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| import theano | |
| import theano.tensor as T | |
| from helper import activations | |
| from helper import misc,updates | |
| from scipy.stats import gaussian_kde | |
| from matplotlib import pyplot as plt | |
| from matplotlib.pyplot import * | |
| from helper import inits | |
| from helper.theano_utils import floatX, sharedX | |
| #defining parameters | |
| sz=2048 | |
| nh=2048 | |
| leaky_rectify = activations.leaky_rectify() | |
| rectify = activations.Rectify() | |
| tanh = activations.Tanh() | |
| sigmoid = activations.Sigmoid() | |
| bce = T.nnet.binary_crossentropy | |
| batch_size = 128 | |
| init_fn = misc.Normal(scale=0.02) | |
| #returns the probability of X to be choosed if gaussian distribution followed | |
| def gaussian_probability(X, u=0., s=1.): | |
| return (1./(s*np.sqrt(2*np.pi)))*np.exp(-(((X - u)**2)/(2*s**2))) | |
| def scale_and_shift(X, g, b): | |
| return X*g + b | |
| #defining Generator(G) network as multilayer perceptron | |
| def G(X, w1, g1, b1, w2, g2, b2, w3): | |
| h1 = leaky_rectify(scale_and_shift(T.dot(X,w1), g1, b1)) | |
| h2 = leaky_rectify(scale_and_shift(T.dot(h1,w2), g2, b2)) | |
| y = T.dot(h2, w3) | |
| return y | |
| #defining Discriminator(D) network as multilayer perceptron | |
| def D(X, w1, g1, b1, w2, g2, b2, w3): | |
| h1 = leaky_rectify(scale_and_shift(T.dot(X,w1), g1, b1)) | |
| h2 = tanh(scale_and_shift(T.dot(h1,w2), g2, b2)) | |
| y = sigmoid(T.dot(h2,w3)) | |
| return y | |
| #initialise parameters for G and D | |
| g_w1 = init_fn((1, nh)) | |
| g_g1 = inits.Normal(1., 0.02)(nh) | |
| g_b1 = inits.Normal(0., 0.02)(nh) | |
| g_w2 = init_fn((nh, nh)) | |
| g_g2 = inits.Normal(1., 0.02)(nh) | |
| g_b2 = inits.Normal(0., 0.02)(nh) | |
| g_w3 = init_fn((nh, 1)) | |
| d_w1 = init_fn((1, nh)) | |
| d_g1 = inits.Normal(1., 0.02)(nh) | |
| d_b1 = inits.Normal(0., 0.02)(nh) | |
| d_w2 = init_fn((nh, nh)) | |
| d_g2 = inits.Normal(1., 0.02)(nh) | |
| d_b2 = inits.Normal(0., 0.02)(nh) | |
| d_w3 = init_fn((nh, 1)) | |
| #defining input | |
| Z = T.matrix() | |
| X = T.matrix() | |
| #building generator, "gen" stores the output of generator layer | |
| gen = G(Z, g_w1, g_g1, g_b1, g_w2, g_g2, g_b2, g_w3 ) | |
| #getting the probability for real ang generated data | |
| prob_real = D(X, d_w1, d_g1, d_b1, d_w2, d_g2, d_b2, d_w3) | |
| prob_gen = D(gen, d_w1, d_g1, d_b1, d_w2, d_g2, d_b2, d_w3) | |
| #cost calculation for G and D | |
| g_cost = T.nnet.binary_crossentropy(prob_gen, T.ones(prob_gen.shape)).mean() | |
| d_real_cost = T.nnet.binary_crossentropy(prob_real, T.ones(prob_gen.shape)).mean() | |
| d_gen_cost = T.nnet.binary_crossentropy(prob_gen, T.zeros(prob_gen.shape)).mean() | |
| d_cost = d_real_cost + d_gen_cost | |
| #all costs summarized in one list | |
| cost = [g_cost, d_cost, d_real_cost, d_gen_cost] | |
| #using Adam optimizer | |
| learning_rate= 0.001 | |
| g_updater = updates.Adam(lr=sharedX(learning_rate) ) | |
| d_updater = updates.Adam(lr=sharedX(learning_rate) ) | |
| g_update = g_updater([g_w1, g_g1, g_b1, g_w2, g_g2, g_b2, g_w3 ], g_cost) | |
| d_update = d_updater([d_w1, d_g1, d_b1, d_w2, d_g2, d_b2, d_w3 ], d_cost) | |
| #interconversion between variable and function | |
| train_g = theano.function([X, Z], cost, updates=g_update) | |
| train_d = theano.function([X, Z], cost, updates=d_update) | |
| _gen = theano.function([Z], gen) | |
| _score = theano.function([X], prob_real) | |
| # visualising G and D | |
| def visualise(i): | |
| fig = plt.figure() | |
| #generatiing distribution | |
| x = np.linspace(-5, 5, 500).astype('float32') | |
| z = np.linspace(-1, 1, 500).astype('float32') | |
| y_true = gaussian_probability(x) | |
| kde = gaussian_kde(_gen(z.reshape(-1,1)).flatten()) | |
| y_gen = kde(x) | |
| preal = _score(x.reshape(-1, 1)).flatten() | |
| #plotting distribution | |
| plt.clf() | |
| plt.plot(x, y_true, '--', lw=2) | |
| plt.plot(x, y_gen, lw=2) | |
| plt.plot(x, preal, lw=2) | |
| plt.xlim([-5.,5.]) | |
| plt.ylim([0.,1.]) | |
| plt.ylabel('Probability--> ') | |
| plt.xlabel('X --> ') | |
| plt.legend(['Training Data', 'Generated Data', 'Discriminator']) | |
| plt.title('GAN learn Gaussian distibution | Generation: '+str(i)) | |
| #fig.canvas.draw() | |
| #plt.show() | |
| #show() | |
| plt.savefig("fig"+str(i)+".png") | |
| #Training G and N for 50 generations | |
| for i in range(100): | |
| x = np.random.normal(1, 1, size=(batch_size, 1)).astype('float32') | |
| y = np.random.uniform(-1, 1, size=(batch_size, 1)).astype('float32') | |
| if i%5==0: | |
| train_g(x, y) | |
| print "Generation = ",str(i) | |
| visualise(i) | |
| else: | |
| train_d(x, y) |
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