Activation('softmax'))
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return model
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def create_temporal_sequential_model():
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model = Sequential()
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model.add(GRU(32, input_shape=(timesteps, input_dim), return_sequences=True))
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model.add(TimeDistributedDense(nb_classes))
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model.add(Activation('softmax'))
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return model
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@keras_test
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def _test_weights_sequential(model, class_weight=None, sample_weight=None,
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X_train=X_train, Y_train=Y_train,
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X_test=X_test, Y_test=Y_test):
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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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# 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_sequential = _test_weights_sequential(model)
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@keras_test
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def test_sequential_class_weights():
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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_sequential)
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@keras_test
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def test_sequential_sample_weights():
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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_sequential)
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@keras_test
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def test_sequential_temporal_sample_weights():
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model = create_temporal_sequential_model()
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model.compile(loss=loss, optimizer='rmsprop',
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sample_weight_mode='temporal')
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score = _test_weights_sequential(model,
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sample_weight=temporal_sample_weight,
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X_train=temporal_X_train,
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X_test=temporal_X_test,
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Y_train=temporal_Y_train,
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Y_test=temporal_Y_test)
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assert(score < standard_score_sequential)
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# a twist: sample-wise weights with temporal output
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model = create_temporal_sequential_model()
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model.compile(loss=loss, optimizer='rmsprop',
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sample_weight_mode=None)
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score = _test_weights_sequential(model,
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sample_weight=sample_weight,
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X_train=temporal_X_train,
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X_test=temporal_X_test,
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Y_train=temporal_Y_train,
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Y_test=temporal_Y_test)
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assert(score < standard_score_sequential)
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if __name__ == '__main__':
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pytest.main([__file__])
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