def check_AddGate():
x = np.random.randn(10, 3)
# b = np.random.randn(1, 3)
b = np.random.randn(1, 1)
df = np.random.randn(10, 3)
grad_x = eval_numerical_gradient_array(lambda t: AddGate.forward(t, b), x,
df)
grad_b = eval_numerical_gradient_array(lambda t: AddGate.forward(x, t), b,
df)
dx, db = AddGate.backward(x, b, df)
print sum_abs_err(grad_x, dx)
print sum_abs_err(grad_b, db)
def check_MultiGate():
x = np.random.randn(10, 4)
w = np.random.randn(4, 6)
df = np.random.randn(10, 6)
grad_x = eval_numerical_gradient_array(lambda a: MulGate.forward(w, a), x,
df)
grad_w = eval_numerical_gradient_array(lambda b: MulGate.forward(b, x), w,
df)
dw, dx = MulGate.backward(w, x, df)
print np.sum(np.abs(grad_x - dx))
print np.sum(np.abs(grad_w - dw))
def check_ReLUGate():
x = np.random.randn(10, 4)
df = np.random.randn(10, 4)
grad_x = eval_numerical_gradient_array(ReLUGate.forward, x, df)
dx = ReLUGate.backward(x, df)
print sum_abs_err(grad_x, dx)
def check_LinearGate():
x = np.random.randn(10, 4).astype(np.double)
dy = np.random.randn(10, 4).astype(np.double)
grad_x = eval_numerical_gradient_array(LinearGate.forward, x, dy)
dx = LinearGate.backward(x, dy)
print grad_x
print dx
print np.sum(np.abs(grad_x - dx))
def check_Convolution2D():
N, C, H, W = 1, 1, 4, 5
KN, KC, KH, KW = 1, C, 3, 3
stride = 1
x = np.random.rand(N, C, H, W)
w = np.random.rand(KN, KC, KH, KW)
conv_layer = Convolution2D(nb_kernel=KN,
kernel_height=KH,
kernel_width=KW,
input_shape=(C, H, W))
conv_layer.build()
# =========================================================
# experiment x
z = conv_layer.forward(x)
d_z = z
d_x = conv_layer.backward(d_z)
grad_x = eval_numerical_gradient_array(conv_layer.forward, x, d_z)
# print d_x
# print grad_x
print sum_abs_err(d_x, grad_x)
# =========================================================
# experiment w
z = conv_layer.forward(x)
d_z = z
conv_layer.backward(d_z)
d_w = conv_layer.grads[0]
def w_forword(layer, xx, ww):
layer.w = ww
return layer.forward(xx)
grad_w = eval_numerical_gradient_array(
lambda t: w_forword(conv_layer, x, t), w, d_z)
# print d_w
# print grad_w
print sum_abs_err(d_w, grad_w)
def check_MeanPooling2D():
layer = MeanPooling2D(pooling_size=[2, 2], input_shape=(1, 6, 6))
layer.build()
x = np.arange(36).reshape(1, 1, 6, 6).astype(np.double)
# print x
z = layer.forward(x)
d_z = z
d_x = layer.backward(d_z)
grad_x = eval_numerical_gradient_array(layer.forward, x, d_z)
# print d_x
# print grad_x
print sum_abs_err(d_x, grad_x)
