def run_generator(num, x1, x2, fig_name='sample.png'):
with tf.Session() as sess:
tf.sg_init(sess)
# restore parameters
tf.sg_restore(sess,
tf.train.latest_checkpoint('asset/train/infogan'),
category='generator')
# run generator
imgs = sess.run(gen, {
target_num: num,
target_cval_1: x1,
target_cval_2: x2
})
# plot result
_, ax = plt.subplots(10, 10, sharex=True, sharey=True)
for i in range(10):
for j in range(10):
ax[i][j].imshow(imgs[i * 10 + j], 'gray')
ax[i][j].set_axis_off()
plt.savefig('asset/train/infogan/' + fig_name, dpi=600)
tf.sg_info('Sample image saved to "asset/train/infogan/%s"' % fig_name)
plt.close()
def generate(self, prev_midi):
with tf.Session() as sess:
tf.sg_init(sess)
# saver = tf.train.Saver()
# saver.restore(sess, tf.train.latest_checkpoint('save/train/small'))
# KDK: choose self.next_token or self.preds
# out = sess.run(self.next_token, {self.x: prev_midi})
tf.sg_restore(sess, tf.train.latest_checkpoint('save/train/small'))
out = sess.run(self.next_token, {self.x: prev_midi})
return out
def genIt(name='bird'):
z = tf.random_normal((batch_size, rand_dim))
gen = generator(z)
with tf.Session() as sess:
sess.run(
tf.group(tf.global_variables_initializer(),
tf.sg_phase().assign(False)))
tf.sg_restore(sess,
tf.train.latest_checkpoint('asset/train/gan'),
category=['generator', 'discriminator'])
fake_features = []
for i in range(100):
fake_features.append(sess.run(gen))
np.save('../data/fake_' + name + '_negative.npy',
np.array(fake_features).reshape((-1, 4096)))
def testIt():
data = raw
positive = np.array(data.label_train) > 0
x = tf.placeholder(tf.float32, [None, 4096])
y = tf.placeholder(tf.float32)
disc_real = discriminator(x)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.cast(disc_real > 0.5, "float"), y), tf.float32))
np.set_printoptions(precision=3, suppress=True)
with tf.Session() as sess:
sess.run(
tf.group(tf.global_variables_initializer(),
tf.sg_phase().assign(False)))
# restore parameters
tf.sg_restore(sess,
tf.train.latest_checkpoint('asset/train/gan'),
category=['generator', 'discriminator'])
ans = sess.run(disc_real, feed_dict={x: np.array(data.test)})
print np.sum(ans > 0.5)
np.save('dm_bird.npy', ans)
z = tf.random_normal((batch_size, rand_dim))
# generator
gen = generator(z).sg_squeeze()
#
# draw samples
#
with tf.Session() as sess:
tf.sg_init(sess)
# restore parameters
tf.sg_restore(sess,
tf.train.latest_checkpoint('asset/train/gan'),
category='generator')
# run generator
imgs = sess.run(gen)
# plot result
_, ax = plt.subplots(10, 10, sharex=True, sharey=True)
for i in range(10):
for j in range(10):
ax[i][j].imshow(imgs[i * 10 + j], 'gray')
ax[i][j].set_axis_off()
plt.savefig('asset/train/gan/sample.png', dpi=600)
tf.sg_info('Sample image saved to "asset/train/gan/sample.png"')
plt.close()
gen = (z.sg_dense(dim=1024).sg_dense(dim=7 * 7 * 128).sg_reshape(
shape=(-1, 7, 7,
128)).sg_upconv(dim=64).sg_upconv(dim=1,
act='sigmoid').sg_squeeze())
#
# draw samples
#
with tf.Session() as sess:
tf.sg_init(sess)
# restore parameters
tf.sg_restore(sess,
tf.train.latest_checkpoint('asset/train/vae'),
category='decoder')
# run generator
imgs = sess.run(gen)
# plot result
_, ax = plt.subplots(10, 10, sharex=True, sharey=True)
for i in range(10):
for j in range(10):
ax[i][j].imshow(imgs[i * 10 + j], 'gray')
ax[i][j].set_axis_off()
plt.savefig('asset/train/vae/sample.png', dpi=600)
tf.sg_info('Sample image saved to "asset/train/vae/sample.png"')
plt.close()
