#DaydayUp1
import MyDay as M
'''
def dayUP(df):
dayup=1
for i in range(365):
if i % 7 in[6,0]:
dayup=(1-0.01)*dayup
else:
dayup=(1+df)*dayup
return dayup
'''
dayfactory = 0.001
while M(dayfactory) < 37.78:
dayfactory += 0.001
print('{:.3f}'.format(dayfactory))
def count_finite_det(design, one_purpose):
return np.linalg.det(M(purpose=one_purpose, design=design,
kernel=kernel, p=p, h=h))
def main():
m=M()
t1=T1(m)
t2=T2(m)
t1.join()
t2.join()
def calc_gpu_fraction(fraction_string):
idx, num = fraction_string.split('/')
idx, num = float(idx), float(num)
fraction = 1 / (num - idx + 1)
print "[*] GPU : %.4f" % fraction
return fraction
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = calc_gpu_fraction('1/3')
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Instantiate simulators for synthetic gradient and synthetic input
gradient_simulator = M(output_dimension, num_parameters, n_layer, sess)
input_simulator = I(IMAGE_PIXELS * IMAGE_PIXELS, input_dimension, n_layer,
sess)
iteration = 0
while iteration < 20000:
iteration += 1
batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
if n_layer != 1:
syn_input_val = input_simulator.get_syn_input(batch_xs, iteration)
else:
syn_input_val = batch_xs.astype(np.float32)
syn_input_val = append_ones(syn_input_val)
