def test():
#test_x, test_y, class_all = getdata(args.mode)
test_handle = getgen(args.mode)
model = keras.models.load_model(os.path.join(args.save_path,
'baseline.h5'),
custom_objects={'LEAM': LEAM})
result = model.evaluate_generator(datagen(test_handle, opt),
steps=np.ceil(test_handle['x'].shape[0] /
args.batch_size))
print("Result on the held-out set: ", result)
train_encoder_batch = batch(train_encoder, maxlen, input_ctable,
train_batch_size, reverse)
train_decoder_batch = batch(train_decoder, maxlen, target_ctable,
train_batch_size)
train_target_batch = batch(train_target, maxlen, target_ctable,
train_batch_size)
val_encoder_batch = batch(val_encoder, maxlen, input_ctable,
val_batch_size, reverse)
val_decoder_batch = batch(val_decoder, maxlen, target_ctable,
val_batch_size)
val_target_batch = batch(val_target, maxlen, target_ctable,
val_batch_size)
train_loader = datagen(train_encoder_batch,
train_decoder_batch, train_target_batch)
val_loader = datagen(val_encoder_batch,
val_decoder_batch, val_target_batch)
model.fit_generator(train_loader,
steps_per_epoch=train_steps,
epochs=1, verbose=1,
validation_data=val_loader,
validation_steps=val_steps)
# On epoch end - decode a batch of misspelled tokens from the
# validation set to visualize speller performance.
nb_tokens = 5
input_tokens, target_tokens, decoded_tokens = decode_sequences(
val_encoder, val_target, input_ctable, target_ctable,
maxlen, reverse, encoder_model, decoder_model, nb_tokens,
def open_sesemi():
args = parse_args()
network = args.network
dataset = args.dataset
nb_labels = args.nb_labels
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
arg2var = {
'convnet': convnet,
'wrn': wrn,
'nin': nin,
'svhn': svhn,
'cifar10': cifar10,
'cifar100': cifar100,
}
# Experiment- and dataset-dependent parameters.
zca = True
hflip = True
epochs = 50
if dataset in {'svhn', 'cifar10'}:
if dataset == 'svhn':
zca = False
hflip = False
epochs = 30
nb_classes = 10
elif dataset == 'cifar100':
nb_classes = 100
else:
raise ValueError('`dataset` must be "svhn", "cifar10", "cifar100".')
super_dropout = 0.2
in_network_dropout = 0.0
if network == 'convnet' and dataset == 'svhn':
super_dropout = 0.5
in_network_dropout = 0.5
elif network == 'wrn' and dataset == 'svhn':
super_dropout = 0.5
# Prepare the dataset.
(x_train, y_train), (x_test, y_test) = arg2var[dataset].load_data()
x_test = global_contrast_normalize(x_test)
x_train = global_contrast_normalize(x_train)
if zca:
zca_whiten = zca_whitener(x_train)
x_train = zca_whiten(x_train)
x_test = zca_whiten(x_test)
x_test = x_test.reshape((len(x_test), 32, 32, 3))
x_train = x_train.reshape((len(x_train), 32, 32, 3))
if nb_labels in {50000, 73257}:
x_labeled = x_train
y_labeled = y_train
else:
labels_per_class = nb_labels // nb_classes
sample_inds = stratified_sample(y_train, labels_per_class)
x_labeled = x_train[sample_inds]
y_labeled = y_train[sample_inds]
y_labeled = to_categorical(y_labeled)
# Shared training parameters.
base_lr = 0.05
batch_size = 16
lr_decay_power = 0.5
input_shape = (32, 32, 3)
max_iter = (len(x_train) // batch_size) * epochs
# Compile the SESEMI model.
sesemi_model, inference_model = compile_sesemi(arg2var[network],
input_shape, nb_classes,
base_lr, in_network_dropout,
super_dropout)
print(sesemi_model.summary())
lr_poly_decay = LRScheduler(base_lr, max_iter, lr_decay_power)
evaluate = DenseEvaluator(inference_model, (x_test, y_test),
hflip,
oversample=True)
super_datagen = ImageDataGenerator(
width_shift_range=[-2, -1, 0, 1, 2],
height_shift_range=[-2, -1, 0, 1, 2],
horizontal_flip=hflip,
preprocessing_function=gaussian_noise,
fill_mode='reflect',
)
self_datagen = ImageDataGenerator(
width_shift_range=[-2, -1, 0, 1, 2],
height_shift_range=[-2, -1, 0, 1, 2],
horizontal_flip=False,
preprocessing_function=gaussian_noise,
fill_mode='reflect',
)
super_data = super_datagen.flow(x_labeled,
y_labeled,
shuffle=True,
batch_size=1,
seed=None)
self_data = self_datagen.flow(x_train,
shuffle=True,
batch_size=1,
seed=None)
train_data_loader = datagen(super_data, self_data, batch_size)
# Fit the SESEMI model on mini-batches with data augmentation.
print('Run configuration:')
print('network=%s,' % network, 'dataset=%s,' % dataset, \
'horizontal_flip=%s,' % hflip, 'ZCA=%s,' % zca, \
'nb_epochs=%d,' % epochs, 'batch_size=%d,' % batch_size, \
'nb_labels=%d,' % len(y_labeled), 'gpu_id=%s' % args.gpu_id)
sesemi_model.fit_generator(
train_data_loader,
epochs=epochs,
verbose=1,
steps_per_epoch=len(x_train) // batch_size,
callbacks=[lr_poly_decay, evaluate],
)
def main():
args = parse_args()
network = args.network
dataset = args.dataset
nb_labels = args.nb_labels
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
arg2var = {'convnet': convnet,
'wrn': wrn,
'resnet50v2':resnet50v2,
'svhn': svhn,
'cifar10': cifar10,
'cifar100': cifar100,}
# Dataset-specific parameters
hflip = True
zca = True
epochs = 10
if dataset in ['svhn', 'cifar10']:
if dataset == 'svhn':
hflip = False
zca = False
epochs = 30
nb_classes = 10
elif dataset == 'cifar100':
nb_classes = 100
else:
raise ValueError('`dataset` must be "svhn", "cifar10", "cifar100".')
(x_train, y_train), (x_test, y_test) = arg2var[dataset].load_data()
x_train = global_contrast_normalize(x_train)
x_test = global_contrast_normalize(x_test)
if zca:
zca_whiten = zca_whitener(x_train)
x_train = zca_whiten(x_train)
x_test = zca_whiten(x_test)
x_train = x_train.reshape((len(x_train), 32, 32, 3))
x_test = x_test.reshape((len(x_test), 32, 32, 3))
labels_per_class = nb_labels // nb_classes
if nb_labels == 73257:
labels_per_class = 1000000
sample_inds = stratified_sample(y_train, labels_per_class)
x_labeled = x_train[sample_inds]
y_labeled = y_train[sample_inds]
y_labeled = to_categorical(y_labeled)
# Training parameters
input_shape = (32, 32, 3)
batch_size = 32
base_lr = 0.05
lr_decay_power = 0.5
dropout_rate = 0.2
max_iter = (len(x_train) // batch_size) * epochs
sesemi_model, inference_model = open_sesemi(
arg2var[network], input_shape, nb_classes, base_lr, dropout_rate)
print(sesemi_model.summary())
super_datagen = ImageDataGenerator(
width_shift_range=3,
height_shift_range=3,
horizontal_flip=hflip,
preprocessing_function=gaussian_noise,
fill_mode='reflect',
)
self_datagen = ImageDataGenerator(
width_shift_range=3,
height_shift_range=3,
horizontal_flip=False,
preprocessing_function=gaussian_noise,
fill_mode='reflect',
)
super_data = super_datagen.flow(
x_labeled, y_labeled, shuffle=True, batch_size=1, seed=None)
self_data = self_datagen.flow(
x_train, shuffle=True, batch_size=1, seed=None)
train_data_loader = datagen(super_data, self_data, batch_size)
lr_poly_decay = LRScheduler(base_lr, max_iter, lr_decay_power)
evaluate = DenseEvaluator(inference_model, (x_test, y_test), hflip)
# Fit the SESEMI model on mini-batches with data augmentation
print('Run configuration:')
print('network=%s,' % network, 'dataset=%s,' % dataset, \
'horizontal_flip=%s,' % hflip, 'ZCA=%s,' % zca, \
'nb_epochs=%d,' % epochs, 'batch_size=%d,' % batch_size, \
'nb_labels=%d,' % len(x_labeled), 'gpu_id=%d' % args.gpu_id)
sesemi_model.fit_generator(train_data_loader,
epochs=epochs, verbose=1,
steps_per_epoch=len(x_train) // batch_size,
callbacks=[lr_poly_decay, evaluate],)
return
def train():
# Read word embeddings from VECTOR_DIR
with open(args.emb_path, 'rb') as f:
word_vector = np.array(pickle.load(f))
# f0 - Where you convert the text sequence into their respective embeddings.
sentence_inputs = Input(shape=(args.maxlen, ), dtype='int32')
print("sentence_inputs, each of size max_len: ",
K.int_shape(sentence_inputs))
sentence_embeddings = Embedding(args.token_size + 1,
args.embedding_size,
mask_zero=False,
weights=[word_vector],
trainable=False)(sentence_inputs)
print("sentence_embeddings, each of shape (max_len, embedding_size): ",
K.int_shape(sentence_embeddings))
# Calculates the attention values \beta and then the sentence encoder - z.
#sentence_attn = AttentionLayer()(sentence_embeddings)
#sentence_encoder = Model(sentence_inputs,sentence_attn)
# Obtain the class embedding C (K X P) = (20 X 300)
class_all_inputs = Input((args.class_num, ), dtype='int32')
class_all_embeddings = Embedding(args.class_num,
args.embedding_size,
mask_zero=False)(class_all_inputs)
#token_inputs = Input((args.sentence_size, args.maxlen,), dtype='int32')
#label_inputs = Input((args.class_num,), dtype='int32')
#token_encoder = TimeDistributed(sentence_encoder)(token_inputs)
# f1 layer which outputs 'z' (average of the word embeddings weighted by the attentions score).
#doc_leam = LEAM()([token_encoder, label_inputs, token_inputs, class_all_embeddings])
doc_leam = LEAM()([sentence_embeddings, class_all_embeddings])
# f2 layer (output) where you get the class probability after taking the sentence embedding - z (doc_leam here)
output = Dense(args.class_num, activation='softmax')(doc_leam)
#model = Model(input=[token_inputs,label_inputs,class_all_inputs], output=[output])
model = Model(input=[sentence_inputs, class_all_inputs], output=[output])
#plot_model(model, to_file=os.path.join(args.save_path, 'model_plot.png'), show_shapes=True, show_layer_names=True)
optimizer = keras.optimizers.Adam(lr=args.lr)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['acc'])
print(model.summary())
#train_x, train_y, test_x, test_y, class_all = getdata(args.mode)
train_handle, valid_handle = getgen(args.mode)
history = model.fit_generator(
datagen(train_handle, opt),
epochs=args.epochs,
steps_per_epoch=np.ceil(train_handle['x'].shape[0] / args.batch_size),
validation_data=datagen(valid_handle, opt),
validation_steps=np.ceil(valid_handle['x'].shape[0] / args.batch_size))
#Save the cross_validation results
if args.fold != '':
args.save_path = os.path.join(args.save_path,
'e_{}_lr{}'.format(args.epochs, args.lr))
args.save_path = os.path.join(args.save_path,
'fold{}'.format(args.fold))
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
with open(os.path.join(args.save_path, 'accuracy.pkl'), 'wb') as f:
pickle.dump(history.history['acc'], f)
model.save(os.path.join(args.save_path, 'baseline.h5'))
def main():
#load the model
batch_size = 32
model = keras.models.load_model('cifar10-1000-1.h5')
print(model.summary())
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = global_contrast_normalize(x_train)
x_test = global_contrast_normalize(x_test)
print(x_train.shape)
x_train = x_train.reshape((len(x_train), 32, 32, 3))
x_test = x_test.reshape((len(x_test), 32, 32, 3))
print(x_test.shape)
#print(x_test[1])
nb_classes = 10
nb_labels = 1000
labels_per_class = nb_labels // nb_classes
if nb_labels == 73257:
labels_per_class = 1000000
sample_inds = stratified_sample(y_test, labels_per_class)
x_labeled = x_test[sample_inds]
y_labeled = y_test[sample_inds]
y_labeled = to_categorical(y_labeled)
#print(x_labeled)
#print(y_labeled)
print(x_labeled.shape)
print(y_labeled.shape)
super_datagen = ImageDataGenerator(
width_shift_range=3,
height_shift_range=3,
#horizontal_flip=hflip,
preprocessing_function=gaussian_noise,
fill_mode='reflect',
)
self_datagen = ImageDataGenerator(
width_shift_range=3,
height_shift_range=3,
horizontal_flip=False,
preprocessing_function=gaussian_noise,
fill_mode='reflect',
)
super_data = super_datagen.flow(
x_labeled,y_labeled,shuffle=True, batch_size=1, seed=None)
self_data = self_datagen.flow(
x_test, shuffle=True, batch_size=1, seed=None)
#super_data = x_labeled
#self_data = x_test
train_data_loader = datagen(super_data, self_data, batch_size)
print('self - data')
print(self_data)
print('supervised - data')
print(super_data)
print('train_data_loader')
print(train_data_loader)
print(len(model.layers))
print(model.layers)
layer_name = 'convnet_trunk'
layer_outputs = [model.get_layer(layer_name).get_output_at(2)]
# extract the ouputs of the top 6 layers
activation_model = Model(inputs=model.input,outputs=layer_outputs)
steps = len(x_test)/batch_size
activations = activation_model.predict_generator(train_data_loader,steps=steps,verbose=0)
print(activations)
print(activations.shape)
k=6666
first_layer_activation = activations[k]
print(first_layer_activation.shape)
print(first_layer_activation)
#plt.imshow(first_layer_activation)
#plt.show()
#imsave('first_layer_activation.jpg',first_layer_activation)
#plt.figure(1)
plt.matshow(first_layer_activation[:,:,0])
plt.show()
#imsave('first_layer_activation'+str(k)+'[:,:,0].jpg',first_layer_activation[:,:,0])
layer_name_2 = 'self_clf'
layer_outputs_2 = [model.get_layer(layer_name_2).get_output_at(0)]
representation_model = Model(inputs=model.input,outputs=layer_outputs_2)
final_representation = np.array(representation_model.predict_generator(train_data_loader,steps = steps,verbose = 0))
print('The shape of final_representation')
print(final_representation.shape)
#print(final_representation)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
representation = scaler.fit_transform(final_representation)
print(representation.shape)
plot_representation = representation[:,0:2] #取其中的前两维
print(plot_representation.shape)
plot_Y = plot_representation
plt.scatter(plot_Y[:, 0], plot_Y[:, 1], c = "green", marker='o', label='two')
#plt.show()
plt.savefig('The_scatter_of_first_2_columns.jpg',dpi = None)
'''kmeans = KMeans(n_clusters=10, init='k-means++')
kmeans.fit(plot_Y)
print(kmeans.inertia_)
centroids = kmeans.cluster_centers_
print(centroids)
print(centroids.shape)'''
#plt.scatter(centroids[:, 0], centroids[:, 1],
#marker='x', s=169, linewidths=3,
#color='w', zorder=10)
#plt.savefig('centers-cifar10-sesemi-features-1.jpg')
'''tsne = manifold.TSNE(n_components=2, init='pca', random_state=501)
X_tsne = tsne.fit_transform(X)
print("Org data dimension is {}.Embedded data dimension is {}".format(X.shape[-1], X_tsne.shape[-1]))
x_min, x_max = X_tsne.min(0), X_tsne.max(0)
X_norm = (X_tsne - x_min) / (x_max - x_min) # 归一化
plt.figure(figsize=(8, 8))
for i in range(X_norm.shape[0]):
plt.text(X_norm[i, 0], X_norm[i, 1], str(y[i]), color=plt.cm.Set1(y[i]),
fontdict={'weight': 'bold', 'size': 9})
plt.xticks([])
plt.yticks([])
plt.show()
plt.savefig('t-SNE-cifar10.jpg')'''
'''superd = next(super_data)
selfd = next(self_data)
print('super - d')
print(superd)
print(superd.shape())
print('self - d')
print(selfd)
print('Try to extract the representation of the sesemi model')
fig = plt.figure(figsize=(14,10))
for n in range(1,29):
fig.add_subplot(4, 7, n)
img_tensor = [self_data[n],super_data[n]]
#img_tensor = np.expand_dims(img_tensor, axis=0)
#img_tensor /= 255.
print('image tensor to be shown')
print(img_tensor)
print(len(img_tensor))
#plt.imshow(self_data)
#plt.show()
#print(img_tensor2.shape)
#img = expand_dims(img, axis=0)i
#img = preprocess_input(img)
img_tensor = list(itertools.chain.from_iterable(img_tensor))
print(img_tensor.shape())
img_tensor.flatten()
print(img_tensor)
feature_maps = model.predict(img_tensor)
print(feature_maps)
draw_features(feature_maps)
plt.axis('off')
plt.show()
return
print('Try to visualize the representation!')'''
return
