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Remove code with side effects from main (#1577)
* Remove code with side effects from main When running tests withy pytest, some modules execute code in main scope and open plot or browser windows. Moves such code under `if __name__ == "__main__"`. * fixup! Format Python code with psf/black push
This commit is contained in:
Mantas Zimnickas
Christian Clauss
parent
5616fa9e62
commit
12f69a86f5
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@ -6,97 +6,97 @@ Requirements:
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Python:
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- 3.5
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"""
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# Create universe of discourse in python using linspace ()
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import numpy as np
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X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
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# Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc).
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import skfuzzy as fuzz
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abc1 = [0, 25, 50]
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abc2 = [25, 50, 75]
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young = fuzz.membership.trimf(X, abc1)
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middle_aged = fuzz.membership.trimf(X, abc2)
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# Compute the different operations using inbuilt functions.
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one = np.ones(75)
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zero = np.zeros((75,))
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# 1. Union = max(µA(x), µB(x))
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union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
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# 2. Intersection = min(µA(x), µB(x))
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intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
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# 3. Complement (A) = (1- min(µA(x))
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complement_a = fuzz.fuzzy_not(young)
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# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
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difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
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# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
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alg_sum = young + middle_aged - (young * middle_aged)
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# 6. Algebraic Product = (µA(x) * µB(x))
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alg_product = young * middle_aged
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# 7. Bounded Sum = min[1,(µA(x), µB(x))]
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bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
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# 8. Bounded difference = min[0,(µA(x), µB(x))]
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bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
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if __name__ == "__main__":
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# Create universe of discourse in python using linspace ()
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X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
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# max-min composition
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# max-product composition
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# Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc).
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abc1 = [0, 25, 50]
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abc2 = [25, 50, 75]
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young = fuzz.membership.trimf(X, abc1)
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middle_aged = fuzz.membership.trimf(X, abc2)
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# Compute the different operations using inbuilt functions.
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one = np.ones(75)
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zero = np.zeros((75,))
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# 1. Union = max(µA(x), µB(x))
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union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
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# 2. Intersection = min(µA(x), µB(x))
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intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
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# 3. Complement (A) = (1- min(µA(x))
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complement_a = fuzz.fuzzy_not(young)
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# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
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difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
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# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
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alg_sum = young + middle_aged - (young * middle_aged)
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# 6. Algebraic Product = (µA(x) * µB(x))
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alg_product = young * middle_aged
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# 7. Bounded Sum = min[1,(µA(x), µB(x))]
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bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
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# 8. Bounded difference = min[0,(µA(x), µB(x))]
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bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
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# Plot each set A, set B and each operation result using plot() and subplot().
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import matplotlib.pyplot as plt
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# max-min composition
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# max-product composition
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plt.figure()
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# Plot each set A, set B and each operation result using plot() and subplot().
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import matplotlib.pyplot as plt
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plt.subplot(4, 3, 1)
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plt.plot(X, young)
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plt.title("Young")
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plt.grid(True)
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plt.figure()
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plt.subplot(4, 3, 2)
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plt.plot(X, middle_aged)
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plt.title("Middle aged")
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plt.grid(True)
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plt.subplot(4, 3, 1)
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plt.plot(X, young)
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plt.title("Young")
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plt.grid(True)
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plt.subplot(4, 3, 3)
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plt.plot(X, union)
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plt.title("union")
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plt.grid(True)
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plt.subplot(4, 3, 2)
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plt.plot(X, middle_aged)
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plt.title("Middle aged")
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plt.grid(True)
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plt.subplot(4, 3, 4)
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plt.plot(X, intersection)
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plt.title("intersection")
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plt.grid(True)
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plt.subplot(4, 3, 3)
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plt.plot(X, union)
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plt.title("union")
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plt.grid(True)
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plt.subplot(4, 3, 5)
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plt.plot(X, complement_a)
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plt.title("complement_a")
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plt.grid(True)
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plt.subplot(4, 3, 4)
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plt.plot(X, intersection)
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plt.title("intersection")
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plt.grid(True)
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plt.subplot(4, 3, 6)
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plt.plot(X, difference)
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plt.title("difference a/b")
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plt.grid(True)
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plt.subplot(4, 3, 5)
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plt.plot(X, complement_a)
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plt.title("complement_a")
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plt.grid(True)
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plt.subplot(4, 3, 7)
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plt.plot(X, alg_sum)
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plt.title("alg_sum")
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plt.grid(True)
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plt.subplot(4, 3, 6)
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plt.plot(X, difference)
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plt.title("difference a/b")
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plt.grid(True)
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plt.subplot(4, 3, 8)
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plt.plot(X, alg_product)
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plt.title("alg_product")
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plt.grid(True)
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plt.subplot(4, 3, 7)
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plt.plot(X, alg_sum)
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plt.title("alg_sum")
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plt.grid(True)
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plt.subplot(4, 3, 9)
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plt.plot(X, bdd_sum)
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plt.title("bdd_sum")
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plt.grid(True)
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plt.subplot(4, 3, 8)
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plt.plot(X, alg_product)
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plt.title("alg_product")
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plt.grid(True)
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plt.subplot(4, 3, 10)
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plt.plot(X, bdd_difference)
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plt.title("bdd_difference")
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plt.grid(True)
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plt.subplot(4, 3, 9)
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plt.plot(X, bdd_sum)
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plt.title("bdd_sum")
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plt.grid(True)
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plt.subplots_adjust(hspace=0.5)
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plt.show()
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plt.subplot(4, 3, 10)
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plt.plot(X, bdd_difference)
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plt.title("bdd_difference")
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plt.grid(True)
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plt.subplots_adjust(hspace=0.5)
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plt.show()
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@ -36,8 +36,9 @@ def viz_polymonial():
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return
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viz_polymonial()
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if __name__ == "__main__":
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viz_polymonial()
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# Predicting a new result with Polymonial Regression
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pol_reg.predict(poly_reg.fit_transform([[5.5]]))
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# output should be 132148.43750003
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# Predicting a new result with Polymonial Regression
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pol_reg.predict(poly_reg.fit_transform([[5.5]]))
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# output should be 132148.43750003
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@ -59,409 +59,139 @@ def plot(samples):
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return fig
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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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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))) * 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(size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0)))
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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(size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0)))
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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), scale=(1.0 / np.sqrt(hidden_input / 2.0))
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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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* 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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# 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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G_W3 = (
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np.random.normal(
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size=(hidden_input2, hidden_input3), 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), 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), 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), 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 Optimzier
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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
|
||||
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",
|
||||
D_W2 = (
|
||||
np.random.normal(
|
||||
size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0))
|
||||
)
|
||||
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])
|
||||
* 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
|
||||
|
|
@ -479,20 +209,298 @@ for iter in range(num_epoch):
|
|||
|
||||
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",
|
||||
# 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)
|
||||
)
|
||||
# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
|
||||
# -- end code --
|
||||
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 --
|
||||
|
|
|
|||
|
|
@ -5,16 +5,18 @@ from bs4 import BeautifulSoup
|
|||
from fake_useragent import UserAgent
|
||||
import requests
|
||||
|
||||
print("Googling.....")
|
||||
url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
|
||||
res = requests.get(url, headers={"UserAgent": UserAgent().random})
|
||||
# res.raise_for_status()
|
||||
with open("project1a.html", "wb") as out_file: # only for knowing the class
|
||||
for data in res.iter_content(10000):
|
||||
out_file.write(data)
|
||||
soup = BeautifulSoup(res.text, "html.parser")
|
||||
links = list(soup.select(".eZt8xd"))[:5]
|
||||
|
||||
print(len(links))
|
||||
for link in links:
|
||||
webbrowser.open(f"http://google.com{link.get('href')}")
|
||||
if __name__ == "__main__":
|
||||
print("Googling.....")
|
||||
url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
|
||||
res = requests.get(url, headers={"UserAgent": UserAgent().random})
|
||||
# res.raise_for_status()
|
||||
with open("project1a.html", "wb") as out_file: # only for knowing the class
|
||||
for data in res.iter_content(10000):
|
||||
out_file.write(data)
|
||||
soup = BeautifulSoup(res.text, "html.parser")
|
||||
links = list(soup.select(".eZt8xd"))[:5]
|
||||
|
||||
print(len(links))
|
||||
for link in links:
|
||||
webbrowser.open(f"http://google.com{link.get('href')}")
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user