def test_HorizontalKernel(self): nn = MLPR(layers=[ C("Rectifier", channels=7, kernel_shape=(16,1)), L("Linear", units=5)]) a_in = numpy.zeros((8,16,16,1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [256, 16 * 7, 5])
def test_SquareKernelFull(self): nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(3,3), border_mode='full'), L("Linear", units=5)]) a_in = numpy.zeros((8,32,32,1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 32 * 32 * 4, 5])
def test_VerticalKernel(self): nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(1,16)), L("Linear", units=7)]) a_in = numpy.zeros((8,16,16,1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [256, 16 * 4, 7])
def test_SquareKernelPool(self): nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(3,3), pool_shape=(2,2)), L("Linear", units=5)]) a_in = numpy.zeros((8,32,32,1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 15 * 15 * 4, 5])
def test_SquareKernelFull(self): nn = MLPR(layers=[ C("ExpLin", channels=4, kernel_shape=(3, 3), border_mode='full'), L("Linear", units=5) ]) a_in = numpy.zeros((8, 32, 32, 1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 4624, 5])
def test_SquareKernelPool(self): # TODO: After creation the outputs don't seem to correspond; pooling enabled? nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(3,3), pool_shape=(2,2)), L("Linear", units=5)]) a_in = numpy.zeros((8,32,32,1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 15 * 15 * 4, 5])
def test_SquareKernelPool(self): nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(3, 3), pool_shape=(2, 2)), L("Linear", units=5) ]) a_in = numpy.zeros((8, 32, 32, 1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 15 * 15 * 4, 5])
def test_VerticalKernel(self): nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(1, 16)), L("Linear", units=7) ]) a_in = numpy.zeros((8, 16, 16, 1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [256, 16 * 4, 7])
def test_HorizontalKernel(self): nn = MLPR(layers=[ C("Rectifier", channels=7, kernel_shape=(16, 1)), L("Linear", units=5) ]) a_in = numpy.zeros((8, 16, 16, 1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [256, 16 * 7, 5])
def test_Upscaling(self): nn = MLPR( layers=[ C("Rectifier", channels=4, kernel_shape=(1, 1), scale_factor=(2, 2), border_mode="same"), L("Linear", units=5), ] ) a_in = numpy.zeros((8, 32, 32, 1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 64 * 64 * 4, 5])
def test_SmallSquareKernel(self): nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(3, 3), border_mode='valid'), L("Linear", units=5) ]) a_in = numpy.zeros((8, 32, 32, 1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 30 * 30 * 4, 5])
def test_Upscaling(self): nn = MLPR(layers=[ C("Rectifier", channels=4, kernel_shape=(1, 1), scale_factor=(2, 2), border_mode='same'), L("Linear", units=5) ]) a_in = numpy.zeros((8, 32, 32, 1)) nn._create_specs(a_in) assert_equal(nn.unit_counts, [1024, 64 * 64 * 4, 5])