Tensorflow实例：实现GAN（生成对抗网）

"""Dependencies:tensorflow: 1.1.0matplotlibnumpy"""import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plttf.set_random_seed(1)np.random.seed(1)# Hyper ParametersBATCH_SIZE = 64LR_G = 0.0001           # learning rate for generatorLR_D = 0.0001           # learning rate for discriminatorN_IDEAS = 5             # think of this as number of ideas for generating an art work (Generator)ART_COMPONENTS = 15     # it could be total point G can draw in the canvasPAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])# show our beautiful painting rangeplt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')plt.legend(loc='upper right')plt.show()def artist_works():     # painting from the famous artist (real target)    a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]    paintings = a * np.power(PAINT_POINTS, 2) + (a-1)    return paintingswith tf.variable_scope('Generator'):    G_in = tf.placeholder(tf.float32, [None, N_IDEAS])          # random ideas (could from normal distribution)    G_l1 = tf.layers.dense(G_in, 128, tf.nn.relu)    G_out = tf.layers.dense(G_l1, ART_COMPONENTS)               # making a painting from these random ideaswith tf.variable_scope('Discriminator'):    real_art = tf.placeholder(tf.float32, [None, ART_COMPONENTS], name='real_in')   # receive art work from the famous artist    D_l0 = tf.layers.dense(real_art, 128, tf.nn.relu, name='l')    prob_artist0 = tf.layers.dense(D_l0, 1, tf.nn.sigmoid, name='out')              # probability that the art work is made by artist    # reuse layers for generator    D_l1 = tf.layers.dense(G_out, 128, tf.nn.relu, name='l', reuse=True)            # receive art work from a newbie like G    prob_artist1 = tf.layers.dense(D_l1, 1, tf.nn.sigmoid, name='out', reuse=True)  # probability that the art work is made by artistD_loss = -tf.reduce_mean(tf.log(prob_artist0) + tf.log(1-prob_artist1))G_loss = tf.reduce_mean(tf.log(1-prob_artist1))train_D = tf.train.AdamOptimizer(LR_D).minimize(    D_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator'))train_G = tf.train.AdamOptimizer(LR_G).minimize(    G_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator'))sess = tf.Session()sess.run(tf.global_variables_initializer())plt.ion()   # something about continuous plottingfor step in range(5000):    artist_paintings = artist_works()           # real painting from artist    G_ideas = np.random.randn(BATCH_SIZE, N_IDEAS)    G_paintings, pa0, Dl = sess.run([G_out, prob_artist0, D_loss, train_D, train_G],    # train and get results                                    {G_in: G_ideas, real_art: artist_paintings})[:3]    if step % 50 == 0:  # plotting        plt.cla()        plt.plot(PAINT_POINTS[0], G_paintings[0], c='#4AD631', lw=3, label='Generated painting',)        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % pa0.mean(), fontdict={'size': 15})        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -Dl, fontdict={'size': 15})        plt.ylim((0, 3)); plt.legend(loc='upper right', fontsize=12); plt.draw(); plt.pause(0.01)plt.ioff()plt.show()

运行结果：