def create_discriminator(img_shape=(28, 28, 1)):
sequence = Input(shape=img_shape)
# model = Flatten()(sequence)
# model = Dense(512)(model)
# model = LeakyReLU(alpha=0.2)(model)
# model = Dense(256)(model)
# model = LeakyReLU(alpha=0.2)(model)
# output = Dense(1, activation='sigmoid')(model)
model = Conv2D(32, kernel_size=3, strides=2, padding='same')(sequence)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Conv2D(64, kernel_size=3, strides=2, padding='same')(model)
model = ZeroPadding2D(padding=((0, 1), (0, 1)))(model)
model = BatchNormalization(momentum=0.8)(model)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Conv2D(128, kernel_size=3, strides=2, padding='same')(model)
model = BatchNormalization(momentum=0.8)(model)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Conv2D(256, kernel_size=3, strides=2, padding='same')(model)
model = BatchNormalization(momentum=0.8)(model)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Flatten()(model)
output = Dense(1, activation='sigmoid')(model)
model = Model(inputs=sequence, outputs=output)
model.summary()
return model
def create_discriminator():
discriminator_input = Input(shape=(28, 28, 1))
discriminator = Conv2D(65, 5, strides=(2, 2), padding='same')(discriminator_input)
discriminator = LeakyReLU()(discriminator)
discriminator = Dropout(0.4)(discriminator)
discriminator = Conv2D(256, 5, strides=(2, 2), padding='same')(discriminator)
discriminator = LeakyReLU()(discriminator)
discriminator = Dropout(0.4)(discriminator)
discriminator = Conv2D(512, 5, strides=(2, 2), padding='same')(discriminator)
discriminator = LeakyReLU()(discriminator)
discriminator = Dropout(0.4)(discriminator)
discriminator = Flatten()(discriminator)
discriminator_output = Dense(1, activation='sigmoid')(discriminator)
discriminator = Model(discriminator_input, discriminator_output)
discriminator.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
discriminator.summary()
return discriminator
def build_critic(self):
num_feat = 1
max_len = self.inp_cols
mus = Input(shape=(max_len, num_feat))
condition_tensor = Input(shape=(NUM_CONDS, ))
model = Conv1D(16, kernel_size=2, padding="same")(mus)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Conv1D(32, kernel_size=2, padding="same")(model)
model = BatchNormalization(momentum=0.8)(model)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Conv1D(64, kernel_size=2, padding="same")(model)
model = BatchNormalization(momentum=0.8)(model)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Conv1D(128, kernel_size=2, padding="same")(model)
model = BatchNormalization(momentum=0.8)(model)
model = LeakyReLU(alpha=0.2)(model)
model = Dropout(0.25)(model)
model = Flatten()(model)
model = Concatenate(axis=1)([model, condition_tensor])
model = Dense(1)(model)
output_layer = model
model = Model([mus, condition_tensor], output_layer)
model.summary()
validity = model([mus, condition_tensor])
model = Model([mus, condition_tensor], validity)
return model
