def testComputeShape(self):
shape = core_layers.Flatten().compute_output_shape((1, 2, 3, 2))
self.assertEqual(shape.as_list(), [1, 12])
shape = core_layers.Flatten().compute_output_shape((None, 3, 2))
self.assertEqual(shape.as_list(), [None, 6])
shape = core_layers.Flatten().compute_output_shape((None, 3, None))
self.assertEqual(shape.as_list(), [None, None])
def testFlattenUnknownAxes(self):
with self.cached_session() as sess:
x = array_ops.placeholder(shape=(5, None, None), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((5, 2, 3))})
self.assertEqual(list(np_output.shape), [5, 6])
self.assertEqual(y.get_shape().as_list(), [5, None])
x = array_ops.placeholder(shape=(5, None, 2), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((5, 3, 2))})
self.assertEqual(list(np_output.shape), [5, 6])
self.assertEqual(y.get_shape().as_list(), [5, None])
def testCreateFlatten(self):
with self.cached_session() as sess:
x = array_ops.placeholder(shape=(None, 2, 3), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((3, 2, 3))})
self.assertEqual(list(np_output.shape), [3, 6])
self.assertEqual(y.get_shape().as_list(), [None, 6])
x = array_ops.placeholder(shape=(1, 2, 3, 2), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((1, 2, 3, 2))})
self.assertEqual(list(np_output.shape), [1, 12])
self.assertEqual(y.get_shape().as_list(), [1, 12])
def testFlatten0D(self):
x = array_ops.placeholder(shape=(None, ), dtype='float32')
y = core_layers.Flatten()(x)
with self.cached_session() as sess:
np_output = sess.run(y, feed_dict={x: np.zeros((5, ))})
self.assertEqual(list(np_output.shape), [5, 1])
self.assertEqual(y.shape.as_list(), [None, 1])
def testDataFormat4d(self):
np_input_channels_last = np.arange(24, dtype='float32').reshape(
[1, 4, 3, 2])
with self.test_session() as sess:
x = array_ops.placeholder(shape=(1, 4, 3, 2), dtype='float32')
y = core_layers.Flatten(data_format='channels_last')(x)
np_output_cl = sess.run(y, feed_dict={x: np_input_channels_last})
x = array_ops.placeholder(shape=(1, 2, 4, 3), dtype='float32')
y = core_layers.Flatten(data_format='channels_first')(x)
np_input_channels_first = np.transpose(np_input_channels_last,
[0, 3, 1, 2])
np_output_cf = sess.run(y, feed_dict={x: np_input_channels_first})
self.assertAllEqual(np_output_cl, np_output_cf)
def testFlattenLayer(self):
flatten_layer = core.Flatten()
x = constant_op.constant([[[-10, -20], [-30, -40]], [[10, 20],
[30, 40]]])
y = flatten_layer(x)
self.assertAllEqual([[-10, -20, -30, -40], [10, 20, 30, 40]],
y.numpy())
def testFlattenLargeDim(self):
if any(platform.win32_ver()):
self.skipTest(
'values are truncated on windows causing test failures')
x = array_ops.placeholder(shape=(None, 21316, 21316, 80),
dtype='float32')
y = core_layers.Flatten()(x)
self.assertEqual(y.shape.as_list(), [None, 21316 * 21316 * 80])
def build_layer_fn(x, w_initializer, b_initializer):
x = layers_core.Flatten()(x)
layer = layers_core.Dense(units=3,
kernel_initializer=w_initializer,
bias_initializer=b_initializer)
net = layer.apply(x)
expected_normalized_vars = {'tf.layers.Dense.kernel': layer.kernel}
expected_not_normalized_vars = {'tf.layers.Dense.bias': layer.bias}
return net, expected_normalized_vars, expected_not_normalized_vars
def testFlattenLargeBatchDim(self):
batch_size = np.iinfo(np.int32).max + 10
x = array_ops.placeholder(shape=(batch_size, None, None, 1),
dtype='float32')
y = core_layers.Flatten()(x)
self.assertEqual(y.shape.as_list(), [batch_size, None])
def testFlattenValueError(self):
x = array_ops.placeholder(shape=(None, ), dtype='float32')
with self.assertRaises(ValueError):
core_layers.Flatten()(x)