def make_special_conv4_l1(latent_dim, filter_length=3, num_filters=256):
x_shape = (128, 54)
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDatasource(huzz), x_shape[1],
x_shape[0])
decoder_input = tf.placeholder(tf.float32,
shape=(1, latent_dim),
name='decoder_input')
encoder_input = tf.placeholder(tf.float32,
shape=(1, *x_shape),
name='encoder_input')
with conv_arg_scope2():
encoder_output, _, dense_layer_size = build_special_conv4_encoder(
encoder_input,
latent_dim,
num_filters,
filter_length=filter_length)
decoder_output = build_special_conv4_decoder(
decoder_input,
x_shape,
num_filters,
filter_length=filter_length,
dense_layer_size=dense_layer_size)
decoder_output = tf.nn.softmax(decoder_output, dim=-1)
decoder_output = tf.squeeze(decoder_output, 0)
return data_pipeline, encoder_input, encoder_output, decoder_input, decoder_output
def test_one_hot():
expected_shape = (256, 54)
one_hotter = OneHotVecotorizer(TokenDatasource(HuzzerSource()), 54, 256)
# check deteminism and dimensions
for i in range(20, 60):
x = one_hotter['{0}'.format(i)]
y = one_hotter['{0}'.format(i)]
assert np.array_equal(
x, y), 'Accessing same element produces different outcome.'
assert x.shape == expected_shape, 'Incorrect shape for output.'
def make_conv1():
latent_dim = 16
x_shape = (128, 54)
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDatasource(huzz), x_shape[1],
x_shape[0])
decoder_input = tf.placeholder(tf.float32,
shape=(1, latent_dim),
name='decoder_input')
with conv_arg_scope():
encoder_input = tf.placeholder(tf.float32,
shape=(1, *x_shape),
name='encoder_input')
encoder_output, _ = build_conv1_encoder(encoder_input, latent_dim)
decoder_output = build_conv1_decoder(decoder_input, x_shape)
decoder_output = tf.reshape(decoder_output, x_shape)
return data_pipeline, encoder_input, encoder_output, decoder_input, decoder_output
def make_simple(latent_dim, sequence_length):
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDatasource(huzz), 54, 256)
x_shape = (sequence_length, 54)
encoder_input = tf.placeholder(tf.float32,
shape=(1, *x_shape),
name='encoder_input')
x_flat = slim.flatten(encoder_input)
z = slim.fully_connected(x_flat,
latent_dim,
scope='encoder_output',
activation_fn=tf.tanh)
encoder_output = tf.identity(z, 'this_is_output')
decoder_input = tf.placeholder(tf.float32,
shape=(1, latent_dim),
name='decoder_input')
decoder_output = build_decoder(decoder_input, x_shape)
return data_pipeline, encoder_input, encoder_output, decoder_input, decoder_output
def make_special_conv():
latent_dim = 64
x_shape = (128, 54)
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDatasource(huzz), x_shape[1],
x_shape[0])
decoder_input = tf.placeholder(tf.float32,
shape=(1, latent_dim),
name='decoder_input')
encoder_input = tf.placeholder(tf.float32,
shape=(1, *x_shape),
name='encoder_input')
with conv_arg_scope():
encoder_output, _ = build_special_conv_encoder(encoder_input,
latent_dim)
decoder_output = build_special_conv_decoder(decoder_input, x_shape)
decoder_output = tf.nn.softmax(decoder_output, dim=-1)
decoder_output = tf.squeeze(decoder_output, 0)
return data_pipeline, encoder_input, encoder_output, decoder_input, decoder_output
def make_simple_sss(latent_dim=32):
x_shape = (128, 54)
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDatasource(huzz), x_shape[1],
x_shape[0])
encoder_input = tf.placeholder(tf.float32,
shape=(1, *x_shape),
name='encoder_input')
x_flat = slim.flatten(encoder_input)
z = slim.fully_connected(x_flat,
latent_dim,
scope='encoder_output',
activation_fn=tf.tanh)
decoder_input = tf.placeholder(tf.float32,
shape=(1, latent_dim),
name='decoder_input')
decoder_output = build_decoder(decoder_input,
x_shape,
activation=tf.nn.relu6)
decoder_output = tf.reshape(decoder_output, x_shape)
return data_pipeline, encoder_input, z, decoder_input, decoder_output
def autoencode():
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDataSource(huzz), 54, 256)
x_shape = (256, 54)
latent_dim = 16
x = tf.placeholder(tf.float32, shape=(1, *x_shape), name='encoder_input')
x_flat = slim.flatten(x)
z = slim.fully_connected(x_flat,
latent_dim,
scope='encoder_output',
activation_fn=tf.tanh)
z = tf.identity(z, 'this_is_output')
z = tf.placeholder(tf.float32, shape=(1, 16), name='decoder_input')
build_decoder(z, (256, 54))
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(
sess,
tf.train.latest_checkpoint('experiments/VAE_baseline/simple'))
def a(example_data=None):
if example_data is None:
key = str(randint(0, 100000000))
code = huzz[key]
print('INPUT CODE FOR NETWORK:')
print(code)
example_data = data_pipeline[key]
example_data = np.reshape(example_data, (1, *example_data.shape))
return sess.run(['this_is_output:0'],
feed_dict={NAMES['encoder_input']: example_data})
def g(latent_rep):
return sess.run(NAMES['decoder_output'],
feed_dict={NAMES['decoder_input']: latent_rep[0]})
for i in trange(5):
key = str(randint(0, 100000000))
code = huzz[key]
with open(BASEDIR + 'simple_examples/auto_{}_input.hs'.format(i),
'w') as f:
f.write(code)
example_data = data_pipeline[key]
imsave(BASEDIR + 'simple_examples/auto_{}_input.png'.format(i),
example_data.astype(np.float32).T)
latent_reps = a(example_data)
recon = g(latent_reps)[0]
imsave(BASEDIR + 'simple_examples/auto_{}_output.png'.format(i),
recon.T)
tokens = np.argmax(recon, axis=-1)
def token_to_string(t):
if t == 0:
return ''
return TOKEN_MAP[t - 1]
text = ' '.join([token_to_string(t) for t in tokens])
with open(BASEDIR + 'simple_examples/auto_{}_output.hs'.format(i),
'w') as f:
f.write(text)