Update BasicGAN

BasicGAN

@@ -0,0 +1,44 @@
+import tensorflow as tf
+from tensorflow.keras import layers
+
+# Generator
+def make_generator_model():
+    model = tf.keras.Sequential()
+    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Reshape((7, 7, 256)))
+    assert model.output_shape == (None, 7, 7, 256)
+
+    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
+    assert model.output_shape == (None, 7, 7, 128)
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
+    assert model.output_shape == (None, 14, 14, 64)
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
+    assert model.output_shape == (None, 28, 28, 1)
+
+    return model
+
+# Discriminator
+def make_discriminator_model():
+    model = tf.keras.Sequential()
+    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
+                                     input_shape=[28, 28, 1]))
+    model.add(layers.LeakyReLU())
+    model.add(layers.Dropout(0.3))
+
+    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
+    model.add(layers.LeakyReLU())
+    model.add(layers.Dropout(0.3))
+
+    model.add(layers.Flatten())
+    model.add(layers.Dense(1))
+
+    return model