more realistic range of filters

phasenn_test8.py

@@ -14,6 +14,9 @@ from keras import initializers
 import matplotlib.pyplot as plt
 from scipy import signal
 from keras import backend as K
+# make tensorflow less verbose ....
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 # custom loss function
 def sparse_loss(y_true, y_pred):
@@ -33,7 +36,7 @@ assert loss_func([[[0,1,0]], [[0,2,0]]]) == np.array([1])
 N                 = 80      # number of time domain samples in frame
 nb_samples        = 400000
 nb_batch          = 32
-nb_epochs         = 100
+nb_epochs         = 10
 width             = 256
 pairs             = 2*width
 fo_min            = 50
@@ -64,8 +67,8 @@ for i in range(nb_samples):
     # sample 2nd order IIR filter with random peak freq
 
     r = np.random.rand(2)
-    alpha = 0.1*np.pi + 0.8*np.pi*r[0]
-    gamma = r[1]
+    alpha = 0.1*np.pi + 0.4*np.pi*r[0]
+    gamma = 0.9 + 0.09*r[1]
     w,h = signal.freqz(1, [1, -2*gamma*np.cos(alpha), gamma*gamma], range(1,L[i])*Wo[i])
     
     for m in range(1,L[i]):
@@ -84,7 +87,7 @@ model.add(layers.Dense(pairs))
 model.summary()
 
 from keras import optimizers
-sgd = optimizers.SGD(lr=0.08, decay=1e-6, momentum=0.9, nesterov=True)
+sgd = optimizers.SGD(lr=0.2, decay=1e-6, momentum=0.9, nesterov=True)
 model.compile(loss=sparse_loss, optimizer=sgd)
 history = model.fit(filter_amp, filter_phase_rect, batch_size=nb_batch, epochs=nb_epochs)