import os
import tempfile
import numpy as np
import pytest
from numpy.testing import assert_allclose
from keras import backend as K
from keras import losses
from keras import metrics
from keras import optimizers
from keras.models import save_model, load_model
from keras.utils import np_utils
from keras.utils.test_utils import keras_test
from keras_gcnn.applications.densenetnew import GDenseNet
skipif_no_tf_gpu = pytest.mark.skipif(
(K.backend() != 'tensorflow') or (not K.tensorflow_backend._get_available_gpus()),
reason='Requires TensorFlow backend and a GPU')
@keras_test
def test_functional_model_saving():
img_rows, img_cols = 32, 32
img_channels = 3
# Parameters for the DenseNet model builder
img_dim = (img_channels, img_rows, img_cols) if K.image_data_format() == 'channels_first' else (
img_rows, img_cols, img_channels)
depth = 40
nb_dense_block = 3
growth_rate = 3 # number of z2 maps equals growth_rate * group_size, so keep this small.
nb_filter = 16
dropout_rate = 0.0 # 0.0 for data augmentation
conv_group = 'D4' # C4 includes 90 degree rotations, D4 additionally includes reflections in x and y axis.
use_gcnn = True
# Create the model (without loading weights)
model = GDenseNet(mc_dropout=False, padding='same', nb_dense_block=nb_dense_block, growth_rate=growth_rate,
nb_filter=nb_filter, dropout_rate=dropout_rate, weights=None, input_shape=img_dim, depth=depth,
use_gcnn=use_gcnn, conv_group=conv_group)
model.compile(loss=losses.categorical_crossentropy,
optimizer=optimizers.Adam(),
metrics=[metrics.categorical_accuracy])
x = np.random.random((1, 32, 32, 3))
y = np.random.randint(0, 10, 1)
y = np_utils.to_categorical(y, 10)
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
model = load_model(fname)
os.remove(fname)
out2 = model.predict(x)
assert_allclose(out, out2, atol=1e-05)
if __name__ == '__main__':
pytest.main([__file__])
