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Create GAN.py (#1445)
* Create GAN.py * gan update * Delete train-labels-idx1-ubyte.gz * Update GAN.py * Update GAN.py * Delete GAN.py * Create gan.py * Update gan.py * input_data import file
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Devil Lord
Christian Clauss
parent
f8a30b42ce
commit
4f86f58482
391
neural_network/gan.py
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391
neural_network/gan.py
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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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# 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 = np.random.normal(size=(784,hidden_input),scale=(1. / np.sqrt(784 / 2.))) *0.002
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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 = np.random.normal(size=(hidden_input,1),scale=(1. / np.sqrt(hidden_input / 2.))) *0.002
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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 = np.random.normal(size=(G_input,hidden_input),scale=(1. / np.sqrt(G_input / 2.))) *0.002
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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 = np.random.normal(size=(hidden_input,hidden_input2),scale=(1. / np.sqrt(hidden_input / 2.))) *0.002
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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 = np.random.normal(size=(hidden_input2,hidden_input3),scale=(1. / np.sqrt(hidden_input2 / 2.))) *0.002
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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 = np.random.normal(size=(hidden_input3,hidden_input4),scale=(1. / np.sqrt(hidden_input3 / 2.))) *0.002
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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 = np.random.normal(size=(hidden_input4,hidden_input5),scale=(1. / np.sqrt(hidden_input4 / 2.))) *0.002
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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 = np.random.normal(size=(hidden_input5,hidden_input6),scale=(1. / np.sqrt(hidden_input5 / 2.))) *0.002
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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 = np.random.normal(size=(hidden_input6,784),scale=(1. / np.sqrt(hidden_input6 / 2.))) *0.002
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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., 1., 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)) * (m1/(1-beta_1))
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D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 /(1-beta_2) ) + eps)) * (m2/(1-beta_1))
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D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 /(1-beta_2) ) + eps)) * (m3/(1-beta_1))
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D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 /(1-beta_2) ) + eps)) * (m4/(1-beta_1))
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# Func: Forward Feed for G
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Z = np.random.uniform(-1., 1., 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(D_W1.T)
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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
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v13 = beta_2 * v13 + (1 - beta_2) * grad_G_w5 ** 2
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m14 = beta_1 * m14 + (1 - beta_1) * grad_G_b5
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v14 = beta_2 * v14 + (1 - beta_2) * grad_G_b5 ** 2
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m15 = beta_1 * m15 + (1 - beta_1) * grad_G_w6
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v15 = beta_2 * v15 + (1 - beta_2) * grad_G_w6 ** 2
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m16 = beta_1 * m16 + (1 - beta_1) * grad_G_b6
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v16 = beta_2 * v16 + (1 - beta_2) * grad_G_b6 ** 2
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m17 = beta_1 * m17 + (1 - beta_1) * grad_G_w7
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v17 = beta_2 * v17 + (1 - beta_2) * grad_G_w7 ** 2
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m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7
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v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2
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|
||||
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., 1., 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 --
|
||||
332
neural_network/input_data.py
Normal file
332
neural_network/input_data.py
Normal file
|
|
@ -0,0 +1,332 @@
|
|||
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ==============================================================================
|
||||
"""Functions for downloading and reading MNIST data (deprecated).
|
||||
|
||||
This module and all its submodules are deprecated.
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import collections
|
||||
import gzip
|
||||
import os
|
||||
|
||||
import numpy
|
||||
from six.moves import urllib
|
||||
from six.moves import xrange # pylint: disable=redefined-builtin
|
||||
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import random_seed
|
||||
from tensorflow.python.platform import gfile
|
||||
from tensorflow.python.util.deprecation import deprecated
|
||||
|
||||
_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])
|
||||
|
||||
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
|
||||
DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
|
||||
|
||||
|
||||
def _read32(bytestream):
|
||||
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
|
||||
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
|
||||
|
||||
|
||||
@deprecated(None, 'Please use tf.data to implement this functionality.')
|
||||
def _extract_images(f):
|
||||
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
|
||||
|
||||
Args:
|
||||
f: A file object that can be passed into a gzip reader.
|
||||
|
||||
Returns:
|
||||
data: A 4D uint8 numpy array [index, y, x, depth].
|
||||
|
||||
Raises:
|
||||
ValueError: If the bytestream does not start with 2051.
|
||||
|
||||
"""
|
||||
print('Extracting', f.name)
|
||||
with gzip.GzipFile(fileobj=f) as bytestream:
|
||||
magic = _read32(bytestream)
|
||||
if magic != 2051:
|
||||
raise ValueError('Invalid magic number %d in MNIST image file: %s' %
|
||||
(magic, f.name))
|
||||
num_images = _read32(bytestream)
|
||||
rows = _read32(bytestream)
|
||||
cols = _read32(bytestream)
|
||||
buf = bytestream.read(rows * cols * num_images)
|
||||
data = numpy.frombuffer(buf, dtype=numpy.uint8)
|
||||
data = data.reshape(num_images, rows, cols, 1)
|
||||
return data
|
||||
|
||||
|
||||
@deprecated(None, 'Please use tf.one_hot on tensors.')
|
||||
def _dense_to_one_hot(labels_dense, num_classes):
|
||||
"""Convert class labels from scalars to one-hot vectors."""
|
||||
num_labels = labels_dense.shape[0]
|
||||
index_offset = numpy.arange(num_labels) * num_classes
|
||||
labels_one_hot = numpy.zeros((num_labels, num_classes))
|
||||
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
|
||||
return labels_one_hot
|
||||
|
||||
|
||||
@deprecated(None, 'Please use tf.data to implement this functionality.')
|
||||
def _extract_labels(f, one_hot=False, num_classes=10):
|
||||
"""Extract the labels into a 1D uint8 numpy array [index].
|
||||
|
||||
Args:
|
||||
f: A file object that can be passed into a gzip reader.
|
||||
one_hot: Does one hot encoding for the result.
|
||||
num_classes: Number of classes for the one hot encoding.
|
||||
|
||||
Returns:
|
||||
labels: a 1D uint8 numpy array.
|
||||
|
||||
Raises:
|
||||
ValueError: If the bystream doesn't start with 2049.
|
||||
"""
|
||||
print('Extracting', f.name)
|
||||
with gzip.GzipFile(fileobj=f) as bytestream:
|
||||
magic = _read32(bytestream)
|
||||
if magic != 2049:
|
||||
raise ValueError('Invalid magic number %d in MNIST label file: %s' %
|
||||
(magic, f.name))
|
||||
num_items = _read32(bytestream)
|
||||
buf = bytestream.read(num_items)
|
||||
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
|
||||
if one_hot:
|
||||
return _dense_to_one_hot(labels, num_classes)
|
||||
return labels
|
||||
|
||||
|
||||
class _DataSet(object):
|
||||
"""Container class for a _DataSet (deprecated).
|
||||
|
||||
THIS CLASS IS DEPRECATED.
|
||||
"""
|
||||
|
||||
@deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py'
|
||||
' from tensorflow/models.')
|
||||
def __init__(self,
|
||||
images,
|
||||
labels,
|
||||
fake_data=False,
|
||||
one_hot=False,
|
||||
dtype=dtypes.float32,
|
||||
reshape=True,
|
||||
seed=None):
|
||||
"""Construct a _DataSet.
|
||||
|
||||
one_hot arg is used only if fake_data is true. `dtype` can be either
|
||||
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
|
||||
`[0, 1]`. Seed arg provides for convenient deterministic testing.
|
||||
|
||||
Args:
|
||||
images: The images
|
||||
labels: The labels
|
||||
fake_data: Ignore inages and labels, use fake data.
|
||||
one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
|
||||
False).
|
||||
dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
|
||||
range [0,255]. float32 output has range [0,1].
|
||||
reshape: Bool. If True returned images are returned flattened to vectors.
|
||||
seed: The random seed to use.
|
||||
"""
|
||||
seed1, seed2 = random_seed.get_seed(seed)
|
||||
# If op level seed is not set, use whatever graph level seed is returned
|
||||
numpy.random.seed(seed1 if seed is None else seed2)
|
||||
dtype = dtypes.as_dtype(dtype).base_dtype
|
||||
if dtype not in (dtypes.uint8, dtypes.float32):
|
||||
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
|
||||
dtype)
|
||||
if fake_data:
|
||||
self._num_examples = 10000
|
||||
self.one_hot = one_hot
|
||||
else:
|
||||
assert images.shape[0] == labels.shape[0], (
|
||||
'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
|
||||
self._num_examples = images.shape[0]
|
||||
|
||||
# Convert shape from [num examples, rows, columns, depth]
|
||||
# to [num examples, rows*columns] (assuming depth == 1)
|
||||
if reshape:
|
||||
assert images.shape[3] == 1
|
||||
images = images.reshape(images.shape[0],
|
||||
images.shape[1] * images.shape[2])
|
||||
if dtype == dtypes.float32:
|
||||
# Convert from [0, 255] -> [0.0, 1.0].
|
||||
images = images.astype(numpy.float32)
|
||||
images = numpy.multiply(images, 1.0 / 255.0)
|
||||
self._images = images
|
||||
self._labels = labels
|
||||
self._epochs_completed = 0
|
||||
self._index_in_epoch = 0
|
||||
|
||||
@property
|
||||
def images(self):
|
||||
return self._images
|
||||
|
||||
@property
|
||||
def labels(self):
|
||||
return self._labels
|
||||
|
||||
@property
|
||||
def num_examples(self):
|
||||
return self._num_examples
|
||||
|
||||
@property
|
||||
def epochs_completed(self):
|
||||
return self._epochs_completed
|
||||
|
||||
def next_batch(self, batch_size, fake_data=False, shuffle=True):
|
||||
"""Return the next `batch_size` examples from this data set."""
|
||||
if fake_data:
|
||||
fake_image = [1] * 784
|
||||
if self.one_hot:
|
||||
fake_label = [1] + [0] * 9
|
||||
else:
|
||||
fake_label = 0
|
||||
return [fake_image for _ in xrange(batch_size)
|
||||
], [fake_label for _ in xrange(batch_size)]
|
||||
start = self._index_in_epoch
|
||||
# Shuffle for the first epoch
|
||||
if self._epochs_completed == 0 and start == 0 and shuffle:
|
||||
perm0 = numpy.arange(self._num_examples)
|
||||
numpy.random.shuffle(perm0)
|
||||
self._images = self.images[perm0]
|
||||
self._labels = self.labels[perm0]
|
||||
# Go to the next epoch
|
||||
if start + batch_size > self._num_examples:
|
||||
# Finished epoch
|
||||
self._epochs_completed += 1
|
||||
# Get the rest examples in this epoch
|
||||
rest_num_examples = self._num_examples - start
|
||||
images_rest_part = self._images[start:self._num_examples]
|
||||
labels_rest_part = self._labels[start:self._num_examples]
|
||||
# Shuffle the data
|
||||
if shuffle:
|
||||
perm = numpy.arange(self._num_examples)
|
||||
numpy.random.shuffle(perm)
|
||||
self._images = self.images[perm]
|
||||
self._labels = self.labels[perm]
|
||||
# Start next epoch
|
||||
start = 0
|
||||
self._index_in_epoch = batch_size - rest_num_examples
|
||||
end = self._index_in_epoch
|
||||
images_new_part = self._images[start:end]
|
||||
labels_new_part = self._labels[start:end]
|
||||
return numpy.concatenate((images_rest_part, images_new_part),
|
||||
axis=0), numpy.concatenate(
|
||||
(labels_rest_part, labels_new_part), axis=0)
|
||||
else:
|
||||
self._index_in_epoch += batch_size
|
||||
end = self._index_in_epoch
|
||||
return self._images[start:end], self._labels[start:end]
|
||||
|
||||
|
||||
@deprecated(None, 'Please write your own downloading logic.')
|
||||
def _maybe_download(filename, work_directory, source_url):
|
||||
"""Download the data from source url, unless it's already here.
|
||||
|
||||
Args:
|
||||
filename: string, name of the file in the directory.
|
||||
work_directory: string, path to working directory.
|
||||
source_url: url to download from if file doesn't exist.
|
||||
|
||||
Returns:
|
||||
Path to resulting file.
|
||||
"""
|
||||
if not gfile.Exists(work_directory):
|
||||
gfile.MakeDirs(work_directory)
|
||||
filepath = os.path.join(work_directory, filename)
|
||||
if not gfile.Exists(filepath):
|
||||
urllib.request.urlretrieve(source_url, filepath)
|
||||
with gfile.GFile(filepath) as f:
|
||||
size = f.size()
|
||||
print('Successfully downloaded', filename, size, 'bytes.')
|
||||
return filepath
|
||||
|
||||
|
||||
@deprecated(None, 'Please use alternatives such as:'
|
||||
' tensorflow_datasets.load(\'mnist\')')
|
||||
def read_data_sets(train_dir,
|
||||
fake_data=False,
|
||||
one_hot=False,
|
||||
dtype=dtypes.float32,
|
||||
reshape=True,
|
||||
validation_size=5000,
|
||||
seed=None,
|
||||
source_url=DEFAULT_SOURCE_URL):
|
||||
if fake_data:
|
||||
|
||||
def fake():
|
||||
return _DataSet([], [],
|
||||
fake_data=True,
|
||||
one_hot=one_hot,
|
||||
dtype=dtype,
|
||||
seed=seed)
|
||||
|
||||
train = fake()
|
||||
validation = fake()
|
||||
test = fake()
|
||||
return _Datasets(train=train, validation=validation, test=test)
|
||||
|
||||
if not source_url: # empty string check
|
||||
source_url = DEFAULT_SOURCE_URL
|
||||
|
||||
train_images_file = 'train-images-idx3-ubyte.gz'
|
||||
train_labels_file = 'train-labels-idx1-ubyte.gz'
|
||||
test_images_file = 't10k-images-idx3-ubyte.gz'
|
||||
test_labels_file = 't10k-labels-idx1-ubyte.gz'
|
||||
|
||||
local_file = _maybe_download(train_images_file, train_dir,
|
||||
source_url + train_images_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
train_images = _extract_images(f)
|
||||
|
||||
local_file = _maybe_download(train_labels_file, train_dir,
|
||||
source_url + train_labels_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
train_labels = _extract_labels(f, one_hot=one_hot)
|
||||
|
||||
local_file = _maybe_download(test_images_file, train_dir,
|
||||
source_url + test_images_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
test_images = _extract_images(f)
|
||||
|
||||
local_file = _maybe_download(test_labels_file, train_dir,
|
||||
source_url + test_labels_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
test_labels = _extract_labels(f, one_hot=one_hot)
|
||||
|
||||
if not 0 <= validation_size <= len(train_images):
|
||||
raise ValueError(
|
||||
'Validation size should be between 0 and {}. Received: {}.'.format(
|
||||
len(train_images), validation_size))
|
||||
|
||||
validation_images = train_images[:validation_size]
|
||||
validation_labels = train_labels[:validation_size]
|
||||
train_images = train_images[validation_size:]
|
||||
train_labels = train_labels[validation_size:]
|
||||
|
||||
options = dict(dtype=dtype, reshape=reshape, seed=seed)
|
||||
|
||||
train = _DataSet(train_images, train_labels, **options)
|
||||
validation = _DataSet(validation_images, validation_labels, **options)
|
||||
test = _DataSet(test_images, test_labels, **options)
|
||||
|
||||
return _Datasets(train=train, validation=validation, test=test)
|
||||
Loading…
Reference in New Issue
Block a user