#----------Initialize----------
tf.reset_default_graph()
# optimizer1 = tf.train.GradientDescentOptimizer(0.001)
optimizer1 = tf.train.AdamOptimizer(0.01)
# optimizer2 = tf.train.GradientDescentOptimizer(0.0005)
optimizer2 = tf.train.AdamOptimizer(0.001)
#----------load regressors----------
folded_cascode = Folded_Cascode()
classab = ClassAB()
folded_cascode_spice = Folded_Cascode_spice()
classab_spice = ClassAB_spice()
sxin = make_var(
"amplifier", "fc_classab", (1, 28),
tf.random_uniform_initializer(-np.ones((1, 28)), np.ones((1, 28))))
#==================================================================
#******************** Tensorflow Initiation *********************
#==================================================================
hardcost, usercost, tf_specs, tf_param, tf_metric, tf_mid, tf_const = graph_tf(
sxin, folded_cascode, classab)
opt1 = optimizer1.minimize(hardcost)
opt2 = optimizer2.minimize(hardcost)
init = tf.global_variables_initializer()
calc = 1
lastvalue = -1000000
lst_params = []
#==================================================================
#***************** Building the graph ***************************
#==================================================================
tf.compat.v1.disable_eager_execution()
#----------Initialize----------
tf.compat.v1.reset_default_graph()
optimizer1 = tf.compat.v1.train.AdamOptimizer(0.01)
optimizer2 = tf.compat.v1.train.AdamOptimizer(0.001)
#----------load regressors----------
lna1 = LNA(tech=28)
sh1 = SH(tech=28)
sxin = make_var(
"RF_FE", "LNA", (1, 9),
tf.random_uniform_initializer(-np.ones((1, 9)), np.ones((1, 9))))
hardcost, usercost, tf_specs, tf_params, tf_metrics, tf_const = graph_tf(
sxin, lna1, sh1)
#==================================================================
#******************** Tensorflow Initiation *********************
#==================================================================
opt1 = optimizer1.minimize(usercost)
opt2 = optimizer2.minimize(hardcost)
init = tf.compat.v1.global_variables_initializer()
calc = 1
lastvalue = -1000000
lst_params = []
optimizer1 = tf.compat.v1.train.GradientDescentOptimizer(0.01)
optimizer2 = tf.compat.v1.train.GradientDescentOptimizer(0.001)
# ----------load regressors----------
seqp11 = SEQ1()
seqp21 = SEQ2()
compp1 = COMPP2()
thdac1 = THDAC2()
comppspice1 = Compp_spice2()
dacthspice1 = DACTH2_spice()
seqp1spice1 = Seqpart1_spice()
seqp2spice1 = Seqpart2_spice()
var_in = make_var(
"SAR_ADC", "SEQ_COMPP_THDAC", (1, 26),
tf.random_uniform_initializer(-np.ones((1, 26)), np.ones((1, 26))))
xload = tf.compat.v1.placeholder(tf.float32, shape=(1, 26))
initvar = var_in.assign(xload)
sxin = 2 * tf.math.sigmoid(var_in) - 1.0
hardcost, softcost, tf_specs, tf_params, tf_metrics, tf_mids, tf_const = graph_tf3(
sxin, seqp11, seqp21, compp1, thdac1)
# tf.float32.real_dtype
# ==================================================================
# ******************** Tensorflow Initiation *********************
# ==================================================================
#***************** Building the graph ***************************
#==================================================================
tf.compat.v1.disable_eager_execution()
#----------Initialize----------
tf.compat.v1.reset_default_graph()
# optimizer1 = tf.train.GradientDescentOptimizer(0.001)
optimizer1 = tf.compat.v1.train.AdamOptimizer(0.001)
# optimizer2 = tf.train.GradientDescentOptimizer(0.0005)
optimizer2 = tf.compat.v1.train.AdamOptimizer(0.001)
#----------load regressors----------
vcdl = vcdl()
dll = dll()
sxin = make_var("vcdl", "dll", (1, 10),
tf.random_uniform_initializer(-np.ones((1, 10)),
np.ones((1, 10)))) #Giao#
#==================================================================
#******************** Tensorflow Initiation *********************
#==================================================================
hardcost, usercost, tf_specs, tf_param, tf_metric, tf_mid, tf_const = graph_tf(
sxin, vcdl, dll)
opt1 = optimizer1.minimize(hardcost)
opt2 = optimizer2.minimize(hardcost)
init = tf.compat.v1.global_variables_initializer()
calc = 1
lastvalue = -1000000
lst_params = []
#***************** Building the graph ***************************
#==================================================================
tf.compat.v1.disable_eager_execution()
#----------Initialize----------
tf.compat.v1.reset_default_graph()
# optimizer1 = tf.train.GradientDescentOptimizer(0.001)
optimizer1 = tf.compat.v1.train.AdamOptimizer(0.001)
# optimizer2 = tf.train.GradientDescentOptimizer(0.0005)
optimizer2 = tf.compat.v1.train.AdamOptimizer(0.001)
#----------load regressors----------
cs_driver_cml = cs_driver_cml()
cs_array_8b = cs_array_8b()
sxin = make_var("cs_driver_cml", "cs_array_8b", (1, 9),
tf.random_uniform_initializer(-np.ones((1, 9)),
np.ones((1, 9)))) #Giao#
#==================================================================
#******************** Tensorflow Initiation *********************
#==================================================================
hardcost, usercost, tf_specs, tf_param, tf_metric, tf_mid, tf_const = graph_tf(
sxin, cs_driver_cml, cs_array_8b)
opt1 = optimizer1.minimize(hardcost)
opt2 = optimizer2.minimize(hardcost)
init = tf.compat.v1.global_variables_initializer()
calc = 1
lastvalue = -1000000
lst_params = []
#***************** Building the graph ***************************
#==================================================================
tf.compat.v1.disable_eager_execution()
#----------Initialize----------
tf.compat.v1.reset_default_graph()
# optimizer1 = tf.train.GradientDescentOptimizer(0.001)
optimizer1 = tf.compat.v1.train.AdamOptimizer(0.001)
# optimizer2 = tf.train.GradientDescentOptimizer(0.0005)
optimizer2 = tf.compat.v1.train.AdamOptimizer(0.001)
#----------load regressors----------
dtc1 = DTC1()
dtc2 = DTC2()
sxin = make_var(
"DTC1", "DTC2", (1, 4),
tf.random_uniform_initializer(-np.ones((1, 4)), np.ones((1, 4))))
#==================================================================
#******************** Tensorflow Initiation *********************
#==================================================================
hardcost, usercost, tf_specs, tf_param, tf_metric, tf_mid, tf_const = graph_tf(
sxin, dtc1, dtc2)
opt1 = optimizer1.minimize(hardcost)
opt2 = optimizer2.minimize(hardcost)
init = tf.compat.v1.global_variables_initializer()
calc = 1
lastvalue = -1000000
lst_params = []
#==================================================================
tf.compat.v1.disable_eager_execution()
#----------Initialize----------
tf.compat.v1.reset_default_graph()
# Define to optimizers. You can change the values in the parenthesis, which is the learning rate
optimizer1 = tf.compat.v1.train.AdamOptimizer(0.001)
optimizer2 = tf.compat.v1.train.AdamOptimizer(0.001)
#----------load regressors----------
# Define an object in class INV
inv = INV()
# Initialize all design parameters. We use random initialization in this case. We have in total 2 design parameters, 1 module here
sxin = make_var(
"INV", "INV", (1, 2),
tf.random_uniform_initializer(-np.ones((1, 2)), np.ones((1, 2))))
#==================================================================
#******************** Tensorflow Initiation *********************
#==================================================================
hardcost, usercost, tf_specs, tf_param, tf_metric, tf_mid, tf_const = graph_tf(
sxin, inv)
# Optimizer1 will minimize hardcost, and optimizer 2 will minimize usercost. Both are defined previously.
opt1 = optimizer1.minimize(hardcost)
opt2 = optimizer2.minimize(usercost)
init = tf.compat.v1.global_variables_initializer()
calc = 1
lastvalue = -1000000
tf.compat.v1.disable_eager_execution()
#----------Initialize----------
tf.compat.v1.reset_default_graph()
optimizer1 = tf.compat.v1.train.AdamOptimizer(0.01)
optimizer2 = tf.compat.v1.train.AdamOptimizer(0.001)
#----------load regressors----------
vco1 = VCO(tech=65)
inbuf2 = INBUF2(tech=65)
#--------load spice netlists--------
vcospice1 = VCOSpice()
inbufspice2 = INBUF2Spice()
var_in = make_var(
"VCO_ADC", "BUF_VCO", (1, 12),
tf.random_uniform_initializer(-np.ones((1, 12)), np.ones((1, 12))))
hardcost, usercost, tf_specs, tf_params, tf_metrics, tf_mids, tf_const = graph_tf2(
var_in, vco1, inbuf2)
#==================================================================
#******************** Tensorflow Initiation *********************
#==================================================================
opt1 = optimizer1.minimize(usercost)
opt2 = optimizer2.minimize(hardcost)
init = tf.compat.v1.global_variables_initializer()
calc = 1