Add basic models for DCGAN

1 files changed, 37 insertions, 0 deletions

diff --git a/models.py b/models.py
new file mode 100644
index 0000000..5a28b27
--- /dev/null
+++ b/models.py
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+# models.py
+# EE4 Computer vision coursework: Models for GAN coursework 
+from keras.models import Model, Sequential
+from keras.layers import *
+
+def get_generator:
+  generator = Sequential([
+          Dense(128*7*7, input_dim=100, activation=LeakyReLU(0.2)),
+          BatchNormalization(),
+          Reshape((7,7,128)),
+          UpSampling2D(),
+          Convolution2D(64, 5, 5, border_mode='same', activation=LeakyReLU(0.2)),
+          BatchNormalization(),
+          UpSampling2D(),
+          Convolution2D(1, 5, 5, border_mode='same', activation='tanh')
+      ])
+  
+  discriminator = Sequential([
+          Convolution2D(64, 5, 5, subsample=(2,2), input_shape=(28,28,1), border_mode='same', activation=LeakyReLU(0.2)),
+          Dropout(0.3),
+          Convolution2D(128, 5, 5, subsample=(2,2), border_mode='same', activation=LeakyReLU(0.2)),
+          Dropout(0.3),
+          Flatten(),
+          Dense(1, activation='sigmoid')
+      ])
+  return generator
+
+def get_discriminator:
+  discriminator = Sequential([
+          Convolution2D(64, 5, 5, subsample=(2,2), input_shape=(28,28,1), border_mode='same', activation=LeakyReLU(0.2)),
+          Dropout(0.3),
+          Convolution2D(128, 5, 5, subsample=(2,2), border_mode='same', activation=LeakyReLU(0.2)),
+          Dropout(0.3),
+          Flatten(),
+          Dense(1, activation='sigmoid')
+      ])
+  return discriminator