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 # 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 --