Update GAN.

gan/gan.py

@@ -17,6 +17,17 @@ def _backward_last(self):
         pass
 
     def backward(self, y, y_hat, z, discriminator):
+        """
+        Generator don't compute directly with loss, so we need discriminator 
+            backprop gradient from earlier layers according backward direction.
+                    
+        Parameters
+        ----------
+        y: vector ones (for optimizing fake to real), we want optimize generator parameters to fool discriminator.
+        y_hat: output pass from generator -> discriminator
+        z: random noise variables.
+        discriminator: discriminator network. 
+        """
         dA = discriminator.return_input_grads(y, y_hat)
         grads = self._backward(dA, None)
         self._update_params(grads)
@@ -27,14 +38,17 @@ def __init__(self, optimizer:object, layers:list, loss_func:object=BinaryCrossEn
         super().__init__(optimizer, layers, loss_func)
 
     def return_input_grads(self, y, y_hat):
+        """
+        Compute gradient of Loss w.r.t inputs, flow gradient to compute gradient of Loss w.r.t generator parameters.
+        """
         dA_prev, _ = self._backward_last(y, y_hat)
         for i in range(len(self.layers)-1, 0, -1):
             backward_func = self.layers[i].backward_layer if isinstance(self.layers[i], LearnableLayer) else self.layers[i].backward
             dA_prev = backward_func(dA_prev, self.layers[i-1])
         return dA_prev
 
 
-def main():
+def main(digit=2):
 
     mnist_dim = 784
 
@@ -86,7 +100,7 @@ def main():
     images, labels = mndata.load_training()
     images, labels = preprocess_data(images, labels, test=True)
 
-    images = images[labels == 2]
+    images = images[labels == digit]
 
     optimizer_G = Adam(alpha=0.006)
     optimizer_D = Adam(alpha=0.006)
@@ -98,8 +112,10 @@ def main():
 
     batch_size = 64
     iterations = 10000
+
+    print("Training GAN with MNIST dataset to generate digit %d" % digit)
     trainerGAN = TrainerGAN(generator, discriminator, batch_size, iterations)
     trainerGAN.train(images)
 
 if __name__ == "__main__":
-    main()
+    main(digit=8)

libs/utils.py

@@ -14,7 +14,6 @@
 from sklearn.metrics import classification_report, confusion_matrix, precision_recall_fscore_support
 from tqdm import tqdm
 from .cifar10_lib import get_file, load_batch
-import matplotlib.pyplot as plt
 from mpl_toolkits.axes_grid1 import ImageGrid
 
 
@@ -83,7 +82,10 @@ def save_grid_images(images, iteration):
         # Iterating over the grid returns the Axes.
         ax.imshow(im, cmap="gray")
 
-    plt.savefig("gan-%d.png" % iteration)    
+    path = os.path.abspath(".")
+    f = "gan-%d.png" % iteration
+    plt.savefig(f)
+    print("Image saved: %s" % os.path.join(path, f))
 
 
 class Trainer:

nn_components/layers.py

@@ -116,7 +116,7 @@ def backward(self, d_prev, prev_layer):
         ----------
         d_prev: gradient of J respect to A[l+1] of the previous layer according backward direction.
         prev_layer: previous layer according forward direction.
-        
+
         Returns
         -------
         d_prev: gradient of J respect to A[l] at the current layer.