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