//x = concatenate([x_mask,x_mask,x_mask,x_mask,x_mask,x_mask])
//x = keras.layers.multiply([x,x_grad])
x = myConv(x_src,32,strides=2)
x = concatenate([x,x_pose])
x = myConv(x,64,strides=2)
x = myConv(x,128,strides=2)
x = myConv(x,128,strides=2)
x = myConv(x,256,strides=2)
x = Flatten()(x)
x = myDense(x,10,activation="relu")
y = myDense(x,2,activation="softmax")
model = Model(inputs=[x_src,x_pose],outputs=y, name="discriminator")
After Change
y = myDense(x,2,activation="softmax")
"""
model = Model(inputs=[x_src,x_tgt_pose],outputs=y, name="discriminator")
return model
def gan(generator,discriminator,param,feat_net,feat_weights):
