def test_cnn_iter_fit():
X = np.random.standard_normal((10, 2 * 100 * 50))
Z = np.random.random((10, 1)) > 0.5
X, Z = theano_floatx(X, Z)
m = Cnn(
100 * 50,
[10, 15],
[20, 12],
1,
['sigmoid', 'sigmoid'],
['rectifier', 'rectifier'],
'sigmoid',
'cat_ce',
100,
50,
2,
optimizer=('rmsprop', {
'step_rate': 1e-4,
'decay': 0.9
}),
batch_size=2,
max_iter=10,
pool_shapes=[(2, 2), (2, 2)],
filter_shapes=[(4, 4), (3, 3)],
)
for i, info in enumerate(m.iter_fit(X, Z)):
if i >= 10:
break
def test_cnn_iter_fit():
X = np.random.standard_normal((10, 2 * 100 * 50))
Z = np.random.random((10, 1)) > 0.5
X, Z = theano_floatx(X, Z)
m = Cnn(100 * 50, [10, 15], [20, 12], 1,
['sigmoid', 'sigmoid'], ['rectifier', 'rectifier'],
'sigmoid',
'cat_ce', 100, 50, 2,
optimizer=('rmsprop', {'step_rate': 1e-4, 'decay': 0.9}),
batch_size=2,
max_iter=10,
pool_shapes=[(2, 2), (2, 2)],
filter_shapes=[(4, 4), (3, 3)],
)
for i, info in enumerate(m.iter_fit(X, Z)):
if i >= 10:
break
