def testNNMultipleInputs(self):
nn = blocks_std.NN(10, bias=blocks_std.Bias(0), act=tf.tanh)
x = [tf.placeholder(dtype=tf.float32, shape=[5, 7]),
tf.placeholder(dtype=tf.float32, shape=[5, 3]),
tf.placeholder(dtype=tf.float32, shape=[5, 5])]
y = nn(*x)
xs = self.CheckNN(y, nn, 'Tanh')
self.assertEqual(len(x), len(xs))
for u, v in zip(x, xs):
self.assertIs(u, v)
def testConv2DBias(self):
input_shape = [19, 14, 14, 64]
filter_shape = [3, 7, 64, 128]
strides = [1, 2, 2, 1]
output_shape = [19, 6, 4, 128]
conv = blocks_std.Conv2D(depth=filter_shape[-1],
filter_size=filter_shape[0:2],
strides=strides[1:3],
padding='VALID',
act=None,
bias=blocks_std.Bias(1))
x = tf.placeholder(dtype=tf.float32, shape=input_shape)
y = conv(x)
self.CheckBiasAdd(y, conv._bias)
self.assertEqual(output_shape, y.get_shape().as_list())
def testNNWithBiasWithAct(self): nn = blocks_std.NN(10, bias=blocks_std.Bias(0), act=tf.square) x = tf.placeholder(dtype=tf.float32, shape=[5, 7]) y = nn(x) self.assertIs(x, self.CheckNN(y, nn, 'Square')[0])