from keras.layers import Conv2D, Activation
from keras.layers import Add, Lambda
from keras.initializers import RandomNormal
weight_init = RandomNormal(mean=0., stddev=0.02)
def resnet_block(input, n_blocks, n_filters, kernel_size=(1, 3)):
output = input
for i in range(n_blocks):
output = Activation('relu')(output)
output = Conv2D(filters=n_filters, kernel_size=kernel_size,
strides=1, padding='same',
kernel_initializer=weight_init)(output)
output = Activation('relu')(output)
output = Conv2D(filters=n_filters, kernel_size=kernel_size, strides=1,
padding='same', kernel_initializer=weight_init)(output)
output = Lambda(lambda x: x * 0.3)(output)
# Residual Connection
output = Add()([input, output])
return output
from keras.layers import Input, Activation, Softmax
from keras.layers import Dense, Conv2D
from keras.layers.core import Reshape
from keras.models import Model
def build_resnet_generator(input_shape, n_filters, n_residual_blocks,
seq_len, vocabulary_size):
inputs = Input(shape=input_shape)
# Dense 1: 1 x seq_len x n_filters
x = Dense(1 * seq_len * n_filters, input_shape=input_shape)(inputs)
x = Reshape((1, seq_len, n_filters))(x)
# ResNet blocks
x = resnet_block(x, n_residual_blocks, n_filters)
# Output layer
x = Conv2D(filters=vocabulary_size, kernel_size=1, padding='same')(x)
x = Softmax(axis=3)(x)
# create model graph
model = Model(inputs=inputs, outputs=x, name='Generator')
print("\nGenerator ResNet")
model.summary()
return model
from keras.layers import Input, Flatten
from keras.layers import Dense, Conv2D
from keras.models import Model
def build_resnet_discriminator(input_shape, n_filters, n_residual_blocks,
kernel_size=(1, 1), stride=1):
input = Input(shape=input_shape)
x = Conv2D(filters=n_filters, kernel_size=kernel_size, strides=stride,
padding='same')(input)
x = resnet_block(x, n_residual_blocks, n_filters)
x = Flatten()(x)
x = Dense(1)(x)
# create model graph
model = Model(inputs=input, outputs=x, name='Discriminator')
print("\nDiscriminator ResNet")
model.summary()
return model
