chore: refactored gan_01.py

gan_01.py

@@ -7,12 +7,20 @@ from keras.models import Sequential
 from keras.datasets import fashion_mnist
 from keras.optimizers import Adam
 
+z_dim = 100
+
+img_lines = 28
+img_columns = 28
+img_channels = 1
+
+img_shape = (img_lines, img_columns, img_channels)
+
 
 def build_generator(img_shape, z_dim):
     model = Sequential()
     model.add(Dense(128, input_dim=z_dim))
     model.add(LeakyReLU(alpha=0.01))
-    model.add(Dense(28*28*1, activation='tanh'))
+    model.add(Dense(img_lines*img_columns*img_channels, activation='tanh'))
     model.add(Reshape(img_shape))
     return model
 
@@ -34,14 +42,6 @@ def build_gan(generator, discriminator):
     return model
 
 
-z_dim = 100
-
-img_lines = 28
-img_columns = 28
-img_channels = 1
-
-img_shape = (img_lines, img_columns, img_channels)
-
 discriminator = build_discriminator(img_shape)
 discriminator.compile(loss='binary_crossentropy',
                       optimizer=Adam(), metrics=['accuracy'])
@@ -91,7 +91,7 @@ def train(iterations, batch_size, sample_interval):
             losses.append((d_loss, g_loss))
             accuracies.append((100*accuracy))
             iteration_checkpoints.append(iteration+1)
-            status = 'iteration: {:} [D loss:{:}, acc.:{:2.2%}] [G loss: {:}]'.format(
+            status = 'iteration: {:} [D loss: {:}, acc.: {:2.2%}] [G loss: {:}]'.format(
                 iteration+1, d_loss, accuracy, g_loss)
             print(status)
             sample_images(generator, iteration+1)