def setUp(self):
self.session = tf.Session()
self.kern = gplvm.SEKernel(session=self.session)
shape = (8, 3)
self.X = tf.constant(np.random.uniform(size=shape), dtype=c.float_type)
self.Y = tf.constant(np.random.uniform(size=shape), dtype=c.float_type)
self.session.run(tf.global_variables_initializer())
output_dir=flags['latent_samples_dir'])
training_data = Data(flags['training_data'],
shuffle_first=flags['shuffle_data'],
batch_size=flags['training_batch_size'],
log_epochs=flags['data_log_epochs'],
name='TrainingData')
Y = training_data.next_batch()
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=config)
kern = gplvm.SEKernel(session=session,
alpha=flags['kernel_alpha'],
gamma=flags['kernel_gamma'],
ARD=flags['kernel_ard'],
Q=flags['q'])
layer = gplvm.GPLVM(Y=Y,
Q=flags['q'],
kern=kern,
noise_variance=flags['noise_variance'],
latent_point_plotter=lp_plotter,
latent_sample_plotter=ls_plotter,
session=session,
name=flags['gplvm_name'])
layer.build_model()
optimizer = tf.train.AdamOptimizer(learning_rate=flags['learning_rate'])