Python/neural_network/gan.py_tf
Christian Clauss 652b891a75
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2020-02-23 04:23:00 +01:00

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import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from sklearn.utils import shuffle
import input_data
random_numer = 42
np.random.seed(random_numer)
def ReLu(x):
mask = (x > 0) * 1.0
return mask * x
def d_ReLu(x):
mask = (x > 0) * 1.0
return mask
def arctan(x):
return np.arctan(x)
def d_arctan(x):
return 1 / (1 + x ** 2)
def log(x):
return 1 / (1 + np.exp(-1 * x))
def d_log(x):
return log(x) * (1 - log(x))
def tanh(x):
return np.tanh(x)
def d_tanh(x):
return 1 - np.tanh(x) ** 2
def plot(samples):
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(4, 4)
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples):
ax = plt.subplot(gs[i])
plt.axis("off")
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect("equal")
plt.imshow(sample.reshape(28, 28), cmap="Greys_r")
return fig
if __name__ == "__main__":
# 1. Load Data and declare hyper
print("--------- Load Data ----------")
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
temp = mnist.test
images, labels = temp.images, temp.labels
images, labels = shuffle(np.asarray(images), np.asarray(labels))
num_epoch = 10
learing_rate = 0.00009
G_input = 100
hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686
print("--------- Declare Hyper Parameters ----------")
# 2. Declare Weights
D_W1 = (
np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0)))
* 0.002
)
# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
D_b1 = np.zeros(hidden_input)
D_W2 = (
np.random.normal(
size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0))
)
* 0.002
)
# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.))) *0.002
D_b2 = np.zeros(1)
G_W1 = (
np.random.normal(
size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0))
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b1 = np.zeros(hidden_input)
G_W2 = (
np.random.normal(
size=(hidden_input, hidden_input2),
scale=(1.0 / np.sqrt(hidden_input / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b2 = np.zeros(hidden_input2)
G_W3 = (
np.random.normal(
size=(hidden_input2, hidden_input3),
scale=(1.0 / np.sqrt(hidden_input2 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b3 = np.zeros(hidden_input3)
G_W4 = (
np.random.normal(
size=(hidden_input3, hidden_input4),
scale=(1.0 / np.sqrt(hidden_input3 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b4 = np.zeros(hidden_input4)
G_W5 = (
np.random.normal(
size=(hidden_input4, hidden_input5),
scale=(1.0 / np.sqrt(hidden_input4 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b5 = np.zeros(hidden_input5)
G_W6 = (
np.random.normal(
size=(hidden_input5, hidden_input6),
scale=(1.0 / np.sqrt(hidden_input5 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b6 = np.zeros(hidden_input6)
G_W7 = (
np.random.normal(
size=(hidden_input6, 784), scale=(1.0 / np.sqrt(hidden_input6 / 2.0))
)
* 0.002
)
# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.))) *0.002
G_b7 = np.zeros(784)
# 3. For Adam Optimzier
v1, m1 = 0, 0
v2, m2 = 0, 0
v3, m3 = 0, 0
v4, m4 = 0, 0
v5, m5 = 0, 0
v6, m6 = 0, 0
v7, m7 = 0, 0
v8, m8 = 0, 0
v9, m9 = 0, 0
v10, m10 = 0, 0
v11, m11 = 0, 0
v12, m12 = 0, 0
v13, m13 = 0, 0
v14, m14 = 0, 0
v15, m15 = 0, 0
v16, m16 = 0, 0
v17, m17 = 0, 0
v18, m18 = 0, 0
beta_1, beta_2, eps = 0.9, 0.999, 0.00000001
print("--------- Started Training ----------")
for iter in range(num_epoch):
random_int = np.random.randint(len(images) - 5)
current_image = np.expand_dims(images[random_int], axis=0)
# Func: Generate The first Fake Data
Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
# Func: Forward Feed for Real data
Dl1_r = current_image.dot(D_W1) + D_b1
Dl1_rA = ReLu(Dl1_r)
Dl2_r = Dl1_rA.dot(D_W2) + D_b2
Dl2_rA = log(Dl2_r)
# Func: Forward Feed for Fake Data
Dl1_f = current_fake_data.dot(D_W1) + D_b1
Dl1_fA = ReLu(Dl1_f)
Dl2_f = Dl1_fA.dot(D_W2) + D_b2
Dl2_fA = log(Dl2_f)
# Func: Cost D
D_cost = -np.log(Dl2_rA) + np.log(1.0 - Dl2_fA)
# Func: Gradient
grad_f_w2_part_1 = 1 / (1.0 - Dl2_fA)
grad_f_w2_part_2 = d_log(Dl2_f)
grad_f_w2_part_3 = Dl1_fA
grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)
grad_f_b2 = grad_f_w2_part_1 * grad_f_w2_part_2
grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)
grad_f_w1_part_2 = d_ReLu(Dl1_f)
grad_f_w1_part_3 = current_fake_data
grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)
grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2
grad_r_w2_part_1 = -1 / Dl2_rA
grad_r_w2_part_2 = d_log(Dl2_r)
grad_r_w2_part_3 = Dl1_rA
grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)
grad_r_b2 = grad_r_w2_part_1 * grad_r_w2_part_2
grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)
grad_r_w1_part_2 = d_ReLu(Dl1_r)
grad_r_w1_part_3 = current_image
grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)
grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2
grad_w1 = grad_f_w1 + grad_r_w1
grad_b1 = grad_f_b1 + grad_r_b1
grad_w2 = grad_f_w2 + grad_r_w2
grad_b2 = grad_f_b2 + grad_r_b2
# ---- Update Gradient ----
m1 = beta_1 * m1 + (1 - beta_1) * grad_w1
v1 = beta_2 * v1 + (1 - beta_2) * grad_w1 ** 2
m2 = beta_1 * m2 + (1 - beta_1) * grad_b1
v2 = beta_2 * v2 + (1 - beta_2) * grad_b1 ** 2
m3 = beta_1 * m3 + (1 - beta_1) * grad_w2
v3 = beta_2 * v3 + (1 - beta_2) * grad_w2 ** 2
m4 = beta_1 * m4 + (1 - beta_1) * grad_b2
v4 = beta_2 * v4 + (1 - beta_2) * grad_b2 ** 2
D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 / (1 - beta_2)) + eps)) * (
m1 / (1 - beta_1)
)
D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 / (1 - beta_2)) + eps)) * (
m2 / (1 - beta_1)
)
D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 / (1 - beta_2)) + eps)) * (
m3 / (1 - beta_1)
)
D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 / (1 - beta_2)) + eps)) * (
m4 / (1 - beta_1)
)
# Func: Forward Feed for G
Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
Dl1 = current_fake_data.dot(D_W1) + D_b1
Dl1_A = ReLu(Dl1)
Dl2 = Dl1_A.dot(D_W2) + D_b2
Dl2_A = log(Dl2)
# Func: Cost G
G_cost = -np.log(Dl2_A)
# Func: Gradient
grad_G_w7_part_1 = ((-1 / Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(
D_W1.T
)
grad_G_w7_part_2 = d_log(Gl7)
grad_G_w7_part_3 = Gl6A
grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_1)
grad_G_b7 = grad_G_w7_part_1 * grad_G_w7_part_2
grad_G_w6_part_1 = (grad_G_w7_part_1 * grad_G_w7_part_2).dot(G_W7.T)
grad_G_w6_part_2 = d_ReLu(Gl6)
grad_G_w6_part_3 = Gl5A
grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2)
grad_G_b6 = grad_G_w6_part_1 * grad_G_w6_part_2
grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T)
grad_G_w5_part_2 = d_tanh(Gl5)
grad_G_w5_part_3 = Gl4A
grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2)
grad_G_b5 = grad_G_w5_part_1 * grad_G_w5_part_2
grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T)
grad_G_w4_part_2 = d_ReLu(Gl4)
grad_G_w4_part_3 = Gl3A
grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2)
grad_G_b4 = grad_G_w4_part_1 * grad_G_w4_part_2
grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T)
grad_G_w3_part_2 = d_arctan(Gl3)
grad_G_w3_part_3 = Gl2A
grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2)
grad_G_b3 = grad_G_w3_part_1 * grad_G_w3_part_2
grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T)
grad_G_w2_part_2 = d_ReLu(Gl2)
grad_G_w2_part_3 = Gl1A
grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2)
grad_G_b2 = grad_G_w2_part_1 * grad_G_w2_part_2
grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T)
grad_G_w1_part_2 = d_arctan(Gl1)
grad_G_w1_part_3 = Z
grad_G_w1 = grad_G_w1_part_3.T.dot(grad_G_w1_part_1 * grad_G_w1_part_2)
grad_G_b1 = grad_G_w1_part_1 * grad_G_w1_part_2
# ---- Update Gradient ----
m5 = beta_1 * m5 + (1 - beta_1) * grad_G_w1
v5 = beta_2 * v5 + (1 - beta_2) * grad_G_w1 ** 2
m6 = beta_1 * m6 + (1 - beta_1) * grad_G_b1
v6 = beta_2 * v6 + (1 - beta_2) * grad_G_b1 ** 2
m7 = beta_1 * m7 + (1 - beta_1) * grad_G_w2
v7 = beta_2 * v7 + (1 - beta_2) * grad_G_w2 ** 2
m8 = beta_1 * m8 + (1 - beta_1) * grad_G_b2
v8 = beta_2 * v8 + (1 - beta_2) * grad_G_b2 ** 2
m9 = beta_1 * m9 + (1 - beta_1) * grad_G_w3
v9 = beta_2 * v9 + (1 - beta_2) * grad_G_w3 ** 2
m10 = beta_1 * m10 + (1 - beta_1) * grad_G_b3
v10 = beta_2 * v10 + (1 - beta_2) * grad_G_b3 ** 2
m11 = beta_1 * m11 + (1 - beta_1) * grad_G_w4
v11 = beta_2 * v11 + (1 - beta_2) * grad_G_w4 ** 2
m12 = beta_1 * m12 + (1 - beta_1) * grad_G_b4
v12 = beta_2 * v12 + (1 - beta_2) * grad_G_b4 ** 2
m13 = beta_1 * m13 + (1 - beta_1) * grad_G_w5
v13 = beta_2 * v13 + (1 - beta_2) * grad_G_w5 ** 2
m14 = beta_1 * m14 + (1 - beta_1) * grad_G_b5
v14 = beta_2 * v14 + (1 - beta_2) * grad_G_b5 ** 2
m15 = beta_1 * m15 + (1 - beta_1) * grad_G_w6
v15 = beta_2 * v15 + (1 - beta_2) * grad_G_w6 ** 2
m16 = beta_1 * m16 + (1 - beta_1) * grad_G_b6
v16 = beta_2 * v16 + (1 - beta_2) * grad_G_b6 ** 2
m17 = beta_1 * m17 + (1 - beta_1) * grad_G_w7
v17 = beta_2 * v17 + (1 - beta_2) * grad_G_w7 ** 2
m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7
v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2
G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 / (1 - beta_2)) + eps)) * (
m5 / (1 - beta_1)
)
G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 / (1 - beta_2)) + eps)) * (
m6 / (1 - beta_1)
)
G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 / (1 - beta_2)) + eps)) * (
m7 / (1 - beta_1)
)
G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 / (1 - beta_2)) + eps)) * (
m8 / (1 - beta_1)
)
G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 / (1 - beta_2)) + eps)) * (
m9 / (1 - beta_1)
)
G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 / (1 - beta_2)) + eps)) * (
m10 / (1 - beta_1)
)
G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 / (1 - beta_2)) + eps)) * (
m11 / (1 - beta_1)
)
G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 / (1 - beta_2)) + eps)) * (
m12 / (1 - beta_1)
)
G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 / (1 - beta_2)) + eps)) * (
m13 / (1 - beta_1)
)
G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 / (1 - beta_2)) + eps)) * (
m14 / (1 - beta_1)
)
G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 / (1 - beta_2)) + eps)) * (
m15 / (1 - beta_1)
)
G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 / (1 - beta_2)) + eps)) * (
m16 / (1 - beta_1)
)
G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 / (1 - beta_2)) + eps)) * (
m17 / (1 - beta_1)
)
G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 / (1 - beta_2)) + eps)) * (
m18 / (1 - beta_1)
)
# --- Print Error ----
# print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')
if iter == 0:
learing_rate = learing_rate * 0.01
if iter == 40:
learing_rate = learing_rate * 0.01
# ---- Print to Out put ----
if iter % 10 == 0:
print(
"Current Iter: ",
iter,
" Current D cost:",
D_cost,
" Current G cost: ",
G_cost,
end="\r",
)
print("--------- Show Example Result See Tab Above ----------")
print("--------- Wait for the image to load ---------")
Z = np.random.uniform(-1.0, 1.0, size=[16, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
fig = plot(current_fake_data)
fig.savefig(
"Click_Me_{}.png".format(
str(iter).zfill(3)
+ "_Ginput_"
+ str(G_input)
+ "_hiddenone"
+ str(hidden_input)
+ "_hiddentwo"
+ str(hidden_input2)
+ "_LR_"
+ str(learing_rate)
),
bbox_inches="tight",
)
# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
# -- end code --