converted phasenn_test8 to tf.keras, this script has a custom loss function

phasenn_test8.py

@@ -8,35 +8,39 @@
 
 import numpy as np
 import sys
-from keras.layers import Dense
-from keras import models,layers
-from keras import initializers
 import matplotlib.pyplot as plt
 from scipy import signal
-from keras import backend as K
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Dense
+
 # make tensorflow less verbose ....
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 # custom loss function
 def sparse_loss(y_true, y_pred):
-    mask = K.cast( K.not_equal(y_pred, 0), dtype='float32')
+    mask = tf.cast( tf.not_equal(y_pred, 0), dtype='float32')
-    n = K.sum(mask)
+    n = tf.reduce_sum(mask)
-    return K.sum(K.square((y_pred - y_true)*mask))/n
+    return tf.reduce_sum(tf.square((y_pred - y_true)*mask))/n
 
+'''
 # testing custom loss function
 x = layers.Input(shape=(None,))
 y = layers.Input(shape=(None,))
 loss_func = K.Function([x, y], [sparse_loss(x, y)])
 assert loss_func([[[1,1,1]], [[0,2,0]]]) == np.array([1])
 assert loss_func([[[0,1,0]], [[0,2,0]]]) == np.array([1])
+'''
 
 # constants
 
 N                 = 80      # number of time domain samples in frame
 nb_samples        = 400000
 nb_batch          = 32
-nb_epochs         = 10
+nb_epochs         = 25
 width             = 256
 pairs             = 2*width
 fo_min            = 50
@@ -80,14 +84,13 @@ for i in range(nb_samples):
         filter_phase_rect[i,2*bin]   = np.cos(filter_phase[i,bin])
         filter_phase_rect[i,2*bin+1] = np.sin(filter_phase[i,bin])
 
-model = models.Sequential()
+model = Sequential()
-model.add(layers.Dense(pairs, activation='relu', input_dim=width))
+model.add(Dense(pairs, activation='relu', input_dim=width))
-model.add(layers.Dense(4*pairs, activation='relu'))
+model.add(Dense(4*pairs, activation='relu'))
-model.add(layers.Dense(pairs))
+model.add(Dense(pairs))
 model.summary()
 
-from keras import optimizers
+sgd = keras.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)
 
@@ -135,7 +138,7 @@ if plot_en:
  
     plt.figure(2)
     plt.subplot(211)
-    plt.hist(err_angle*180/np.pi, bins=20)
+    plt.hist(err_angle*180/np.pi, bins=50)
     plt.subplot(212)
     plt.hist(Wo*(Fs/2)/np.pi, bins=20)
     plt.title('phase angle error (deg) and fo (Hz)')

phasenn_test9.py

@@ -12,8 +12,8 @@ import numpy as np
 import sys
 import matplotlib.pyplot as plt
 from scipy import signal
-from tensorflow import keras
 
+from tensorflow import keras
 from tensorflow.keras import Sequential
 from tensorflow.keras.layers import Dense