def apply_constraints():
logger.debug("Using constraints: {}".format(
str(experiment.constraints)))
with tf.variable_scope("constrained_out"):
d_constraints_kernel = tf.get_variable(
"d_constraints_kernel",
shape=[constraints_features.shape[1], 1],
initializer=xavier_init())
logger.debug(msg.format(
"d_constraints_kernel", d_constraints_kernel.shape, reuse))
input_concat = tf.concat(
[d_hidden4, constraints_features],
axis=1, name="input_concat_{}".format(reuse))
logger.debug(msg.format(
"input_concat", input_concat.shape, reuse))
weight_concat = tf.concat(
[d_out_kernel, d_constraints_kernel],
axis=0, name="weight_concat_{}".format(reuse))
logger.debug(msg.format(
"weight_concat", weight_concat.shape, reuse))
d_constrained_out = tf.add(
tf.matmul(input_concat, weight_concat), d_out_bias,
name="d_constrained_out_{}".format(reuse))
logger.debug(msg.format(
"constrained_out", d_constrained_out.shape, reuse))
# define summaries on the last layer
d_summaries = tf.summary.merge(
[stats_summaries(d_constrained_out),
d_summaries_dh4])
return d_constrained_out, d_summaries
def apply_constraints():
logger.debug("Using constraints: {}".format(
str(experiment.constraints)))
with tf.variable_scope("constrained_ll_out"):
constraints_out = dense(
constraints_features, 1, activation=lrelu,
kernel_initializer=xavier_init())
d_out_kernel = tf.get_variable(
"d_out_kernel", shape=[2, 1], initializer=xavier_init())
logger.debug(
msg.format("d_out_kernel", d_out_kernel.shape, reuse))
d_out_bias = tf.get_variable(
"d_out_bias", shape=[1, 1], initializer=xavier_init())
logger.debug(msg.format("d_out_bias", d_out_bias.shape, reuse))
input_concat = tf.concat(
[d_out, constraints_out],
axis=1, name="input_concat_{}".format(reuse))
logger.debug(msg.format(
"input_concat", input_concat.shape, reuse))
d_constrained_out = tf.add(
tf.matmul(input_concat, d_out_kernel), d_out_bias,
name="d_constrained_out_{}".format(reuse))
logger.debug(msg.format(
"constrained_out", d_constrained_out.shape, reuse))
# define summaries on the last layer
d_summaries = tf.summary.merge(
[stats_summaries(d_constrained_out), d_out_summaries])
return d_constrained_out, d_summaries
def _gan28_discr(experiment=None, X=None, reuse=False, **kwargs):
lrelu = partial(leaky_relu, leakiness=experiment.leak)
h_dim = experiment.h_dim
logger = experiment.logger
msg = "D_SHAPE {} {} [reuse={}]"
logger.debug(msg.format("in", X.shape, reuse))
with tf.variable_scope("discriminator", reuse=reuse):
with tf.variable_scope("hidden1"):
d_hidden1 = conv2d(
X, filters=h_dim, kernel_size=5, strides=2, padding="same",
activation=lrelu, kernel_initializer=xavier_init())
logger.debug(msg.format("dh1", d_hidden1.shape, reuse))
with tf.variable_scope("hidden2"):
d_hidden2 = conv2d(
d_hidden1, filters=h_dim * 2, kernel_size=5, strides=2,
padding="same", activation=lrelu,
kernel_initializer=xavier_init())
logger.debug(msg.format("dh2", d_hidden2.shape, reuse))
with tf.variable_scope("hidden3"):
d_hidden2 = tf.reshape(
d_hidden2, [-1, np.prod(d_hidden2.shape[1:], dtype=int)])
d_hidden3 = dense(d_hidden2, 1024, activation=lrelu,
kernel_initializer=xavier_init())
logger.debug(msg.format("dh3", d_hidden3.shape, reuse))
with tf.variable_scope("output"):
d_out = dense(d_hidden3, 1, kernel_initializer=xavier_init())
logger.debug(msg.format("out", d_out.shape, reuse))
# define summaries on the last layer
d_summaries = stats_summaries(d_out)
return d_out, d_summaries
def skip_constraints():
with tf.variable_scope("output"):
d_out = tf.add(tf.matmul(d_hidden4, d_out_kernel),
d_out_bias, name="d_out_{}".format(reuse))
logger.debug(msg.format("out", d_out.shape, reuse))
# define summaries on the last layer
d_summaries = tf.summary.merge(
[stats_summaries(d_out), d_summaries_dh4])
return d_out, d_summaries
def _can60_discr_32layer_auto(
experiment=None, X=None, reuse=False, use_constraints=None,
constraints_features=None, **kwargs):
lrelu = partial(leaky_relu, leakiness=experiment.leak)
h_dim = experiment.h_dim
logger = experiment.logger
msg = "D_SHAPE {} {} [reuse={}]"
logger.debug(msg.format("in", X.shape, reuse))
with tf.variable_scope("discriminator", reuse=reuse):
with tf.variable_scope("hidden1"):
d_hidden1 = conv2d(
X, filters=h_dim, kernel_size=5, strides=2, padding="same",
activation=lrelu, kernel_initializer=xavier_init())
logger.debug(msg.format("dh1", d_hidden1.shape, reuse))
with tf.variable_scope("hidden15"):
d_hidden15 = conv2d(
d_hidden1, filters=h_dim, kernel_size=5, strides=2,
padding="same", activation=lrelu,
kernel_initializer=xavier_init())
logger.debug(msg.format("dh15", d_hidden15.shape, reuse))
with tf.variable_scope("hidden2"):
d_hidden2 = conv2d(
d_hidden15, filters=h_dim * 2, kernel_size=5, strides=2,
padding="same", activation=lrelu,
kernel_initializer=xavier_init())
logger.debug(msg.format("dh2", d_hidden2.shape, reuse))
d_hidden2 = tf.reshape(
d_hidden2, [-1, np.prod(d_hidden2.shape[1:], dtype=int)])
logger.debug(msg.format("dh2", d_hidden2.shape, reuse))
with tf.variable_scope("hidden3"):
d_hidden3 = dense(d_hidden2, 1024, activation=lrelu,
kernel_initializer=xavier_init())
logger.debug(msg.format("dh3", d_hidden3.shape, reuse))
with tf.variable_scope("hidden4"):
d_hidden4 = dense(d_hidden3, 32, activation=lrelu,
kernel_initializer=xavier_init())
logger.debug(msg.format("dh4", d_hidden4.shape, reuse))
d_summaries_dh4 = stats_summaries(d_hidden4, "dh4_pre_cond")
with tf.variable_scope("shared_weights"):
d_out_kernel = tf.get_variable(
"d_out_kernel", shape=[32, 1], initializer=xavier_init())
logger.debug(msg.format("d_out_kernel", d_out_kernel.shape, reuse))
d_out_bias = tf.get_variable(
"d_out_bias", shape=[1, 1], initializer=xavier_init())
logger.debug(msg.format("d_out_bias", d_out_bias.shape, reuse))
def skip_constraints():
with tf.variable_scope("output"):
d_out = tf.add(tf.matmul(d_hidden4, d_out_kernel),
d_out_bias, name="d_out_{}".format(reuse))
logger.debug(msg.format("out", d_out.shape, reuse))
# define summaries on the last layer
d_summaries = tf.summary.merge(
[stats_summaries(d_out), d_summaries_dh4])
return d_out, d_summaries
def apply_constraints():
logger.debug("Using constraints: {}".format(
str(experiment.constraints)))
with tf.variable_scope("constrained_out"):
d_constraints_kernel = tf.get_variable(
"d_constraints_kernel",
shape=[constraints_features.shape[1], 1],
initializer=xavier_init())
logger.debug(msg.format(
"d_constraints_kernel", d_constraints_kernel.shape, reuse))
input_concat = tf.concat(
[d_hidden4, constraints_features],
axis=1, name="input_concat_{}".format(reuse))
logger.debug(msg.format(
"input_concat", input_concat.shape, reuse))
weight_concat = tf.concat(
[d_out_kernel, d_constraints_kernel],
axis=0, name="weight_concat_{}".format(reuse))
logger.debug(msg.format(
"weight_concat", weight_concat.shape, reuse))
d_constrained_out = tf.add(
tf.matmul(input_concat, weight_concat), d_out_bias,
name="d_constrained_out_{}".format(reuse))
logger.debug(msg.format(
"constrained_out", d_constrained_out.shape, reuse))
# define summaries on the last layer
d_summaries = tf.summary.merge(
[stats_summaries(d_constrained_out),
d_summaries_dh4])
return d_constrained_out, d_summaries
return tf.cond(tf.cast(use_constraints, tf.bool),
skip_constraints, apply_constraints)
