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pseudo_cifar.py
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from keras.layers import Input, Dense, Conv2D, BatchNormalization, Activation, GlobalAveragePooling2D, AveragePooling2D
from keras.models import Model
import keras.backend as K
from keras.callbacks import Callback
from keras.utils import to_categorical
from keras.datasets import cifar10
from keras.objectives import categorical_crossentropy
from keras.metrics import categorical_accuracy
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import numpy as np
import pickle, os, zipfile, glob
def basic_conv_block(input, chs, rep):
x = input
for i in range(rep):
x = Conv2D(chs, 3, padding="same")(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
return x
def create_cnn():
input = Input(shape=(32,32,3))
x = basic_conv_block(input, 64, 3)
x = AveragePooling2D(2)(x)
x = basic_conv_block(x, 128, 3)
x = AveragePooling2D(2)(x)
x = basic_conv_block(x, 256, 3)
x = GlobalAveragePooling2D()(x)
x = Dense(10, activation="softmax")(x)
model = Model(input, x)
return model
class PseudoCallback(Callback):
def __init__(self, model, n_labeled_sample, batch_size):
self.n_labeled_sample = n_labeled_sample
self.batch_size = batch_size
self.model = model
self.n_classes = 10
# labeled_unlabeledの作成
(X_train, y_train), (self.X_test, self.y_test) = cifar10.load_data()
indices = np.arange(X_train.shape[0])
np.random.shuffle(indices)
self.X_train_labeled = X_train[indices[:n_labeled_sample]]
self.y_train_labeled = y_train[indices[:n_labeled_sample]]
self.X_train_unlabeled = X_train[indices[n_labeled_sample:]]
self.y_train_unlabeled_groundtruth = y_train[indices[n_labeled_sample:]]
# unlabeledの予測値
self.y_train_unlabeled_prediction = np.random.randint(
10, size=(self.y_train_unlabeled_groundtruth.shape[0], 1))
# steps_per_epoch
self.train_steps_per_epoch = X_train.shape[0] // batch_size
self.test_stepes_per_epoch = self.X_test.shape[0] // batch_size
# unlabeledの重み
self.alpha_t = 0.0
# labeled/unlabeledの一致率推移
self.unlabeled_accuracy = []
self.labeled_accuracy = []
def train_mixture(self):
# 返り値：X, y, フラグ
X_train_join = np.r_[self.X_train_labeled, self.X_train_unlabeled]
y_train_join = np.r_[self.y_train_labeled, self.y_train_unlabeled_prediction]
flag_join = np.r_[np.repeat(0.0, self.X_train_labeled.shape[0]),
np.repeat(1.0, self.X_train_unlabeled.shape[0])].reshape(-1,1)
indices = np.arange(flag_join.shape[0])
np.random.shuffle(indices)
return X_train_join[indices], y_train_join[indices], flag_join[indices]
def train_generator(self):
while True:
X, y, flag = self.train_mixture()
n_batch = X.shape[0] // self.batch_size
for i in range(n_batch):
X_batch = (X[i*self.batch_size:(i+1)*self.batch_size]/255.0).astype(np.float32)
y_batch = to_categorical(y[i*self.batch_size:(i+1)*self.batch_size], self.n_classes)
y_batch = np.c_[y_batch, flag[i*self.batch_size:(i+1)*self.batch_size]]
yield X_batch, y_batch
def test_generator(self):
while True:
indices = np.arange(self.y_test.shape[0])
np.random.shuffle(indices)
for i in range(len(indices)//self.batch_size):
current_indices = indices[i*self.batch_size:(i+1)*self.batch_size]
X_batch = (self.X_test[current_indices] / 255.0).astype(np.float32)
y_batch = to_categorical(self.y_test[current_indices], self.n_classes)
y_batch = np.c_[y_batch, np.repeat(0.0, y_batch.shape[0])] # flagは0とする
yield X_batch, y_batch
def loss_function(self, y_true, y_pred):
y_true_item = y_true[:, :self.n_classes]
unlabeled_flag = y_true[:, self.n_classes]
entropies = categorical_crossentropy(y_true_item, y_pred)
coefs = 1.0-unlabeled_flag + self.alpha_t * unlabeled_flag # 1 if labeled, else alpha_t
return coefs * entropies
def accuracy(self, y_true, y_pred):
y_true_item = y_true[:, :self.n_classes]
return categorical_accuracy(y_true_item, y_pred)
def on_epoch_end(self, epoch, logs):
# alpha(t)の更新
if epoch < 10:
self.alpha_t = 0.0
elif epoch >= 70:
self.alpha_t = 3.0
else:
self.alpha_t = (epoch - 10.0) / (70.0-10.0) * 3.0
# unlabeled のラベルの更新
self.y_train_unlabeled_prediction = np.argmax(
self.model.predict(self.X_train_unlabeled), axis=-1,).reshape(-1, 1)
y_train_labeled_prediction = np.argmax(
self.model.predict(self.X_train_labeled), axis=-1).reshape(-1, 1)
# ground-truthとの一致率
self.unlabeled_accuracy.append(np.mean(
self.y_train_unlabeled_groundtruth == self.y_train_unlabeled_prediction))
self.labeled_accuracy.append(np.mean(
self.y_train_labeled == y_train_labeled_prediction))
print("labeled / unlabeled accuracy : ", self.labeled_accuracy[-1],
"/", self.unlabeled_accuracy[-1])
def on_train_end(self, logs):
y_true = np.ravel(self.y_test)
emb_model = Model(self.model.input, self.model.layers[-2].output)
embedding = emb_model.predict(self.X_test / 255.0)
proj = TSNE(n_components=2).fit_transform(embedding)
cmp = plt.get_cmap("tab10")
plt.figure()
for i in range(10):
select_flag = y_true == i
plt_latent = proj[select_flag, :]
plt.scatter(plt_latent[:,0], plt_latent[:,1], color=cmp(i), marker=".")
plt.savefig(f"result_pseudo/embedding_{self.n_labeled_sample:05}.png")
def train(n_labeled_data):
model = create_cnn()
pseudo = PseudoCallback(model, n_labeled_data, min(512, n_labeled_data))
model.compile("adam", loss=pseudo.loss_function, metrics=[pseudo.accuracy])
if not os.path.exists("result_pseudo"):
os.mkdir("result_pseudo")
hist = model.fit_generator(pseudo.train_generator(), steps_per_epoch=pseudo.train_steps_per_epoch,
validation_data=pseudo.test_generator(), callbacks=[pseudo],
validation_steps=pseudo.test_stepes_per_epoch, epochs=1).history
hist["labeled_accuracy"] = pseudo.labeled_accuracy
hist["unlabeled_accuracy"] = pseudo.unlabeled_accuracy
with open(f"result_pseudo/history_{n_labeled_data:05}.dat", "wb") as fp:
pickle.dump(hist, fp)
if __name__ == "__main__":
n_batches = [500, 1000, 5000, 10000]
for nb in n_batches:
print(nb, "Starts")
train(nb)
with zipfile.ZipFile("result_pseudo.zip", "w") as zip:
for f in glob.glob("result_pseudo/*"):
zip.write(f)