gendis.compile(optimizer=gen_opt, loss='binary_crossentropy')
shape = dis.get_input_shape_at(0)[1:]
gen_input, real_input = Input(shape), Input(shape)
dis2batch = Model([gen_input, real_input],
[dis(gen_input), dis(real_input)])
dis.trainable = True
dis2batch.compile(optimizer=dis_opt,
loss='binary_crossentropy',
metrics=['binary_accuracy'])
gen_trainner = gendis
dis_trainner = dis2batch
imsave('{}/real.png'.format(vis_dir),
Song.grid_vis_songs(data_generator(25)))
vis_Z = code_generator(25)
for iteration in range(0, niter):
print 'iteration', iteration
Z = code_generator(nbatch)
gen_img = gen.predict(Z)
if iteration < args.boost or iteration % args.k == 0:
real_img = data_generator(nbatch)
gen_y = np.zeros((nbatch, 1))
real_y = np.ones((nbatch, 1))
d_loss = dis_trainner.train_on_batch([gen_img, real_img],
[gen_y, real_y])
print('\tDiscriminator:\t{}'.format(d_loss))
else:
y = np.ones((nbatch, 1))
shape = dis.get_input_shape_at(0)[1:]
gen_input, real_input = Input(shape), Input(shape)
dis2batch = Model([gen_input, real_input],
[dis(gen_input), dis(real_input)])
dis.trainable = True
dis2batch.compile(optimizer=dis_opt,
loss='binary_crossentropy',
metrics=['binary_accuracy'])
gen_trainner = gendis
dis_trainner = dis2batch
gh = 2
gw = 7
imsave('{}/real.png'.format(vis_dir),
Song.grid_vis_songs(data_generator(gh*gw)[:, :, :, 0], gh=gh, gw=gw))
vis_Z = code_generator(gh*gw)
fake_pool = gen.predict(vis_Z)
for iteration in range(0, niter):
print 'iteration', iteration
Z = code_generator(nbatch)
gen_img = gen.predict(Z)
fake_pool = np.vstack([fake_pool[:nbatch], gen_img])
shuffle_indices = np.random.permutation(len(fake_pool))
fake_pool = fake_pool[shuffle_indices]
gen_img = fake_pool[:nbatch]
real_img = data_generator(nbatch)
gen_y = np.zeros((nbatch, 1))
real_y = np.ones((nbatch, 1))