mirror of
https://github.com/wassname/keras-contrib.git
synced 2026-06-27 16:10:11 +08:00
nzw
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
from __future__ import print_function
|
|
from keras.utils.test_utils import get_test_data
|
|
from keras.models import Sequential
|
|
from keras.layers.core import Dense, Activation
|
|
from keras.utils.np_utils import to_categorical
|
|
from keras_contrib import optimizers
|
|
import pytest
|
|
import numpy as np
|
|
np.random.seed(1337)
|
|
|
|
|
|
(X_train, y_train), (X_test, y_test) = get_test_data(num_train=1000,
|
|
num_test=200,
|
|
input_shape=(10,),
|
|
classification=True,
|
|
num_classes=2)
|
|
y_train = to_categorical(y_train)
|
|
y_test = to_categorical(y_test)
|
|
|
|
|
|
def get_model(input_dim, nb_hidden, output_dim):
|
|
model = Sequential()
|
|
model.add(Dense(nb_hidden, input_shape=(input_dim,)))
|
|
model.add(Activation('relu'))
|
|
model.add(Dense(output_dim))
|
|
model.add(Activation('softmax'))
|
|
return model
|
|
|
|
|
|
def _test_optimizer(optimizer, target=0.89):
|
|
model = get_model(X_train.shape[1], 10, y_train.shape[1])
|
|
model.compile(loss='categorical_crossentropy',
|
|
optimizer=optimizer,
|
|
metrics=['accuracy'])
|
|
history = model.fit(X_train, y_train, epochs=12, batch_size=16,
|
|
validation_data=(X_test, y_test), verbose=2)
|
|
config = optimizer.get_config()
|
|
assert type(config) == dict
|
|
assert history.history['val_acc'][-1] >= target
|
|
|
|
|
|
if __name__ == '__main__':
|
|
pytest.main([__file__])
|