def train():
# load data
batch_size = 128
tr_X, tr_y, va_X, va_y, te_X, te_y = pp_data.load_data()
n_batches = int(tr_X.shape[0] / batch_size)
# normalize data between [-1,1]
tr_X = (tr_X - 0.5) * 2
tr_X = tr_X.reshape((50000, 1, 28, 28))
print tr_X.shape
# generator
a0 = InputLayer(100)
a1 = Dense(128 * 7 * 7, act='linear')(a0)
a1 = BN(axis=0)(a1)
a1 = Reshape(out_shape=(128, 7, 7))(a1)
a1 = Convolution2D(64, 5, 5, act='linear', border_mode=(2, 2))(a1)
a1 = BN(axis=(0, 2, 3))(a1)
a1 = Activation('leaky_relu')(a1)
a1 = UpSampling2D(size=(2, 2))(a1)
a1 = Convolution2D(32, 5, 5, act='linear', border_mode=(2, 2))(a1)
a1 = BN(axis=(0, 2, 3))(a1)
a1 = Activation('leaky_relu')(a1)
a1 = UpSampling2D(size=(2, 2))(a1)
a8 = Convolution2D(1, 5, 5, act='tanh', border_mode=(2, 2), name='a8')(a1)
g = Model([a0], [a8])
g.compile()
g.summary()
# discriminator
b0 = InputLayer((1, 28, 28), name='b0')
b1 = Convolution2D(64, 5, 5, act='relu', border_mode=(0, 0), name='b1')(b0)
b1 = MaxPooling2D(pool_size=(2, 2))(b1)
b1 = Convolution2D(128, 5, 5, act='relu', border_mode=(0, 0))(b1)
b1 = MaxPooling2D(pool_size=(2, 2))(b1)
b1 = Flatten()(b1)
b8 = Dense(1, act='sigmoid')(b1)
d = Model([b0], [b8])
d.compile()
d.summary()
# discriminator on generator
d_on_g = Model()
d.set_trainability(False)
d_on_g.add_models([g, d])
d.set_trainability(True)
d_on_g.joint_models('a8', 'b0')
d_on_g.compile()
d_on_g.summary()
# optimizer
opt_d = Adam(1e-4)
opt_g = Adam(1e-4)
# optimization function
f_train_d = d.get_optimization_func(target_dims=[2],
loss_func='binary_crossentropy',
optimizer=opt_d,
clip=None)
f_train_g = d_on_g.get_optimization_func(target_dims=[2],
loss_func='binary_crossentropy',
optimizer=opt_g,
clip=None)
noise = np.zeros((batch_size, 100))
for epoch in range(100):
print epoch
for index in range(n_batches):
# concatenate generated img and real image to train discriminator.
noise = np.random.uniform(-1, 1, (batch_size, 100))
batch_x = tr_X[index * batch_size:(index + 1) * batch_size]
batch_gx = g.predict(noise)
batch_x_all = np.concatenate((batch_x, batch_gx))
# assign real img label as 1, generated img label as 0
batch_y_all = np.array([1] * batch_size + [0] * batch_size)
batch_y_all = batch_y_all.reshape((batch_y_all.shape[0], 1))
# save out generated img
if index % 50 == 0:
image = pp_data.combine_images(batch_gx)
image = image * 127.5 + 127.5
if not os.path.exists("img_dcgan"): os.makedirs("img_dcgan")
Image.fromarray(image.astype(
np.uint8)).save("img_dcgan/" + str(epoch) + "_" +
str(index) + ".png")
# train discriminator
d_loss = d.train_on_batch(f_train_d, batch_x_all, batch_y_all)
# assign generate img label as 1, so as to deceive discriminator
noise = np.random.uniform(-1, 1, (batch_size, 100))
batch_y_all = np.array([1] * batch_size)
batch_y_all = batch_y_all.reshape((batch_y_all.shape[0], 1))
# train generator
g_loss = d_on_g.train_on_batch(f_train_g, noise, batch_y_all)
print index, "d_loss:", d_loss, "\tg_loss:", g_loss
# print summary info of model
md.summary()
### callbacks (optional)
# save model every n epoch (optional)
dump_fd = 'imdb_lstm_models'
if not os.path.exists(dump_fd): os.makedirs(dump_fd)
save_model = SaveModel(dump_fd=dump_fd, call_freq=2)
# validate model every n epoch (optional)
validation = Validation(tr_x=tr_x, tr_y=tr_y,
va_x=None, va_y=None,
te_x=te_x, te_y=te_y,
batch_size=500,
metrics=['categorical_error', 'categorical_crossentropy'],
call_freq=1)
# callbacks function
callbacks = [validation, save_model]
### train model
# optimization method
optimizer = Rmsprop(0.001)
md.fit(x=tr_x, y=tr_y, batch_size=32, n_epochs=20,
loss_func='categorical_crossentropy', optimizer=optimizer,
callbacks=callbacks)
### predict using model
pred_y = md.predict(te_x)
if not os.path.exists('Md'): os.makedirs('Md') # create folder
save_model = SaveModel(dump_fd='Md', call_freq=1)
# validate model every n epoch (optional)
validation = Validation(tr_x=tr_X,
tr_y=tr_y,
va_x=None,
va_y=None,
te_x=te_X,
te_y=te_y,
batch_size=100,
metrics=['categorical_error'],
call_freq=1,
dump_path='validation.p')
# callbacks function
callbacks = [validation, save_model]
### train model
md.fit(x=tr_X,
y=tr_y,
batch_size=100,
n_epochs=10,
loss_func='categorical_crossentropy',
optimizer=optimizer,
callbacks=callbacks,
verbose=2)
### predict using model
pred_y = md.predict(te_X, batch_size=100)
save_model = SaveModel(dump_fd=dump_fd, call_freq=2)
# validate model every n epoch (optional)
validation = Validation(tr_x=tr_x,
tr_y=tr_y,
va_x=None,
va_y=None,
te_x=te_x,
te_y=te_y,
batch_size=500,
metrics=['categorical_error'],
call_freq=1)
# callbacks function
callbacks = [validation, save_model]
### train model
# optimization method
optimizer = Adam(lr=0.001) # Try SGD, Adagrad, Rmsprop, etc. instead
md.fit(x=tr_x,
y=tr_y,
batch_size=500,
n_epochs=101,
loss_func='categorical_crossentropy',
optimizer=optimizer,
callbacks=callbacks)
### predict using model
pred_y = md.predict(te_x)
