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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
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