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from __future__ import absolute_import
from __future__ import print_function
import pytest
import numpy as np
from keras import backend as K
from keras.utils.test_utils import get_test_data
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, GRU, TimeDistributed, Input
from keras.utils import np_utils
from numpy.testing import assert_almost_equal, assert_array_almost_equal
num_classes = 10
batch_size = 128
epochs = 15
weighted_class = 5
high_weight = 10
train_samples = 5000
test_samples = 1000
timesteps = 3
input_dim = 10
loss = 'mse'
standard_weight = 1
standard_score_sequential = 0.5
decimal_precision = {
'cntk': 2,
'theano': 6,
'tensorflow': 6
}
def _get_test_data():
np.random.seed(1337)
(x_train, y_train), (x_test, y_test) = get_test_data(num_train=train_samples,
num_test=test_samples,
input_shape=(input_dim,),
classification=True,
num_classes=num_classes)
int_y_test = y_test.copy()
int_y_train = y_train.copy()
# convert class vectors to binary class matrices
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)
test_ids = np.where(int_y_test == np.array(weighted_class))[0]
class_weight = dict([(i, standard_weight) for i in range(num_classes)])
class_weight[weighted_class] = high_weight
sample_weight = np.ones((y_train.shape[0])) * standard_weight
sample_weight[int_y_train == weighted_class] = high_weight
return ((x_train, y_train), (x_test, y_test),
(sample_weight, class_weight, test_ids))
def create_sequential_model():
model = Sequential()
model.add(Dense(32, input_shape=(input_dim,)))
model.add(Activation('relu'))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
return model
def create_temporal_sequential_model():
model = Sequential()
model.add(GRU(32, input_shape=(timesteps, input_dim), return_sequences=True))
model.add(TimeDistributed(Dense(num_classes)))
model.add(Activation('softmax'))
return model
def test_sequential_class_weights():
model = create_sequential_model()
model.compile(loss=loss, optimizer='rmsprop')
((x_train, y_train), (x_test, y_test),
(sample_weight, class_weight, test_ids)) = _get_test_data()
model.fit(x_train, y_train, batch_size=batch_size,
epochs=epochs // 3, verbose=0,
class_weight=class_weight,
validation_data=(x_train, y_train, sample_weight))
model.fit(x_train, y_train, batch_size=batch_size,
epochs=epochs // 2, verbose=0,
class_weight=class_weight)
model.fit(x_train, y_train, batch_size=batch_size,
epochs=epochs // 2, verbose=0,
class_weight=class_weight,
validation_split=0.1)
model.train_on_batch(x_train[:32], y_train[:32],
class_weight=class_weight)
score = model.evaluate(x_test[test_ids, :], y_test[test_ids, :], verbose=0)
assert(score < standard_score_sequential)
def test_sequential_sample_weights():
model = create_sequential_model()
model.compile(loss=loss, optimizer='rmsprop')
((x_train, y_train), (x_test, y_test),
(sample_weight, class_weight, test_ids)) = _get_test_data()
model.fit(x_train, y_train, batch_size=batch_size,
epochs=epochs // 3, verbose=0,
sample_weight=sample_weight)
model.fit(x_train, y_train, batch_size=batch_size,
epochs=epochs // 3, verbose=0,
sample_weight=sample_weight,
validation_split=0.1)
model.train_on_batch(x_train[:32], y_train[:32],
sample_weight=sample_weight[:32])
model.test_on_batch(x_train[:32], y_train[:32],
sample_weight=sample_weight[:32])
score = model.evaluate(x_test[test_ids, :], y_test[test_ids, :], verbose=0)
assert(score < standard_score_sequential)
def test_sequential_temporal_sample_weights():
((x_train, y_train), (x_test, y_test),
(sample_weight, class_weight, test_ids)) = _get_test_data()
temporal_x_train = np.reshape(x_train, (len(x_train), 1, x_train.shape[1]))
temporal_x_train = np.repeat(temporal_x_train, timesteps, axis=1)
temporal_x_test = np.reshape(x_test, (len(x_test), 1, x_test.shape[1]))
temporal_x_test = np.repeat(temporal_x_test, timesteps, axis=1)
temporal_y_train = np.reshape(y_train, (len(y_train), 1, y_train.shape[1]))
temporal_y_train = np.repeat(temporal_y_train, timesteps, axis=1)
temporal_y_test = np.reshape(y_test, (len(y_test), 1, y_test.shape[1]))
temporal_y_test = np.repeat(temporal_y_test, timesteps, axis=1)
temporal_sample_weight = np.reshape(sample_weight, (len(sample_weight), 1))
temporal_sample_weight = np.repeat(temporal_sample_weight, timesteps, axis=1)
model = create_temporal_sequential_model()
model.compile(loss=loss, optimizer='rmsprop',
sample_weight_mode='temporal')
model.fit(temporal_x_train, temporal_y_train, batch_size=batch_size,
epochs=epochs // 3, verbose=0,
sample_weight=temporal_sample_weight)
model.fit(temporal_x_train, temporal_y_train, batch_size=batch_size,
epochs=epochs // 3, verbose=0,
sample_weight=temporal_sample_weight,
validation_split=0.1)
model.train_on_batch(temporal_x_train[:32], temporal_y_train[:32],
sample_weight=temporal_sample_weight[:32])
model.test_on_batch(temporal_x_train[:32], temporal_y_train[:32],
sample_weight=temporal_sample_weight[:32])
score = model.evaluate(temporal_x_test[test_ids], temporal_y_test[test_ids],
verbose=0)
assert(score < standard_score_sequential)
def test_class_weight_wrong_classes():
model = create_sequential_model()
model.compile(loss=loss, optimizer='rmsprop')
((x_train, y_train), (x_test, y_test),
(sample_weight, class_weight, test_ids)) = _get_test_data()
del class_weight[1]
with pytest.raises(ValueError):
model.fit(x_train, y_train,
epochs=0, verbose=0, class_weight=class_weight)
if __name__ == '__main__':
pytest.main([__file__])
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