trainMidData = tf.placeholder(tf.float32, shape=(batch_siz,en_mid_siz,1),
name="trainMidData")
trainMidNorm = tf.placeholder(tf.float32, shape=(batch_siz),
name="trainMidNorm")
trainOutData = tf.placeholder(tf.float32, shape=(batch_siz,out_siz//2,1),
name="trainOutData")
trainOutNorm = tf.placeholder(tf.float32, shape=(batch_siz),
name="trainOutNorm")
global_steps=tf.Variable(0, trainable=False)
#=========================================================
#----- Training Preparation
if de:
en_butterfly_net = ButterflyLayer(N, in_siz, en_mid_siz, False,
channel_siz, en_nlvl, -1, True,
in_range, en_mid_range)
else:
en_butterfly_net = ButterflyLayer(2*N, in_siz, en_mid_siz, False,
channel_siz, en_nlvl, -1, True,
in_range, en_mid_range)
middle_net = MiddleLayer(in_siz, en_mid_siz, de_mid_siz, sine = True,
a = a[::(2**10//N)], prefixed = 2, std = 0.03)
de_butterfly_net = ButterflyLayer(N, de_mid_siz, out_siz, False,
channel_siz, de_nlvl, 1, True,
de_mid_range, out_range)
y_train_en_mid = en_butterfly_net(trainInData)
testMidNorm = tf.placeholder(tf.float32, shape=(test_siz),
name="testMidNorm")
testOutData = tf.placeholder(tf.float32, shape=(test_siz,out_siz//2,1),
name="testOutData")
testOutNorm = tf.placeholder(tf.float32, shape=(test_siz),
name="testOutNorm")
global_steps=tf.Variable(0, trainable=False)
#=========================================================
#----- Training Preparation
middle_net = MiddleLayer(in_siz, en_mid_siz, de_mid_siz, False)
if butterfly:
en_butterfly_net = ButterflyLayer(N, in_siz, en_mid_siz, False,
channel_siz, en_nlvl, -1, prefixed,
in_range, en_mid_range,0.45)
de_butterfly_net = ButterflyLayer(N, de_mid_siz, out_siz, False,
channel_siz, de_nlvl, 1, prefixed,
de_mid_range, out_range,0.45)
y_train_en_mid = en_butterfly_net(trainInData)
y_train_de_mid = middle_net(y_train_en_mid)
train_output = de_butterfly_net(y_train_de_mid)/N
x_train_output = train_output[:,::2]
y_test_en_mid = en_butterfly_net(testInData)
y_test_de_mid = middle_net(y_test_en_mid)
test_output = de_butterfly_net(y_test_de_mid)/N
x_test_output = test_output[:,::2]
else: