def test_generator_training():
# parameters
file_name = "animals.txt"
genr_hidden_size = 10
disr_hidden_size = 3
num_epochs_d = 20
num_epochs_g = 20
lr = 1
alpha = 0.9
batch_size = 100
# load data
char_list = dataloader.get_char_list(file_name)
X_actual = dataloader.load_data(file_name)
num_examples = X_actual.shape[0]
seq_len = X_actual.shape[1]
# generate
genr_input = np.random.randn(num_examples, len(char_list))
genr = Generator(genr_hidden_size, char_list)
X_generated = genr.generate_tensor(seq_len, num_examples, genr_input)
# train discriminator
disr = Discriminator(len(char_list), disr_hidden_size)
disr.train_RMS(X_actual, X_generated, num_epochs_d, lr, alpha, batch_size)
# evaluate discriminator
accuracy = disr.accuracy(X_actual, X_generated)
print("accuracy: ", accuracy)
# train generator
genr.train_RMS(genr_input,
seq_len,
disr,
num_epochs_g,
1,
lr,
alpha,
batch_size,
print_progress=True)
# evaluate discriminator again
X_generated = genr.generate_tensor(seq_len, num_examples, genr_input)
accuracy = disr.accuracy(X_actual, X_generated)
print("accuracy: ", accuracy)
def test_discriminator():
# parameters
file_name = "animals.txt"
genr_hidden_size = 10
disr_hidden_size = 11
num_epochs = 20
lr = 1
alpha = 0.9
batch_size = 100
# load data
char_list = dataloader.get_char_list(file_name)
X_actual = dataloader.load_data(file_name)
num_examples = X_actual.shape[0]
seq_len = X_actual.shape[1]
# generate
genr = Generator(genr_hidden_size, char_list)
X_generated = genr.generate_tensor(seq_len, num_examples)
# train discriminator
disr = Discriminator(len(char_list), disr_hidden_size)
disr.train_RMS(X_actual,
X_generated,
num_epochs,
lr,
alpha,
batch_size,
print_progress=True)
# print discriminator output
outp = disr.discriminate(np.concatenate((X_actual, X_generated), axis=0))
print(outp)
# evaluate discriminator
accuracy = disr.accuracy(X_actual, X_generated)
print("accuracy: ", accuracy)
class GAN:
# g_hidden_size: size of hidden layer in generator
# d_hidden_size: size of hidden layer in discriminator
# char_list: list of characters the generator can generate
def __init__(self, g_hidden_size, d_hidden_size, char_list):
self.char_list = char_list
self.generator = Generator(g_hidden_size, char_list)
self.discriminator = Discriminator(len(char_list), d_hidden_size)
# X_actual: input data from dataset (not generated)
# n_epochs: total epochs to train entire network
# g_epochs: how long to train generator each epoch
# d_epochs: how long to train disciminator each epoch
# g_initial_lr, g_multiplier: generator RMSprop parameters
# d_initial_lr, d_multiplier: discriminator RMSprop parameters
# g_batch_size, d_batch_size: batch sizes for generator and discriminator
# num_displayed: if print progress is True, this is how many example words
# to display - make this None to display all examples
def train(self,
X_actual,
seq_len,
n_epochs,
g_epochs,
d_epochs,
g_initial_lr,
d_initial_lr,
g_multiplier,
d_multiplier,
g_batch_size,
d_batch_size,
print_progress=False,
num_displayed=None):
num_examples = X_actual.shape[0]
# TODO: make genr_input change every epoch
genr_input = np.random.randn(num_examples, self.generator.input_size)
for i in range(n_epochs):
# generate text
genr_output = self.generator.generate_tensor(
seq_len, num_examples, genr_input)
# train discriminator
self.discriminator.train_RMS(X_actual, genr_output, d_epochs,
d_initial_lr, d_multiplier,
d_batch_size)
# evaluate dicriminator
if print_progress:
genr_output = self.generator.generate_tensor(
seq_len, num_examples, genr_input)
accuracy = self.discriminator.accuracy(X_actual, genr_output)
print("accuracy before generator training: ", accuracy)
# train generator
self.generator.train_RMS(genr_input, seq_len, self.discriminator,
g_epochs, 1, g_initial_lr, g_multiplier,
g_batch_size)
#print(sum(l.magnitude_theta() for l in self.generator.lstm.layers))
# evaluate discriminator
if print_progress:
genr_output = self.generator.generate_tensor(
seq_len, num_examples, genr_input)
accuracy = self.discriminator.accuracy(X_actual, genr_output)
print("accuracy after generator training: ", accuracy)
# display generator's output
if print_progress:
gen_text = self.generator.generate(seq_len, num_examples,
genr_input)
if num_displayed is not None:
gen_text = gen_text[:num_displayed]
for line in gen_text:
print(line)
