def main():
X, Y = getData()
rX = X * np.reshape(pixelDBR.reward(Y, tarwave, wavelength, bandwidth),
newshape=(-1, 1))
rX = np.sum(rX, axis=0)
minX = np.min(rX)
rX = rX - minX
avgX = np.mean(rX)
result_state = (rX >= avgX).astype(int)
# result_state = np.reshape(result_state, newshape=N_pixel)
result_R = pixelDBR.calR(result_state, dx, N_pixel, wavelength, nh, nl)
result_R = np.reshape(result_R, newshape=(1, wavelength.shape[1]))
result_reward = pixelDBR.reward(result_R, tarwave, wavelength, bandwidth)
# result_fwhml, result_fwhmf = pixelDBR.calBand(result_R, wavelength, tarwave, minwave, wavestep, 0.5)
# result_99l, result_99f = pixelDBR.calBand(result_R, wavelength, tarwave, minwave, wavestep, 0.99)
print("======== Result ========")
print('result reward: ', result_reward)
# print('resulr fwhm: {} um, {:.3f} THz'.format(result_fwhml, result_fwhmf*10**-12))
# print('resulr 99% width: {} um, {:.3f} THz'.format(result_99l, result_99f*10**-12))
thickness = getThickness(result_state, dx, N_pixel)
print(thickness)
for idx, x in enumerate(result_state):
if idx == 0:
print("[{}, ".format(x), end='')
elif idx == N_pixel - 1:
print("{}]".format(x), end='')
else:
print("{}, ".format(x), end='')
x = np.reshape(wavelength, wavelength.shape[1])
result_R = np.reshape(result_R, wavelength.shape[1])
plt.figure(1)
plt.plot(x, result_R)
plt.figure(2)
x = (c * (1. / wavelength))
x = np.reshape(x * (10**-12), wavelength.shape[1])
plt.plot(x, result_R)
plt.figure(3)
lx = np.arange(N_pixel)
plt.bar(lx, result_state, width=1, color='blue')
plt.show()
def Ratio_Optimization(output_folder, weight_name_save, n_batch, lr_rate, num_layers, n_hidden):
init_list_rand = tf.constant(np.random.randint(2, size=(1, N_pixel)), dtype=tf.float32)
X = tf.get_variable(name='b', initializer=init_list_rand)
Xint = binaryRound(X)
Xint = tf.clip_by_value(Xint, clip_value_min=0, clip_value_max=1)
Y = tf.placeholder(tf.float32, shape=[None, OUTPUT_SIZE], name="output_y")
weights, biases = load_weights(output_folder, weight_name_save, num_layers)
Yhat = forwardprop(Xint, weights, biases, num_layers)
Inval = tf.matmul(Y, tf.transpose(Yhat))
Outval = tf.matmul((1-Y), tf.transpose(Yhat))
# cost = Inval / Outval
cost = Outval / Inval
optimizer = tf.train.AdamOptimizer(learning_rate=1E-3).minimize(cost, var_list=[X])
design_y = pd.read_csv(PATH + "/SpectFile(200,800)_500.csv", header=None)
design_y = design_y.values
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for n in range(10000):
sess.run(optimizer, feed_dict={Y: design_y})
if (n % 100) == 0:
temp_R = np.reshape(sess.run(Yhat), newshape=(1, wavelength.shape[1]))
temp_cost = sess.run(cost, feed_dict={Y: design_y})[0][0]
temp_reward = pixelDBR.reward(temp_R, tarwave, wavelength, bandwidth)
print("{}th epoch, reward: {:.4f}, cost: {:.4f}".format(n, temp_reward[0], temp_cost))
optimized_x = np.reshape(Xint.eval().astype(int), newshape=N_pixel)
# optimized_R = np.reshape(sess.run(Yhat), newshape=(1, wavelength.shape[1]))
optimized_R = np.reshape(pixelDBR.calR(optimized_x, dx, N_pixel, wavelength, nh, nl), newshape=(1, wavelength.shape[1]))
optimized_reward = pixelDBR.reward(optimized_R, tarwave, wavelength, bandwidth)
print("Optimized result: {:.4f}".format(optimized_reward[0]))
print(optimized_x)
wavelength_x = np.reshape(wavelength, wavelength.shape[1])
optimized_R = np.reshape(optimized_R, wavelength.shape[1])
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(wavelength_x, optimized_R)
pixel_x = np.arange(N_pixel)
plt.subplot(2, 1, 2)
plt.bar(pixel_x, optimized_x, width=1, color="black")
plt.show()
def main():
X, Y = getData()
rX = X * np.reshape(pixelDBR.reward(Y, tarwave, wavelength, bandwidth),
newshape=(-1, 1))
rX = np.sum(rX, axis=0)
minX = np.min(rX)
rX = rX - minX
avgX = np.mean(rX)
result_state = (rX >= avgX).astype(int)
# result_state = np.reshape(result_state, newshape=N_pixel)
result_R = pixelDBR.calR(result_state, dx, N_pixel, wavelength, nh, nl)
result_R = np.reshape(result_R, newshape=(1, wavelength.shape[1]))
result_reward = pixelDBR.reward(result_R, tarwave, wavelength, bandwidth)
result_fwhm = pixelDBR.calFWHM(result_R, wavelength, tarwave)
print('result reward: ', result_reward)
print('resulr fwhm: ', result_fwhm)
x = np.reshape(wavelength, wavelength.shape[1])
result_R = np.reshape(result_R, wavelength.shape[1])
plt.figure(2)
plt.subplot(2, 1, 1)
plt.plot(x, result_R)
lx = np.arange(N_pixel)
plt.subplot(2, 1, 2)
plt.bar(lx, result_state, width=1, color='blue')
# plt.show()
plt.figure(1)
plt.bar(lx, rX, width=1, color='blue')
plt.show()
thickness = getThickness(result_state, dx, N_pixel)
print(thickness)
def main():
X, Y, Nsample = getData()
reward = np.reshape(pixelDBR.reward(Y, tarwave, wavelength, bandwidth),
newshape=(-1, 1))
Pt = Tstatic(reward, Nsample)
x = np.arange(10)
xs = np.arange(0.1, 1.1, 0.1)
Tvalues = [
'0,1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0'
]
plt.bar(x, Pt)
plt.xticks(x, Tvalues)
plt.show()
with open('D:/1D_DBR/Figure_plot/Data_distribution(04).csv', "a") as sf:
np.savetxt(sf,
np.reshape(xs, (1, xs.shape[0])),
fmt='%.1f',
delimiter=',')
with open('D:/1D_DBR/Figure_plot/Data_distribution(04).csv', "a") as sf:
np.savetxt(sf, np.reshape(Pt, (1, len(Pt))), fmt='%d', delimiter=',')
def Ratio_Optimization(output_folder, weight_name_save, n_batch, lr_rate,
num_layers, n_hidden):
OUTPUT_SIZE = wavelength.shape[1]
idx_1 = np.where(wavelength == int(tarwave - bandwidth / 2))[1][0]
idx_2 = np.where(wavelength == int(tarwave + bandwidth / 2))[1][0]
design_y = np.zeros((1, OUTPUT_SIZE))
design_y[0, idx_1:idx_2 + 1] = 1
design_y.tolist()
init_list_rand = tf.constant(np.random.randint(2, size=(1, N_pixel)),
dtype=tf.float32)
X = tf.get_variable(name='b', initializer=init_list_rand)
Xint = binaryRound(X)
Xint = tf.clip_by_value(Xint, clip_value_min=0, clip_value_max=1)
Y = tf.placeholder(tf.float32, shape=[None, OUTPUT_SIZE], name="output_y")
weights, biases = load_weights(output_folder, weight_name_save, num_layers)
Yhat = forwardprop(Xint, weights, biases, num_layers)
idxwidth = idx_2 - idx_1
Inval = tf.reduce_mean(tf.matmul(Y, tf.transpose(Yhat))) / idxwidth
Outval = tf.reduce_mean(tf.matmul(
(1 - Y), tf.transpose(Yhat))) / (OUTPUT_SIZE - idxwidth)
# cost = Outval / Inval
# cost = Outval * (10 - Inval)
cost = Outval * (1 - Inval)
optimizer = tf.train.AdamOptimizer(learning_rate=1E-4).minimize(
cost, var_list=[X])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for n in range(20000):
sess.run(optimizer, feed_dict={Y: design_y})
if (n % 100) == 0:
temp_R = np.reshape(sess.run(Yhat),
newshape=(1, wavelength.shape[1]))
temp_cost = sess.run(cost, feed_dict={Y: design_y})
temp_Inval = sess.run(Inval, feed_dict={Y: design_y})
temp_reward = pixelDBR.reward(temp_R, tarwave, wavelength,
bandwidth)
print(
"{}th epoch, reward: {:.4f}, cost: {:.4f}, Inval: {:.4f}".
format(n, temp_reward[0], temp_cost, temp_Inval))
op_x = np.reshape(Xint.eval().astype(int), newshape=N_pixel)
# op_R = np.reshape(sess.run(Yhat), newshape=(1, wavelength.shape[1]))
op_R = np.reshape(pixelDBR.calR(op_x, dx, N_pixel, wavelength, nh, nl),
newshape=(1, wavelength.shape[1]))
op_reward = pixelDBR.reward(op_R, tarwave, wavelength, bandwidth)
op_fwhml, op_fwhmf = pixelDBR.calBand(op_R, wavelength, tarwave,
minwave, wavestep, 0.5)
op_99l, op_99f = pixelDBR.calBand(op_R, wavelength, tarwave, minwave,
wavestep, 0.99)
print("======== Result ========")
print('result fwhm: {} um, {:.3f} THz'.format(op_fwhml,
op_fwhmf * 10**-12))
print('result 99% width: {} um, {:.3f} THz'.format(op_99l,
op_99f * 10**-12))
print("Optimized result: {:.4f}".format(op_reward[0]))
for idx, x in enumerate(op_x):
if idx == 0:
print("[{}, ".format(x), end='')
elif idx == N_pixel - 1:
print("{}]".format(x), end='')
else:
print("{}, ".format(x), end='')
wavelength_x = np.reshape(wavelength, wavelength.shape[1])
op_R = np.reshape(op_R, wavelength.shape[1])
plt.figure(1)
plt.plot(wavelength_x, op_R)
plt.figure(2)
wavelength_x = (c * (1. / wavelength)) * 10**(-12)
wavelength_x = np.reshape(wavelength_x, wavelength.shape[1])
plt.plot(wavelength_x, op_R)
plt.figure(3)
pixel_x = np.arange(N_pixel)
plt.bar(pixel_x, op_x, width=1, color="black")
plt.show()