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romanamo 2024-05-20 15:23:35 +02:00
parent 571c5e03f4
commit bea4c2a20c
2 changed files with 6 additions and 9 deletions
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10
uebungen/aufgabe2/uebung2.py
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@ -31,7 +31,6 @@ delta_L2 = np.ones((hid_size, out_size)) * 0.1
w0 = np.random.random((inp_size, hid_size)) - 0.5 w0 = np.random.random((inp_size, hid_size)) - 0.5
w1 = np.random.random((hid_size, out_size)) - 0.5 w1 = np.random.random((hid_size, out_size)) - 0.5
def multiply_learnrate(old, new): def multiply_learnrate(old, new):
if old * new > 0: if old * new > 0:
return 1.2 return 1.2
@ -61,9 +60,10 @@ for i in range(10000):
L1_error = np.matmul(L2_delta, w1.T) L1_error = np.matmul(L2_delta, w1.T)
L1_delta = L1_error * deriv_sigmoid(L1) L1_delta = L1_error * deriv_sigmoid(L1)
# Gradienten # Gradienten berechnen
L2_grad_new = np.matmul(L1.T, L2_delta)
L1_grad_new = np.matmul(L0.T, L1_delta) L1_grad_new = np.matmul(L0.T, L1_delta)
L2_grad_new = np.matmul(L1.T, L2_delta)
# Gewichte aktualisieren # Gewichte aktualisieren
@ -72,10 +72,10 @@ for i in range(10000):
delta_L2 = np.clip( delta_L2 = np.clip(
delta_L2 * v_multiply_learnrate(L2_grad_old, L2_grad_new), 0, 50) delta_L2 * v_multiply_learnrate(L2_grad_old, L2_grad_new), 0, 50)
w1 -= np.sign(L2_grad_new) * delta_L2
w0 -= np.sign(L1_grad_new) * delta_L1 w0 -= np.sign(L1_grad_new) * delta_L1
w1 -= np.sign(L2_grad_new) * delta_L2
# Gradienten aktualisieren # Gradienten aktualisieren bzw. kopieren
L1_grad_old = np.copy(L1_grad_new) L1_grad_old = np.copy(L1_grad_new)
L2_grad_old = np.copy(L2_grad_new) L2_grad_old = np.copy(L2_grad_new)

5
uebungen/aufgabe4/uebung4.py
View File

@ -36,7 +36,6 @@ def pool(image, stride=1, kernel_size=(2,2), transform=np.max, padding_size=(0,0
next = np.zeros((height, width)) next = np.zeros((height, width))
#'zero'|'mirror'
for i in range(height): for i in range(height):
for j in range(width): for j in range(width):
# Translate upper left corner into original image space # Translate upper left corner into original image space
@ -45,7 +44,6 @@ def pool(image, stride=1, kernel_size=(2,2), transform=np.max, padding_size=(0,0
# Retrieve image window # Retrieve image window
view = padded[y:y+kernel_size[0], x:x+kernel_size[1]] view = padded[y:y+kernel_size[0], x:x+kernel_size[1]]
#print(view)
# Set pooled image value # Set pooled image value
next[i][j] = transform(view) next[i][j] = transform(view)
@ -60,10 +58,9 @@ weights = np.array([
]) ])
new1 = pool(image, kernel_size=(5,5), padding_size=(2,2), transform=convolute(weights)) new1 = pool(image, kernel_size=(5,5), padding_size=(2,2), transform=convolute(weights))
new2 = pool(new1, stride=3, kernel_size=(30,30), transform=np.max)
fig = plt.figure() fig = plt.figure()
fig.canvas.manager.set_window_title("A Cat") fig.canvas.manager.set_window_title("A Cat")
plt.imshow(new2, cmap="gray") plt.imshow(new1, cmap="gray")
plt.show() plt.show()