def __build_net__(self):
with tf_utils.set_device_mode(par.gpu_mode):
self.X = tf.placeholder(shape=[None]+self.input_shape, dtype=tf.float32, name='X')
self.Z = tf.placeholder(shape=[None, self.noise_dim], dtype=tf.float32, name='random_z')
self.G = self.Generator(self.Z , self.X.shape[1])
self.D = self.Discriminator(self.X, self.num_classes)
self.D_G = self.Discriminator(self.G, self.num_classes)
self.__set_loss_and_optim__()
return
def __build_net__(self):
self.X = tf.placeholder(dtype=tf.float32, shape=[None,]+self.input_shape)
self.y = tf.placeholder(dtype=tf.int32, shape=(None,))
self.dropout = tf.placeholder(dtype=tf.float32, shape=())
self.batch_size = tf.placeholder(dtype=tf.int32, shape=())
self.Y = tf.one_hot(self.y, depth=self.num_classes, axis=-1)
x = tf.transpose(self.X,[1,0,2])
with tf.name_scope('LSTM') and tf_utils.set_device_mode(self.gpu_mode):
#if True:
L0, _ = tf_utils.LSTMlayer(x, 256, self.batch_size,0, self.gpu_mode)
L0 = tf.nn.dropout(L0, keep_prob=1-self.dropout)
L1, _ = tf_utils.LSTMlayer(L0, 512, self.batch_size, 1, self.gpu_mode)
L1 = tf.nn.dropout(L1, keep_prob=1-self.dropout)
L2, _ = tf_utils.LSTMlayer(L1, 256, self.batch_size, 2, self.gpu_mode)
L2 = tf.nn.dropout(L2, keep_prob=1-self.dropout)
L2 = L2[-1]
#print(L2.shape)
with tf.name_scope('MLP') and tf_utils.set_device_mode(self.gpu_mode):
MLP1 = tf_utils.Dense(L2, 256, name='M1' ,activation=None)
MLP1 = tf.nn.dropout(MLP1, keep_prob=1-self.dropout)
MLP2 = tf_utils.Dense(MLP1, 128, name='M2',activation=None)
MLP2 = tf.nn.dropout(MLP2, keep_prob=1 - self.dropout)
self.logit = tf_utils.Dense(MLP2, self.num_classes, 'logit')
self.out = tf.nn.softmax(self.logit)
self.loss = tf.nn.softmax_cross_entropy_with_logits(
logits=self.logit,
labels=self.Y,
)
self.loss = tf.reduce_mean(self.loss)
self.optim = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss)
def __build_net__(self):
with tf_utils.set_device_mode(par.gpu_mode):
self.X = tf.placeholder(shape=[None] + self.input_shape, dtype=tf.float32, name='X')
self.Z = tf.placeholder(shape=[None, self.noise_dim], dtype=tf.float32, name='random_z')
self.c_dis = tf.placeholder(shape=[None], dtype=tf.uint8, name='input_c_disc')
self.c_cont = tf.placeholder(shape=[None,self.num_continuous], dtype=tf.float32, name='input_c_cont')
with tf_utils.set_device_mode(False):
self.C_DISC = tf.one_hot(indices=self.c_dis, depth=self.num_discrete, dtype=tf.float32, axis=-1)
#TODO: erase
print(self.C_DISC.shape)
latent = tf.concat([self.C_DISC, self.Z, self.c_cont], 1)
with tf_utils.set_device_mode(par.gpu_mode):
latent = latent
self.G = self.Generator(latent, self.X.shape[1])
self.D, _, _ = self.Discriminator(self.X, self.num_classes)
self.D_G,self.Qdis, self.Qcont_params = self.Discriminator(self.G, self.num_classes)
#with tf_utils.set_device_mode(False):
self.__set_loss_and_optim__()
return