def test_backward(self):
conv_params = {'stride': 2, 'pad': 1}
nr_img = 2
sz_img = 4
nr_in_channel = 3
sz_filter = 4
nr_filter = 3
a = np.random.randn(2, 1, 3, 2)
p = np.array([1, 0, 1, 0, 1, 0, 1, 1, 1, 2, 3, 2, 1, 0, 1, 2, 1,
2]).reshape(1, 2, 3, 3)
x = np.linspace(-.1, .5, 2 * 3 * 4 * 4).reshape(2, 3, 4, 4)
w = np.linspace(-0.2, 0.3, 3 * 3 * 4 * 6).reshape(3, 3, 4, 6)
x = np.linspace(-.1, .5, x_size).reshape(nr_img, nr_in_channel, sz_img,
sz_img)
w = np.linspace(-0.2, 0.3, w_size).reshape(nr_filter, nr_in_channel,
sz_filter, sz_filter)
dout = np.random.randn(nr_img, nr_in_channel - 1, sz_img,
4) # standin for the derivitive from next layer
dx_num = eval_numerical_gradient_array(
lambda x: conv_forward(x, w, conv_params)[0], x, dout)
dw_num = eval_numerical_gradient_array(
lambda w: conv_forward(x, w, conv_params)[0], w, dout)
y, cache = conv_forward(x, w, conv_param=conv_params)
dx, dw = convolution_backward(dout, cache)
# self.assertEqual(tpose1230(p).all(), p.transpose(1, 2, 3, 0).all())
# self.assertEqual(tpose1230(w).all(), w.transpose(1, 2, 3, 0).all())
# self.assertEqual(tpose1230(x).all(), x.transpose(1, 2, 3, 0).all())
self.assertTrue(np.array_equal(tpose1230(a), a.transpose(1, 2, 3, 0)))
self.assertTrue(np.array_equal(tpose1230(p), p.transpose(1, 2, 3, 0)))
self.assertTrue(np.array_equal(tpose1230(w), w.transpose(1, 2, 3, 0)))
self.assertTrue(np.array_equal(tpose1230(x), x.transpose(1, 2, 3, 0)))
self.assertEqual(a.shape[0], a.transpose(1, 2, 3, 0).shape[3])
self.assertEqual(a.shape[1], a.transpose(1, 2, 3, 0).shape[0])
self.assertEqual(a.shape[2], a.transpose(1, 2, 3, 0).shape[1])
self.assertEqual(a.shape[3], a.transpose(1, 2, 3, 0).shape[2])
# print()
# print(tpose1230(p).flatten())
# print()
# print(list(p.transpose(1, 2, 3, 0).flatten()))
# print()
# print(list(x.transpose(1, 2, 3, 0).flatten()))
# print()
print(list(w.transpose(1, 2, 3, 0).flatten()))
def test_conv_backward_copy_231_assign(self):
np.random.seed(231)
x = np.random.randn(4, 3, 5, 5)
w = np.random.randn(2, 3, 3, 3)
dout = np.random.randn(4, 2, 5,
5) # standin for the derivitive from next layer
conv_param = {'stride': 1, 'pad': 1}
dx_num = eval_numerical_gradient_array(
lambda x: conv_forward(x, w, conv_param)[0], x, dout)
dw_num = eval_numerical_gradient_array(
lambda w: conv_forward(x, w, conv_param)[0], w, dout)
out, cache = conv_forward(x, w, conv_param)
dx, dw = convolution_backward(dout, cache)
# print(dw_num)
# print(dw)
# Your errors should be around e-8 or less.
print('Testing conv_backward_naive function')
print('dx error: ', rel_error(dx, dx_num))
print('dw error: ', rel_error(dw, dw_num))
def test_backward_from_picture(self):
conv_params = {'stride': 1, 'pad': 0}
nr_img = 1
sz_img = 3
channels = 2 # input channels
sz_filter = 2
nr_filter = 2 # num output channels
# x = np.random.randn(nr_img, nr_in_channel, sz_img, 4)
# w = np.random.randn(nr_filter, nr_in_channel, sz_filter, sz_filter)
x = np.array([1, 0, 1, 0, 1, 0, 1, 1, 1, 2, 3, 2, 1, 0, 1, 2, 1, 2],
dtype=float).reshape(nr_img, channels, sz_img, sz_img)
w = np.array([1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 2, 2],
dtype=float).reshape(nr_filter, channels, sz_filter,
sz_filter)
dout = np.array([0.7, -1.08,
1.99, -1.36, .3, -.9, 1.22, -.75]).reshape(
1, 2, 2,
2) # standin for the derivative from next layer
dx_num = eval_numerical_gradient_array(
lambda x: conv_forward(x, w, conv_params)[0], x, dout)
dw_num = eval_numerical_gradient_array(
lambda w: conv_forward(x, w, conv_params)[0], w, dout)
y, cache = conv_forward(x, w, conv_param=conv_params)
dx, dw = convolution_backward(dout, cache)
print(list(dx.flatten()))
print(list(dw.flatten()))
# Your errors should be around e-8 or less.
print('Testing conv_backward_naive function')
print('dx error: ', rel_error(dx, dx_num))
print('dw error: ', rel_error(dw, dw_num))
def test_backward_2(self):
conv_params = {'stride': 1, 'pad': 0}
nr_img = 1
sz_img = 3
channels = 2 # input channels
filter_rows = 3
filter_cols = 2 # num output channels
nr_filter = 2
x = np.array([1, 0, 1, 0, 1, 0, 1, 1, 1, 2, 3, 2, 1, 0, 1, 2, 1, 2],
dtype=float).reshape(nr_img, channels, sz_img, sz_img)
w = np.array([
1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 2, 2, 1, 0, 2, 4, 3, 1,
2, 4
],
dtype=float).reshape(nr_filter, channels, filter_rows,
filter_cols)
dout = np.array([0.7, -1.08, 1.99, -1.36]).reshape(
1, 2, 1, 2) # standin for the derivative from next layer
dx_num = eval_numerical_gradient_array(
lambda x: conv_forward(x, w, conv_params)[0], x, dout)
dw_num = eval_numerical_gradient_array(
lambda w: conv_forward(x, w, conv_params)[0], w, dout)
y, cache = conv_forward(x, w, conv_param=conv_params)
print(y.shape)
dx, dw = convolution_backward(dout, cache)
# # Your errors should be around e-8 or less.
print('Testing conv_backward_naive function')
print('dx error: ', rel_error(dx, dx_num))
print('dw error: ', rel_error(dw, dw_num))
def test_conv_forward_im2col(self):
conv_params = {'stride': 2, 'pad': 1}
nr_img = 2
sz_img = 4
nr_in_channel = 3
sz_filter = 4
nr_filter = 3
x_size = nr_img * nr_in_channel * sz_img * sz_img
w_size = nr_filter * nr_in_channel * sz_filter * sz_filter
x = np.linspace(-.1, .5, x_size).reshape(nr_img, nr_in_channel, sz_img,
sz_img)
w = np.linspace(-0.2, 0.3, w_size).reshape(nr_filter, nr_in_channel,
sz_filter, sz_filter)
y, cache = conv_forward(x, w, conv_param=conv_params)
def test_conv_forward_from_picture(self):
"""
this is the example from the picture we looked at... source is here
https://www.microsoft.com/en-us/research/uploads/prod/2018/05/spg-cnn-asplos17.pdf
"""
conv_params = {'stride': 1, 'pad': 0}
nr_img = 1
sz_img = 3
nr_in_channel = 2 # input channels
sz_filter = 2
nr_filter = 2 # num output channels
x = np.array([1, 0, 1, 0, 1, 0, 1, 1, 1, 2, 3, 2, 1, 0, 1, 2, 1,
2]).reshape(nr_img, nr_in_channel, sz_img, sz_img)
w = np.array([1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 2,
2]).reshape(nr_filter, nr_in_channel, sz_filter,
sz_filter)
y, cache = conv_forward(x, w, conv_param=conv_params)