def conv(a: TfLambdaArrow, args: TensorVarList, state) -> Sequence[Tensor]:
with tf.name_scope(a.name):
if 'seen_tf' in state and a.name in state['seen_tf']:
return a.func(args, reuse=True)
else:
state['seen_tf'] = set([a.name])
return a.func(args, reuse=False)
def gan_shopping_arrow_pi(nitems: int, options) -> CompositeArrow:
"""Gan on shopping basket"""
n_fake_samples = options['n_fake_samples']
n_samples = n_fake_samples + 1
batch_size = options['batch_size']
# nitems = options['nitems']
fwd = SumNArrow(nitems)
inv = invert(fwd)
info = propagate(inv)
def gen_func(args, reuse=False):
# import pdb; pdb.set_trace()
"""Generator function"""
with tf.variable_scope("generator", reuse=reuse):
inp = tf.concat(args, axis=1)
# inp = fully_connected(inp, 10, activation='elu')
inp = fully_connected(inp, inv.num_param_ports(), activation='elu')
inps = tf.split(inp,
axis=1,
num_or_size_splits=inv.num_param_ports())
# inps = [tf.Print(inp, [inp[0]], message="Generated!", summarize=100) for inp in inps]
return inps
def disc_func(args, reuse=False):
# import pdb; pdb.set_trace()
"""Discriminator function """
with tf.variable_scope("discriminator", reuse=reuse):
inp = tf.concat(args, axis=2)
# inp = tf.Print(inp, [inp[0]], message="inp to disc", summarize=100)
# inp = fully_connected(inp, 20, activation='elu')
inp = fully_connected(inp, 10, activation='elu')
inp = fully_connected(inp, n_samples, activation='sigmoid')
return [inp]
# Make a conditional generator from the inverse\
num_non_param_in_ports = inv.num_in_ports() - inv.num_param_ports()
g_theta = TfLambdaArrow(inv.num_in_ports() - inv.num_param_ports() + 1,
inv.num_param_ports(),
func=gen_func,
name="g_theta")
cond_gen = g_from_g_theta(inv, g_theta)
disc = TfLambdaArrow(nitems, 1, func=disc_func, name="disc")
gan_arr = set_gan_arrow(fwd,
cond_gen,
disc,
n_fake_samples,
2,
x_shapes=[(batch_size, 1) for i in range(nitems)],
z_shape=(batch_size, 1))
return gan_arr
def set_gan_nn_arrow(options):
"""Test Gan on fwd function f(x) = x"""
fwd_arr = IdentityArrow()
n_fake_samples = options['n_fake_samples']
n_samples = n_fake_samples + 1
batch_size = options['batch_size']
def gen_func(args):
"""Generator function"""
with tf.variable_scope("generator", reuse=False):
# inp = tf.concat(args, axis=1)
inp = args[0]
inp = fully_connected(inp, 10, activation='elu')
# inp = batch_normalization(inp)
# inp = fully_connected(inp, 10, activation='elu')
# inp = batch_normalization(inp)
inp = fully_connected(inp, 1, activation='sigmoid')
# inp = batch_normalization(inp)
return [inp]
# def gen_func(args):
# """Generator function"""
# with tf.variable_scope("generator", reuse=False):
# return [args[0]+0.2]
def disc_func(args):
"""Discriminator function"""
with tf.variable_scope("discriminator", reuse=False):
assert len(args) == 1
inp = args[0]
inp = fully_connected(inp, 5, activation='elu')
# inp = batch_normalization(inp)
inp = fully_connected(inp, 5, activation='elu')
# inp = batch_normalization(inp)
inp = args[0]
inp = fully_connected(inp, n_samples, activation='sigmoid')
return [inp]
cond_gen = TfLambdaArrow(2, 1, func=gen_func)
disc = TfLambdaArrow(1, 1, func=disc_func)
gan_arr = set_gan_arrow(fwd_arr,
cond_gen,
disc,
n_fake_samples,
2,
x_shape=(batch_size, 1),
z_shape=(batch_size, 1))
return gan_arr
def tensor_to_sup_right_inv(output_tensors: Sequence[Tensor], options):
"""Convert outputs from tensoflow graph into supervised loss arrow"""
fwd = graph_to_arrow(output_tensors, name="render")
inv = invert(fwd)
inv_arrow = inv_fwd_loss_arrow(fwd, inv)
info = propagate(inv_arrow)
def g_tf(args, reuse=False):
eps = 1e-3
"""Tensorflow map from image to pi parameters"""
shapes = [info[port]['shape'] for port in inv_arrow.param_ports()]
inp = args[0]
width, height = getn(options, 'width', 'height')
inp = tf.reshape(inp, (-1, width, height))
inp = tf.expand_dims(inp, axis=3)
from tflearn.layers import conv_2d, fully_connected
tf.summary.image("g_tf_output", inp)
# Do convolutional layers
nlayers = 2
for i in range(nlayers):
inp = conv_2d(inp, nb_filter=4, filter_size=1, activation="elu")
inp = conv_2d(inp,
nb_filter=options['nsteps'],
filter_size=1,
activation="sigmoid")
out = []
for i, shape in enumerate(shapes):
this_inp = inp[:, :, :, i:i + 1]
if shape[1] == width * height:
this_inp = tf.reshape(this_inp,
(options['batch_size'], -1)) + eps
out.append(this_inp)
else:
r_length = int(np.ceil(np.sqrt(shape[1])))
rs = tf.image.resize_images(this_inp, (r_length, r_length))
rs = tf.reshape(rs, (options['batch_size'], -1)) + eps
out.append(rs[:, 0:shape[1]])
return out
g_arrow = TfLambdaArrow(1,
inv_arrow.num_param_ports(),
func=g_tf,
name="img_to_theta")
right_inv = unparam(inv_arrow, nnet=g_arrow)
# 1. Attach voxel input to right_inverse to feed in x data
fwd2 = deepcopy(fwd)
data_right_inv = comp(fwd2, right_inv)
# sup_right_inv = supervised_loss_arrow(right_inv)
return data_right_inv
def gan_shopping_arrow_compare(nitems: int, options) -> CompositeArrow:
"""Comparison for Gan, g is straight neural network"""
n_fake_samples = options['n_fake_samples']
n_samples = n_fake_samples + 1
batch_size = options['batch_size']
# nitems = options['nitems']
fwd = SumNArrow(nitems)
def gen_func(args, reuse=False):
# import pdb; pdb.set_trace()
"""Generator function"""
with tf.variable_scope("generator", reuse=reuse):
inp = tf.concat(args, axis=1)
inp = fully_connected(inp, nitems, activation='elu')
inps = tf.split(inp, axis=1, num_or_size_splits=nitems)
return inps
def disc_func(args, reuse=False):
# import pdb; pdb.set_trace()
"""Discriminator function """
with tf.variable_scope("discriminator", reuse=reuse):
inp = tf.concat(args, axis=2)
inp = fully_connected(inp, 10, activation='elu')
inp = fully_connected(inp, n_samples, activation='sigmoid')
return [inp]
cond_gen = TfLambdaArrow(2, nitems, func=gen_func, name="cond_gen")
disc = TfLambdaArrow(nitems, 1, func=disc_func, name="disc")
gan_arr = set_gan_arrow(fwd,
cond_gen,
disc,
n_fake_samples,
2,
x_shapes=[(batch_size, 1) for i in range(nitems)],
z_shape=(batch_size, 1))
return gan_arr
def GanLossArrow(nsamples):
def func(args, eps=1e-6, reuse=False):
"""Do Gan Loss in TensorFlow
Args:
x : (batch_size, nsamples) discriminator output
perm: (namples)
Returns:
(nsamples) - Generator loss (per batch)
(nsamples) - Discriminator loss (per batch)
"""
with tf.name_scope("ganloss"):
assert len(args) == 2
xs = args[0]
perm = args[1]
# Only the tensor as position nsamples is authentic
is_auth = tf.equal(perm, nsamples - 1)
# xs = tf.Print(xs, [xs], message="xs", summarize=1000)
is_auth = tf.Print(is_auth, [is_auth, perm] +
[xs[0, i] for i in range(nsamples)])
xs = tf.split(xs, axis=1, num_or_size_splits=nsamples)
assert len(xs) == nsamples
losses_d = []
losses_g = []
for i in range(nsamples):
x = xs[i]
def lambda_log(v):
return lambda: tf.log(v)
# FIXME: Redundant computation
losses_d.append(
tf.cond(is_auth[i], lambda_log(x + eps),
lambda_log(1 - x + eps)))
losses_g.append(
tf.cond(is_auth[i], lambda: tf.zeros_like(x),
lambda_log(x + eps)))
loss_d = tf.concat(losses_d, axis=1)
loss_g = tf.concat(losses_g, axis=1)
loss_d = tf.Print(loss_d, [loss_d[0], loss_g[0]], message="losses")
sum_loss_d = tf.reduce_sum(loss_d, axis=1)
sum_loss_g = tf.reduce_sum(loss_g, axis=1)
return [sum_loss_d, sum_loss_g]
return TfLambdaArrow(2, 2, func=func, name="ganloss")
def right_inv_nnet(output_tensors: Sequence[Tensor], options):
"""Convert outputs from tensoflow graph into supervised loss arrow"""
fwd = graph_to_arrow(output_tensors, name="render")
info = propagate(fwd)
def g_tf(args, reuse=False):
eps = 1e-3
"""Tensorflow map from image to pi parameters"""
shapes = [info[port]['shape'] for port in fwd.in_ports()]
inp = args[0]
width, height = getn(options, 'width', 'height')
inp = tf.reshape(inp, (-1, width, height))
inp = tf.expand_dims(inp, axis=3)
from tflearn.layers import conv_2d, fully_connected
tf.summary.image("g_tf_output", inp)
# Do convolutional layers
nlayers = 2
for i in range(nlayers):
inp = conv_2d(inp, nb_filter=4, filter_size=1, activation="elu")
ratio = width / options['res']
inp = conv_2d(inp,
nb_filter=options['res'],
filter_size=1,
strides=int(ratio),
activation="sigmoid")
return [tf.reshape(inp, (options['batch_size'], -1))]
from arrows import CompositeArrow, Arrow
def wrap(a: Arrow):
"""Wrap an arrow in a composite arrow"""
c = CompositeArrow(name=a.name)
for port in a.ports():
c_port = c.add_port()
if is_in_port(port):
make_in_port(c_port)
c.add_edge(c_port, port)
if is_param_port(port):
make_param_port(c_port)
if is_out_port(port):
make_out_port(c_port)
c.add_edge(port, c_port)
if is_error_port(port):
make_error_port(c_port)
transfer_labels(port, c_port)
assert c.is_wired_correctly()
return c
right_inv = TfLambdaArrow(fwd.num_out_ports(),
fwd.num_in_ports(),
func=g_tf,
name="img_to_voxels")
right_inv = wrap(right_inv)
for i, in_port in enumerate(fwd.in_ports()):
set_port_shape(right_inv.out_port(i), get_port_shape(in_port, info))
for i, out_port in enumerate(fwd.out_ports()):
set_port_shape(right_inv.in_port(i), get_port_shape(out_port, info))
inv_arrow = inv_fwd_loss_arrow(fwd, right_inv)
# 1. Attach voxel input to right_inverse to feed in x data
fwd2 = deepcopy(fwd)
data_right_inv = comp(fwd2, inv_arrow)
return data_right_inv
def test_set_gan_nn_arrow(options):
fwd = IdentityArrow()
n_fake_samples = 1
n_samples = n_fake_samples + 1
def gen_func(args):
"""Generator function"""
with tf.variable_scope("generator", reuse=False):
inp = tf.concat(args, axis=1)
inp = fully_connected(inp, 1, activation='elu')
inp = batch_normalization(inp)
inp = fully_connected(inp, 1, activation='elu')
inp = batch_normalization(inp)
return [inp]
# def gen_func(args):
# """Generator function"""
# with tf.variable_scope("generator", reuse=False):
# return [args[0]]
def disc_func(args):
"""Discriminator function"""
with tf.variable_scope("discriminator", reuse=False):
assert len(args) == 1
inp = args[0]
l1 = fully_connected(inp, n_samples, activation='sigmoid')
return [l1]
cond_gen = TfLambdaArrow(2, 1, func=gen_func)
disc = TfLambdaArrow(1, 1, func=disc_func)
gan_arr = set_gan_arrow(fwd, cond_gen, disc, n_fake_samples, 2)
x = np.array([1.0])
z = np.array([0.5])
perm = np.array([1, 0])
batch_size = 64
set_port_shape(gan_arr.in_port(0), (batch_size, 1))
set_port_shape(gan_arr.in_port(1), (batch_size, 1))
set_port_shape(gan_arr.in_port(2), (n_samples, ))
set_port_dtype(gan_arr.in_port(2), 'int32')
with tf.name_scope(gan_arr.name):
input_tensors = gen_input_tensors(gan_arr, param_port_as_var=False)
output_tensors = arrow_to_graph(gan_arr, input_tensors)
x_ten, z_ten, perm_ten = input_tensors
d_loss, g_loss, fake_x_1 = output_tensors
d_loss = tf.reduce_mean(-d_loss)
g_loss = tf.reduce_mean(-g_loss)
# fetch = {'d_loss': d_loss, 'g_loss': g_loss, 'x_ten': x_ten, 'fake': fake_x_1}
fetch = {'d_loss': d_loss, 'g_loss': g_loss}
sess = tf.Session()
x = np.random.rand(16, 1)
z = np.random.rand(16, 1)
# output_data = sess.run(fetch,
# feed_dict={x_ten: x, z_ten: z, perm_ten: perm})
losses = {'d_loss': d_loss, 'g_loss': g_loss}
options = {'learning_rate': 0.01, 'update': 'adam'}
d_vars = get_variables('discriminator')
g_vars = get_variables('generator')
loss_updates = [
updates(d_loss, d_vars, options=options)[1],
updates(g_loss, g_vars, options=options)[1]
]
fetch['check'] = tf.add_check_numerics_ops()
loss_ratios = [1, 1]
def train_gen():
while True:
x = np.random.rand(batch_size, 1)
z = np.random.rand(batch_size, 1)
perm = np.arange(n_samples)
np.random.shuffle(perm)
yield {x_ten: x, z_ten: z, perm_ten: perm}
# Summaries
summaries = variable_summaries(losses)
writers = setup_file_writers('summaries', sess)
options['writers'] = writers
callbacks = [every_n(summary_writes, 25)]
sess.run(tf.initialize_all_variables())
train_loop(sess,
loss_updates,
fetch,
train_generators=[train_gen()],
test_generators=None,
num_iterations=100000,
loss_ratios=loss_ratios,
callbacks=callbacks,
callbacks=None)
def gan_renderer_arrow(options):
"""Gan on renderer"""
n_fake_samples = options['n_fake_samples']
n_samples = n_fake_samples + 1
batch_size = options['batch_size']
res = options['res']
width = options['width']
height = options['height']
nvoxels = res * res * res
npixels = width * height
from voxel_render import test_invert_render_graph
fwd, inv = test_invert_render_graph(options)
from arrows.apply.propagate import propagate
info = propagate(inv)
def gen_func(args):
"""Generator function"""
with tf.variable_scope("generator", reuse=False):
# inp = tf.concat(args, axis=1
shapes = [info[port]['shape'] for port in inv.param_ports()]
inp = args[0]
inp = fully_connected(inp, 2, activation='elu')
return [
fully_connected(inp, shape[1], activation='elu')
for shape in shapes
]
def disc_func(args):
"""Discriminator function"""
with tf.variable_scope("discriminator", reuse=False):
assert len(args) == 1
inp = args[0]
inp = fully_connected(inp, n_samples, activation='sigmoid')
return [inp]
# Make a conditional generator from the inverse\
g_theta = TfLambdaArrow(inv.num_in_ports() - inv.num_param_ports() + 1,
inv.num_param_ports(),
func=gen_func)
cond_gen = g_from_g_theta(inv, g_theta)
disc = TfLambdaArrow(1, 1, func=disc_func)
def train_gen():
"""Generator for x, z and permutation"""
from wacacore.util.generators import infinite_batches
from voxel_helpers import model_net_40
voxel_data = model_net_40()
x_gen = infinite_batches(voxel_data, batch_size=batch_size)
while True:
x = next(x_gen)
x = x.reshape(batch_size, -1)
z = np.random.rand(batch_size, 1)
perm = np.arange(n_samples)
np.random.shuffle(perm)
yield {x_ten: x, z_ten: z, perm_ten: perm}
gan_arr = set_gan_arrow(fwd,
cond_gen,
disc,
n_fake_samples,
2,
x_shape=(batch_size, nvoxels),
z_shape=(batch_size, 1))
return gan_arr