def sample(epoch, header, num_chars):
with open(os.path.join(DATA_DIR, 'char_to_idx.json'),
encoding="utf8") as f:
char_to_idx = json.load(f)
idx_to_char = {i: ch for (ch, i) in char_to_idx.items()}
vocab_size = len(char_to_idx)
model = build_sample_model(vocab_size)
load_weights(epoch, model)
model.save(os.path.join(MODEL_DIR, 'model.{}.h5'.format(epoch)))
sampled = [char_to_idx[c] for c in header]
print(sampled)
for i in range(num_chars):
batch = np.zeros((1, 1))
if sampled:
batch[0, 0] = sampled[-1]
else:
batch[0, 0] = np.random.randint(vocab_size)
result = model.predict_on_batch(batch).ravel()
sample = np.random.choice(range(vocab_size), p=result)
sampled.append(sample)
return ''.join(idx_to_char[c] for c in sampled)
def sample(epoch, header, num_chars):
with open(os.path.join(BASE_DIR, MODEL_DIR, 'char_to_idx.json'), 'r') as f:
char_to_idx = json.load(f)
idx_to_char = {i: ch for (ch, i) in list(char_to_idx.items())}
vocab_size = len(char_to_idx)
model = build_sample_model(vocab_size)
load_weights(BASE_DIR, epoch, model)
model.save(os.path.join(BASE_DIR, MODEL_DIR, 'model.{}.h5'.format(epoch)))
sampled = [char_to_idx[c] for c in header]
for c in header[:-1]:
batch = np.zeros((1, 1))
batch[0, 0] = char_to_idx[c]
model.predict_on_batch(batch)
for i in range(num_chars):
batch = np.zeros((1, 1))
if sampled:
batch[0, 0] = sampled[-1]
else:
batch[0, 0] = np.random.randint(vocab_size)
result = model.predict_on_batch(batch).ravel()
sampleWord = np.random.choice(list(range(vocab_size)), p=result)
sampled.append(sampleWord)
return ''.join(idx_to_char[c] for c in sampled)
def train(text, epochs=100, save_freq=10, resume=False):
global model, idx_to_char, vocab_size
if resume:
print("Attempting to resume last training...")
model_dir = Path(MODEL_DIR)
c2ifile = model_dir.joinpath('char_to_idx.json')
with c2ifile.open('r') as f:
char_to_idx = json.load(f)
checkpoints = list(model_dir.glob('weights.*.h5'))
if not checkpoints:
raise ValueError("No checkpoints found to resume from")
resume_epoch = max(int(p.name.split('.')[1]) for p in checkpoints)
print("Resuming from epoch", resume_epoch)
else:
resume_epoch = 0
char_to_idx = {ch: i for (i, ch) in enumerate(sorted(list(set(text))))}
with open(os.path.join(MODEL_DIR, 'char_to_idx.json'), 'w') as f:
json.dump(char_to_idx, f)
vocab_size = len(char_to_idx)
idx_to_char = {i: ch for (ch, i) in list(char_to_idx.items())}
model = build_model(BATCH_SIZE, SEQ_LENGTH, vocab_size)
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
if resume:
load_weights(resume_epoch, model)
T = np.asarray([char_to_idx[c] for c in text], dtype=np.int32)
log = TrainLogger('training_log.csv', resume_epoch)
start = timer()
for epoch in range(resume_epoch, epochs):
elapsed = timer() - start
start = timer()
print(
'\nEpoch {}/{}, previous epoch took %0.3fs or %0.3fms/step'.format(
epoch + 1, epochs) % (elapsed, 1000.0 * elapsed /
(T.shape[0] / BATCH_SIZE / SEQ_LENGTH)))
losses, accs = [], []
for i, (X, Y) in enumerate(read_batches(T, vocab_size)):
loss, acc = model.train_on_batch(X, Y)
print('Batch {}: loss = {:.4f}, acc = {:.5f}\r'.format(
i + 1, loss, acc),
end=" ")
losses.append(loss)
accs.append(acc)
log.add_entry(np.average(losses), np.average(accs))
print()
print(sample(512))
if (epoch + 1) % save_freq == 0:
save_weights(epoch + 1, model)
print('Saved checkpoint to', 'weights.{}.h5'.format(epoch + 1))
def sample(epoch, header, num_lines):
with open(os.path.join(MODEL_DIR, 'char_to_idx.json'), 'r') as f:
char_to_idx = json.load(f)
idx_to_char = {i: ch for (ch, i) in list(char_to_idx.items())}
vocab_size = len(char_to_idx)
newline_symbol = char_to_idx['\n']
model = build_sample_model(vocab_size)
load_weights(epoch, model)
sampled = [char_to_idx[c] for c in header]
for c in header[:-1]:
batch = np.zeros((1, 1))
batch[0, 0] = char_to_idx[c]
model.predict_on_batch(batch)
for i in range(num_lines):
sample = -1
while not sample == newline_symbol:
batch = np.zeros((1, 1))
if sampled:
batch[0, 0] = sampled[-1]
else:
batch[0, 0] = np.random.randint(vocab_size)
result = model.predict_on_batch(batch).ravel()
sample = np.random.choice(list(range(vocab_size)), p=result)
sampled.append(sample)
return ''.join(idx_to_char[c] for c in sampled).strip()
def run(args, meta, model, callbacks, exp, id_=100, data=None):
train_ds, val_ds, train_len, validation_len = prerun(args, meta, data)
init_weights_path = Path(args["run_dir"], 'initial_model_weights.h5')
if init_weights_path.exists():
model.load_weights(str(init_weights_path))
if not init_weights_path.exists():
hist = model.fit(train_ds, epochs=1, steps_per_epoch=1)
model.save_weights(str(init_weights_path))
for i, cb in enumerate(callbacks):
if type(cb) == my_callbacks.ValidationMonitor:
cb.set(val_ds, validation_len, id_, exp)
if type(cb) == my_callbacks.ImageLogger:
cb.set_dataset(train_ds, len(args["channels"]))
hist = model.fit(
train_ds,
epochs=args["epochs"],
steps_per_epoch=int(np.ceil(train_len / args["batch_size"])),
callbacks=callbacks,
validation_data=val_ds,
validation_steps=int(np.ceil(validation_len / args["batch_size"])))
return hist
def load(self, model_dir, load_optimizer=True, lr_scheduler=None):
if not os.path.exists(model_dir):
print(f'file: {model_dir} not found')
load_weights(self.yolov2, self.opt.github_model)
# self.init_weights(self.yolov2)
return float('inf'), 0, 0
else:
state_dict = torch.load(model_dir)
if 'loss' in state_dict:
loss = state_dict['loss']
else:
loss = float('inf')
if 'epoch' in state_dict:
epoch = state_dict['epoch']
else:
epoch = 0
if 'total_steps' in state_dict:
steps = state_dict['total_steps']
else:
steps = 0
if 'model' in state_dict:
print(f'loading pretrained model: {model_dir}')
self.yolov2.load_state_dict(state_dict['model'])
if load_optimizer and 'optimizer' in state_dict:
self.optimizer.load_state_dict(state_dict['optimizer'])
if lr_scheduler is not None and 'lr_scheduler' in state_dict:
lr_scheduler.load_state_dict(state_dict['lr_scheduler'])
return loss, epoch, steps
def sample(epoch, header, num_chars):
# get the character dictionary and build the character-index dict
with open(os.path.join(DATA_DIR, 'char_to_idx2.json')) as f:
char_to_idx2 = json.load(f)
idx_to_char2 = {i: ch for (ch, i) in char_to_idx2.items()}
vocab_size = len(char_to_idx2)
model = build_model(1, 1, vocab_size)
load_weights(epoch, model)
# header implementation if needed
sampled = [char_to_idx2[c] for c in header]
for c in header[:-1]:
batch = np.zeros((1, 1))
batch[0, 0] = char_to_idx2[c]
model.predict_on_batch(batch)
# sample until parameter
for i in range(num_chars):
batch = np.zeros((1, 1))
if sampled:
batch[0, 0] = sampled[-1]
else:
batch[0, 0] = np.random.randint(vocab_size)
result = model.predict_on_batch(batch).ravel()
sample = np.random.choice(range(vocab_size), p=result)
sampled.append(sample)
print ''.join(idx_to_char2[c] for c in sampled)
def init_model(epoch):
with open(os.path.join(MODEL_DIR, 'char_to_idx.json'), 'r') as f:
char_to_idx = json.load(f)
idx_to_char = { i: ch for (ch, i) in list(char_to_idx.items()) }
vocab_size = len(char_to_idx)
model = build_sample_model(vocab_size)
load_weights(epoch, model)
return model, vocab_size, idx_to_char
def train(text, epochs=100, save_freq=10, resume=False):
if resume:
print("Attempting to resume last training...")
model_dir = Path(MODEL_DIR)
c2ifile = model_dir.joinpath('char_to_idx.json')
with c2ifile.open('r') as f:
char_to_idx = json.load(f)
checkpoints = list(model_dir.glob('weights.*.h5'))
if not checkpoints:
raise ValueError("No checkpoints found to resume from")
resume_epoch = max(int(p.name.split('.')[1]) for p in checkpoints)
print("Resuming from epoch", resume_epoch)
else:
resume_epoch = 0
char_to_idx = {ch: i for (i, ch) in enumerate(sorted(list(set(text))))}
with open(os.path.join(MODEL_DIR, 'char_to_idx.json'), 'w') as f:
json.dump(char_to_idx, f)
vocab_size = len(char_to_idx)
model = build_model(BATCH_SIZE, SEQ_LENGTH, vocab_size)
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam', metrics=['accuracy'])
if resume:
load_weights(resume_epoch, model)
T = np.asarray([char_to_idx[c] for c in text], dtype=np.int32)
log = TrainLogger('training_log.csv', resume_epoch)
for epoch in range(resume_epoch, epochs):
print('\nEpoch {}/{}'.format(epoch + 1, epochs))
losses, accs = [], []
for i, (X, Y) in enumerate(read_batches(T, vocab_size)):
loss, acc = model.train_on_batch(X, Y)
print('Batch {}: loss = {:.4f}, acc = {:.5f}'.format(i + 1, loss, acc))
losses.append(loss)
accs.append(acc)
log.add_entry(np.average(losses), np.average(accs))
if (epoch + 1) % save_freq == 0:
save_weights(epoch + 1, model)
sample_model = build_sample_model(vocab_size)
load_weights(epoch + 1, sample_model)
sample_model.save('{}/SavedModel-{}'.format(MODEL_DIR, epoch + 1), save_format='tf', overwrite=True)
tfjs.converters.save_keras_model(sample_model, '{}/SavedModel-{}-tfjs'.format(MODEL_DIR, epoch + 1))
print('Saved checkpoint to', 'weights.{}.h5'.format(epoch + 1))
def train(self):
# self.writer.add_graph(self.trainer.yolov2.cpu(),
# input_to_model=torch.rand(opt.batch_size, 3, opt.img_h, opt.img_w, device='cpu'))
self.trainer.yolov2.train()
loss_tmp = float('inf')
if os.path.exists(opt.saved_model_path):
self.logger.info(f'Use pretrained model: {opt.saved_model_path}')
self.trainer.use_pretrain(opt.saved_model_path)
loss_tmp = self.trainer.last_loss
start_epoch = self.trainer.epoch_num
total_steps = self.trainer.total_steps
else:
start_epoch = 0
total_steps = 0
self.logger.info('Train from stratch ...')
pretrained_ckpt_path = '/home/dk/jyl/V2/COCO/model/ckpt/only_params_trained_yolo_coco'
load_weights(self.trainer.yolov2,
pretrained_ckpt_path,
reinit_last=False)
for epoch in range(start_epoch, opt.epochs):
for i, (imgs, targets) in tqdm(enumerate(self.VocTrainDataLoader)):
imgs, tragets = imgs.to(opt.device), targets.to(opt.device)
self.trainer.train_step(imgs, tragets, epoch + 1)
total_steps += 1
mean_loss = self.trainer.loss_meter.mean
loss_dict = self.trainer.loss_dict
self.add_train_summary(loss_dict, total_steps)
if total_steps % opt.display_step == 0:
message = f'Epoch[{epoch: 03}] Step[{(epoch * self.train_data_length + i + 1): 06}]] \n' \
f'mean_loss : {mean_loss:.3f} \n' \
f'xy_loss : {loss_dict["dxdy_loss"]:.3f} \n' \
f'wh_loss : {loss_dict["twth_loss"]:.3f} \n' \
f'conf_loss : {loss_dict["conf_loss"]:.3f} \n' \
f'class_loss: {loss_dict["class_loss"]:.3f} \n' \
f'obj_loss : {loss_dict["obj_loss"]:.3f} \n' \
f'noobj_loss: {loss_dict["noobj_loss"]:.3f} \n' \
f'total_loss: {loss_dict["total_loss"]:.3f} \n' \
f'current learning rate: {self.scheduler.get_lr()}'
self.logger.info(message)
if total_steps % opt.eval_step == 0:
self.eval(epoch, i + 1)
if mean_loss < loss_tmp:
loss_tmp = mean_loss
self.trainer.save(
epoch, total_steps, mean_loss, opt.model_save_dir +
f'/model_best_{opt.optimizer_type}_2.pkl')
if total_steps % opt.save_step == 0:
self.trainer.save(
epoch, total_steps, mean_loss,
opt.model_save_dir + f'/model_every_2.pkl')
self.scheduler.step()
def test(lowlight_test_images_path):
input_img = Input(shape=(512, 512, 3))
conv1 = Conv2D(32, (3, 3), strides=(1,1), activation='relu', padding='same')(input_img)
conv2 = Conv2D(32, (3, 3), strides=(1,1), activation='relu', padding='same')(conv1)
conv3 = Conv2D(32, (3, 3), strides=(1,1), activation='relu', padding='same')(conv2)
conv4 = Conv2D(32, (3, 3), strides=(1,1), activation='relu', padding='same')(conv3)
int_con1 = Concatenate(axis=-1)([conv4, conv3])
conv5 = Conv2D(32, (3, 3), strides=(1,1), activation='relu', padding='same')(int_con1)
int_con2 = Concatenate(axis=-1)([conv5, conv2])
conv6 = Conv2D(32, (3, 3), strides=(1,1), activation='relu', padding='same')(int_con2)
int_con3 = Concatenate(axis=-1)([conv6, conv1])
x_r = Conv2D(24, (3,3), strides=(1,1), activation='tanh', padding='same')(int_con3)
model = Model(inputs=input_img, outputs = x_r)
model.load_weights("weights/ep_2_it_200.h5")
### load image ###
for test_file in glob.glob(lowlight_test_images_path + "*.jpg"):
data_lowlight_path = test_file
original_img = Image.open(data_lowlight_path)
original_size = (np.array(original_img).shape[1], np.array(original_img).shape[0])
original_img = original_img.resize((512,512), Image.ANTIALIAS)
original_img = (np.asarray(original_img)/255.0)
img_lowlight = Image.open(data_lowlight_path)
img_lowlight = img_lowlight.resize((512,512), Image.ANTIALIAS)
img_lowlight = (np.asarray(img_lowlight)/255.0)
img_lowlight = np.expand_dims(img_lowlight, 0)
# img_lowlight = K.constant(img_lowlight)
### process image ###
A = model.predict(img_lowlight)
r1, r2, r3, r4, r5, r6, r7, r8 = A[:,:,:,:3], A[:,:,:,3:6], A[:,:,:,6:9], A[:,:,:,9:12], A[:,:,:,12:15], A[:,:,:,15:18], A[:,:,:,18:21], A[:,:,:,21:24]
x = original_img + r1 * (K.pow(original_img,2)-original_img)
x = x + r2 * (K.pow(x,2)-x)
x = x + r3 * (K.pow(x,2)-x)
enhanced_image_1 = x + r4*(K.pow(x,2)-x)
x = enhanced_image_1 + r5*(K.pow(enhanced_image_1,2)-enhanced_image_1)
x = x + r6*(K.pow(x,2)-x)
x = x + r7*(K.pow(x,2)-x)
enhance_image = x + r8*(K.pow(x,2)-x)
enhance_image = tf.cast((enhance_image[0,:,:,:] * 255), dtype=np.uint8)
enhance_image = Image.fromarray(enhance_image.numpy())
enhance_image = enhance_image.resize(original_size, Image.ANTIALIAS)
enhance_image.save(test_file.replace(".jpg", "_rs.jpg"))
def simple_model(pretrained_weights=None, input_size=(256, 256, 1)):
inputs = tf.keras.Input(input_size)
conv1 = tf.keras.layers.Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
inputs) # 256
conv1 = tf.keras.layers.Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
conv1 = tf.keras.layers.Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
conv1 = tf.keras.layers.Conv2D(16,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
conv1 = tf.keras.layers.Conv2D(8,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
conv1 = tf.keras.layers.Conv2D(1,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
model = tf.keras.models.Model(inputs=inputs, outputs=conv1)
model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-4),
loss="mean_absolute_error",
metrics=['accuracy'],
run_eagerly=True)
model.summary()
if (pretrained_weights):
model.load_weights(pretrained_weights)
return model
def plot_latent_space(weightsfile):
print('building model')
layers = model.build_model()
batch_size = 128
decoder_func = theano_funcs.create_decoder_func(layers)
print('loading weights from %s' % (weightsfile))
model.load_weights([
layers['l_decoder_out'],
layers['l_discriminator_out'],
], weightsfile)
# regularly-spaced grid of points sampled from p(z)
Z = np.mgrid[2:-2.2:-0.2, -2:2.2:0.2].reshape(2, -1).T[:, ::-1].astype(np.float32)
reconstructions = []
print('generating samples')
for idx in get_batch_idx(Z.shape[0], batch_size):
Z_batch = Z[idx]
X_batch = decoder_func(Z_batch)
reconstructions.append(X_batch)
X = np.vstack(reconstructions)
X = X.reshape(X.shape[0], 28, 28)
fig = plt.figure(1, (12., 12.))
ax1 = plt.axes(frameon=False)
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
plt.title('samples generated from latent space of autoencoder')
grid = ImageGrid(
fig, 111, nrows_ncols=(21, 21),
share_all=True)
print('plotting latent space')
for i, x in enumerate(X):
img = (x * 255).astype(np.uint8)
grid[i].imshow(img, cmap='Greys_r')
grid[i].get_xaxis().set_visible(False)
grid[i].get_yaxis().set_visible(False)
grid[i].set_frame_on(False)
plt.savefig('latent_train_val.png', bbox_inches='tight')
def plot_latent_space(weightsfile):
print('building model')
layers = model.build_model()
batch_size = 128
decoder_func = theano_funcs.create_decoder_func(layers)
print('loading weights from %s' % (weightsfile))
model.load_weights([
layers['l_decoder_out'],
layers['l_discriminator_out'],
], weightsfile)
# regularly-spaced grid of points sampled from p(z)
Z = np.mgrid[2:-2.2:-0.2,
-2:2.2:0.2].reshape(2, -1).T[:, ::-1].astype(np.float32)
reconstructions = []
print('generating samples')
for idx in get_batch_idx(Z.shape[0], batch_size):
Z_batch = Z[idx]
X_batch = decoder_func(Z_batch)
reconstructions.append(X_batch)
X = np.vstack(reconstructions)
X = X.reshape(X.shape[0], 28, 28)
fig = plt.figure(1, (12., 12.))
ax1 = plt.axes(frameon=False)
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
plt.title('samples generated from latent space of autoencoder')
grid = ImageGrid(fig, 111, nrows_ncols=(21, 21), share_all=True)
print('plotting latent space')
for i, x in enumerate(X):
img = (x * 255).astype(np.uint8)
grid[i].imshow(img, cmap='Greys_r')
grid[i].get_xaxis().set_visible(False)
grid[i].get_yaxis().set_visible(False)
grid[i].set_frame_on(False)
plt.savefig('latent_train_val.png', bbox_inches='tight')
def __init__(self, iter_no=None):
weight_joint, weight_joint_group, weight_full = 1. / (3 * 17), 1. / (
3 * 5), 1. / (3 * 1)
self.weight_vec = np.array([1. / 51] * 17 + [1. / 15] * 5 + [1. / 3])
self.config = tf.ConfigProto()
self.config.gpu_options.allow_growth = True
self.session = tf.InteractiveSession(config=self.config)
self.session.run(tf.global_variables_initializer())
if iter_no is not None:
self.iter_no = iter_no
print('Trying to load iter no:', self.iter_no)
M.load_weights(self.iter_no, self.session, H.best_weights_path,
H.best_weights_tag)
else:
self.iter_no = H.best_iter_no
print('Trying to load iter no:', self.iter_no)
M.load_weights(self.iter_no, self.session, H.best_weights_path,
H.best_weights_tag)
def plot_autoencoder(weightsfile):
print('building model')
layers = model.build_model()
batch_size = 128
print('compiling theano function')
encoder_func = theano_funcs.create_encoder_func(layers)
print('loading weights from %s' % (weightsfile))
model.load_weights([
layers['l_decoder_out'],
layers['l_discriminator_out'],
], weightsfile)
print('loading data')
X_train, y_train, X_test, y_test = utils.load_mnist()
train_datapoints = []
print('transforming training data')
for train_idx in get_batch_idx(X_train.shape[0], batch_size):
X_train_batch = X_train[train_idx]
train_batch_codes = encoder_func(X_train_batch)
train_datapoints.append(train_batch_codes)
test_datapoints = []
print('transforming test data')
for test_idx in get_batch_idx(X_test.shape[0], batch_size):
X_test_batch = X_test[test_idx]
test_batch_codes = encoder_func(X_test_batch)
test_datapoints.append(test_batch_codes)
Z_train = np.vstack(train_datapoints)
Z_test = np.vstack(test_datapoints)
plot(Z_train,
y_train,
Z_test,
y_test,
filename='adversarial_train_val.png',
title='projected onto latent space of autoencoder')
def sample(epoch, num_chars):
with open(os.path.join(DATA_DIR, 'char_to_idxi1522879818.json'),
'rb') as f:
print(f)
char_to_idx = json.load(f)
idx_to_char = {i: ch for (ch, i) in char_to_idx.items()}
vocab_size = len(char_to_idx)
model = build_sample_model(vocab_size)
load_weights(epoch, model)
model.save(
os.path.join(MODEL_DIR, 'model1522879818weights.{}.h5'.format(epoch)))
li = " "
treat = " "
file = open(os.path.join(DATA_DIR, "result.csv"), "w")
with open(os.path.join(DATA_DIR, args.seed), 'r') as myfile:
for line in myfile:
#li = line.rstrip()
vals = line.split("#")
li = vals[1]
treat = vals[0] + "#"
sampled = [char_to_idx[c] for c in treat]
for c in treat[:-1]:
batch = np.zeros((1, 1))
batch[0, 0] = char_to_idx[c]
model.predict_on_batch(batch)
for i in range(num_chars):
batch = np.zeros((1, 1))
if sampled:
batch[0, 0] = sampled[-1]
else:
batch[0, 0] = np.random.randint(vocab_size)
result = model.predict_on_batch(batch).ravel()
sample = np.random.choice(range(vocab_size), p=result)
sampled.append(sample)
#print(idx_to_char[c] for c in sampled)
generated = ''.join(idx_to_char[c] for c in sampled)
values = generated.split("#")
print(generated, end='$')
file.write(values[0] + ", " + values[1] + ", " + li + "\n")
def plot_autoencoder(weightsfile):
print('building model')
layers = model.build_model()
batch_size = 128
print('compiling theano function')
encoder_func = theano_funcs.create_encoder_func(layers)
print('loading weights from %s' % (weightsfile))
model.load_weights([
layers['l_decoder_out'],
layers['l_discriminator_out'],
], weightsfile)
print('loading data')
X_train, y_train, X_test, y_test = utils.load_mnist()
train_datapoints = []
print('transforming training data')
for train_idx in get_batch_idx(X_train.shape[0], batch_size):
X_train_batch = X_train[train_idx]
train_batch_codes = encoder_func(X_train_batch)
train_datapoints.append(train_batch_codes)
test_datapoints = []
print('transforming test data')
for test_idx in get_batch_idx(X_test.shape[0], batch_size):
X_test_batch = X_test[test_idx]
test_batch_codes = encoder_func(X_test_batch)
test_datapoints.append(test_batch_codes)
Z_train = np.vstack(train_datapoints)
Z_test = np.vstack(test_datapoints)
plot(Z_train, y_train, Z_test, y_test,
filename='adversarial_train_val.png',
title='projected onto latent space of autoencoder')
def captcha_reco():
pic = flask.request.files('imagefile', '')
x = prep_data.get_data(pic)
model_arc = m.load_cnn.save_model_arc()
cnn_model = keras.models.model_from_json(model_arc)
directory = sys.path.append(
os.path.join(os.path.dirname(__file__), '..', 'model'))
cnn_model_final = load_weights(cnn_model, directory)
result = cnn_model_final.predict({'input': x})
out = [
np.argmax(result['out'][0]),
np.argmax(result['out'][1]),
np.argmax(result['out'][2]),
np.argmax(result['out'][3]),
np.argmax(result['out'][4])
]
r = [prep_data.asc_chr(i) for i in out]
result_json = json.dump(dict(r), ensure_ascii=False)
return flask.Response(result_json, mimetype='application/json')
def loadModel(name):
model = model_from_json(open('./model/'+ name +'_model.json').read())
model.load_weights('./model/best_validation_'+ name +'_model_weights.h5')
return model
# Create a function that saves the model's weights
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath = model_name,
save_weights_only=True,
verbose=0, save_freq="epoch")
# model.load_weights(model_name)
model.fit(X_train, Y_train,
batch_size = batch_size,
epochs = epochs,
validation_data= (X_valid,Y_valid),
callbacks = [cp_callback])
# Step 6: Evaluation
mae, mse = model.evaluate(X_valid, Y_valid, verbose = 2)
print("Evaluation, MAE: {:2.4f}, MSE: {:2.4f}".format(mae, mse))
elif sys.argv[1] == "predict":
# Step 3: Loads the weights
model.load_weights(model_name)
my_model = tf.keras.Sequential([model])
# Step 4: Get Normalization values
stats = pd.read_csv("data/wine_stats.csv", sep = ',', header = 0)
# Step 5: Prepare the input AND predict
input = sys.argv[2].split(",")
input = np.array([float(x) for x in input])
input = loader.normalize(input, stats).values
print(input)
preds = my_model.predict(np.array([input]))
print(preds[0])
if distributional:
if alpha > 0:
modelbest = file0 + "Sen2LCZ_" + str(batch_size) + "_lr_" + str(
lrate) + "_urban_d" + str(alpha) + "_weights_best.hdf5"
else:
modelbest = file0 + "Sen2LCZ_" + str(batch_size) + "_lr_" + str(
lrate) + "_urban_d" + "_weights_best.hdf5"
else:
modelbest = file0 + "Sen2LCZ_" + str(batch_size) + "_lr_" + str(
lrate) + "_urban" + "_weights_best.hdf5"
else:
modelbest = file0 + "Sen2LCZ_" + str(batch_size) + "_lr_" + str(
lrate) + "_weights_best.hdf5"
'load saved best model'
model.load_weights(modelbest, by_name=False)
# 4. test phase
y_pre = model.predict(x_tst, batch_size=batch_size)
y_pre = y_pre.argmax(axis=-1) + 1
y_testV = y_tst.argmax(axis=-1) + 1
# Add training data as well
#y_pre_trn = model.predict(x_trn, batch_size = batch_size)
#y_pre_trn = y_pre_trn.argmax(axis=-1)+1
#y_trnV = y_trn.argmax(axis=-1)+1
#y_pre = np.hstack((y_pre, y_pre_trn))
#y_testV = np.hstack((y_testV, y_trnV))
labels = [
def main():
"""main function
Main function... (what do you expect me to say...)
Args:
- none
Returns:
- none
"""
# Main function for evaluate
parser = argparse.ArgumentParser(
description="A testing framework for semantic segmentation.")
parser.add_argument(
"--net",
required=True,
default="unet",
type=str,
help=
"(str) The type of net work which is either unet, deeplab or custom.")
parser.add_argument("--epochs", required=False, default=500, type=int)
parser.add_argument("--batch_size", required=False, default=16, type=int)
parser.add_argument("--gpu_id",
required=False,
default="0",
type=str,
help="(str) The id of the gpu used when training.")
parser.add_argument("--img_size",
required=False,
default=192,
type=int,
help="(int) The size of input image")
parser.add_argument(
"--load_weights",
required=False,
default=False,
type=bool,
help="(bool) Use old weights or not (named net_imgSize.h5)")
# Parse argument
args = parser.parse_args()
net_type = args.net
epochs = args.epochs
batch_size = args.batch_size
gpu_number = args.gpu_id
img_size = args.img_size
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_number
# Argument check
if not (net_type in {"unet", "deeplab", "custom"}):
raise ValueError("netType should be either unet, deeplab and custom.")
# Get config
Config = cfg.Config()
# COCO instance
print("Reading COCO ground truth...")
cocoGt = COCO(Config.COCO_training_ann_path)
cocoValGt = COCO(Config.COCO_validation_ann_path)
print("Finished")
# Get all classes
classes = len(cocoGt.getCatIds())
id_to_index = dict()
# There is a wired class of 0 in the feature map of type zero
index_to_id = dict()
# Because the id of COCO dataset starts from 92, we should project those id to index so that keras
# utils can convert the segmentation map into one hot categorical encoding.
for index, id in enumerate(cocoGt.getCatIds()):
id_to_index[id] = index
index_to_id[index] = id
if net_type == "unet":
model = basic_model.unet(input_size=(img_size, img_size, 3),
classes=len(id_to_index))
elif net_type == "deeplab":
deeplab_model = basic_model.Deeplabv3(input_shape=(img_size, img_size,
3),
classes=len(id_to_index),
backbone="xception")
output = KL.Activation("softmax")(deeplab_model.output)
model = KM.Model(deeplab_model.input, output)
elif net_type == "custom":
model = model.custom_model(input_shape=(img_size, img_size, 3),
classes=len(id_to_index))
file_list = glob(Config.COCO_training_path + '*')
val_list = glob(Config.COCO_validation_path + '*')
if args.load_weights:
try:
model.load_weights(net_type + "_" + str(img_size) + ".h5")
print("weights loaded!")
except:
print("weights not found!")
checkpointer = KC.ModelCheckpoint(filepath=net_type + "_" + str(img_size) +
".h5",
verbose=1,
save_best_only=True)
model.compile(optimizer=KO.Adam(),
loss="categorical_crossentropy",
metrics=["accuracy"])
model.fit_generator(data.generator(batch_size, file_list,
(img_size, img_size), cocoGt,
id_to_index, True),
validation_data=data.generator(batch_size, val_list,
(img_size, img_size),
cocoValGt, id_to_index,
False),
validation_steps=10,
steps_per_epoch=100,
epochs=epochs,
use_multiprocessing=True,
workers=8,
callbacks=[checkpointer])
print("Prediction start...")
vfunc = np.vectorize(lambda index: index_to_id[index])
anns = []
# Convert into COCO annotation
for i in trange(len(val_list)):
image = val_list[i]
image_id = int(image.replace(".jpg", '')[-12:])
cropping_image, padding_dims, original_size = utils.padding_and_cropping(
image, (img_size, img_size))
cropping_image = preprocess_input(cropping_image, mode="torch")
result = model.predict(cropping_image)
result = np.argmax(result, axis=3)
seg_result = utils.reverse_padding_and_cropping(
result, padding_dims, original_size)
seg_result = vfunc(seg_result)
COCO_ann = cocostuffhelper.segmentationToCocoResult(seg_result,
imgId=image_id)
for ann in COCO_ann:
ann["segmentation"]["counts"] = ann["segmentation"][
"counts"].decode("ascii") # json can't dump byte string
anns += COCO_ann
with open("result.json", "w") as file:
json.dump(anns, file)
# Read result file
# Test for fake result
#resFile = Config.fake_result
# Evaluate result
resFile = "result.json"
cocoDt = cocoValGt.loadRes(resFile)
cocoEval = COCOStuffeval(cocoValGt, cocoDt)
cocoEval.evaluate()
cocoEval.summarize()
import model
model = model.create_model()
# print(X_train)
# print(y_train)
callback = tf.keras.callbacks.ModelCheckpoint(
'Transformer+TimeEmbedding_avg.hdf5',
monitor='val_loss',
save_best_only=True,
verbose=1)
history = model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=10,
callbacks=[callback],
validation_data=(X_val, y_val))
model.load_weights('Transformer+TimeEmbedding_avg.hdf5')
y = model.predict(X_test)
fig = plt.figure()
import datetime
test_tick = test_tick.apply(
lambda x: datetime.datetime.strptime(x, '%Y-%m-%d'))
true_y = y * (max_return - min_return) + min_return
true_ytest = y_test * (max_return - min_return) + min_return
df = pd.read_csv('./input/index.csv',
delimiter=',',
usecols=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])
real_close = df[(df.index >= last_10pct)]['Close'][49:-1].values
pre_close = []
for i in range(len(real_close)):
print('RE FPR, TPR: (%.3f, %.3f)' % (fpr_re[idx3RE], tpr_re[idx3RE]))
print('RE FPR, TPR: (%.3f, %.3f)' % (fpr_re[idx4RE], tpr_re[idx4RE]))
print('DeepIso AUC: %.5f' % metrics.auc(fpr_nn, tpr_nn))
print('DeepIso AUC (training set): %.5f' %
metrics.auc(fpr_nn_train, tpr_nn_train))
print('RelIso AUC: %.5f' % metrics.auc(fpr_re, tpr_re))
print('Now making convergence plot')
auc_test = numpy.zeros(nEpochs)
auc_train = numpy.zeros(nEpochs)
x = numpy.linspace(1, nEpochs, nEpochs)
for i in range(nEpochs):
print(i)
model.load_weights("weights/" + savename + "_weights_" +
str(i + 1).zfill(2) + ".hdf5")
prediction = model.predict([
charged_pf_features[nTrain:], photon_pf_features[nTrain:],
neutralHad_pf_features[nTrain:], global_features[nTrain:]
],
batch_size=nBatch)
prediction_training_set = model.predict([
charged_pf_features[:nTrain], photon_pf_features[:nTrain],
neutralHad_pf_features[:nTrain], global_features[:nTrain]
],
batch_size=nBatch)
#plt.figure()
#plt.hist(prediction[(label[nTrain:]).astype(bool)], bins = 100, label = 'Signal', color = 'red')
#plt.hist(prediction[numpy.logical_not(label[nTrain:])], bins = 100, label = 'Background', color = 'blue')
#plt.legend(loc = 'upper left')
_, frame = vc.read()
img = frame
face_image = lib.align_image(img)
if face_image is not None:
#face_image = frame[max(0, y1):min(height, y2), max(0, x1):min(width, x2)]
identity = recognize_face(face_image, input_embeddings, model)
if identity is not None:
#img = cv2.rectangle(frame,(x1, y1),(x2, y2),(255,255,255),2)
#cv2.putText(img, str(identity), (4,30), font, 1, (255,255,255), 2)
print(str(identity))
else:
print("Não reconhecido")
key = cv2.waitKey(100)
#cv2.imshow("Face Recognizer", img)
if key == 27: # exit on ESC
break
else:
print("Nenhuma face detectada!")
vc.release()
cv2.destroyAllWindows()
model = create_model()
model.load_weights('nn4.small2.v1.h5')
input_embeddings = create_input_image_embeddings(model)
recognize_faces_in_cam(input_embeddings, model)
model = model.model()
los = 99999
t = []
for (dirpath, dirnames, filenames) in walk(train_path):
t.extend(filenames)
v = []
for (dirpath, dirnames, filenames) in walk(valid_path):
v.extend(filenames)
t_files = len(t)
v_files = len(v)
try:
model.load_weights("weights.h5", by_name=True)
print("Loading Exiting weights........ please wait")
except:
print("No weights found...... model loaded with image_net weights...")
pass
SavingWeights = ModelCheckpoint('weights.h5',
save_weights_only=True,
verbose=1,
save_best_only=True)
for epoch in range(epochs):
print("Running Epoch No==> " + str(epoch))
train_batches = generator1.data_gen(train_path, desired_size, no_channels,
batch_size, shuffle)
valid_batches = generator1.data_gen(valid_path, desired_size, no_channels,
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
print('X_train shape:', X_train.shape)
# np_utils.to_categorical将整型标签转为onehot。
# 在这里将向量转成了矩阵
Y_train = np_utils.to_categorical(y_train, 40)
Y_val = np_utils.to_categorical(y_val, 40)
Y_test = np_utils.to_categorical(y_test, 40)
model = model.cnn_model()
# 训练模型
train.train_model(model, X_train, Y_train, X_val, Y_val, epochs)
# 测试模型
score = test.test_model(model, X_test, Y_test)
print(score)
# 加载训练好的模型
model.load_weights('model_weights.h5')
# 计算预测的类别
classes = model.predict_classes(X_test, verbose=0)
# 计算正确率
test_accuracy = np.mean(np.equal(y_test, classes))
print("last accuarcy:", test_accuracy)
error_num = 0
for i in range(0, 40):
if y_test[i] != classes[i]:
error_num += 1
print(y_test[i], '被错误分成', classes[i])
print("共有" + str(error_num) + "张图片被识别错了")
def main():
"""main function
Main function... (what do you expect me to say...)
Args:
- none
Returns:
- none
"""
# Main function for evaluate
parser = argparse.ArgumentParser(description="Evaluate the model")
parser.add_argument(
"--net",
help=
"The type of net work which is either mrcnn, unet, deeplab or custom.",
required=True,
default="unet")
parser.add_argument("--img_size",
required=True,
type=int,
help="The size of input image")
parser.add_argument("--gpu_id",
required=False,
default="0",
type=str,
help="The id of the gpu used when training.")
parser.add_argument(
"--weight_path",
required - False,
defalut=None,
type=str,
help=
"The name of trained weights. If not specified, the model will use 'net_imgSize.h5'. "
)
# Parse argument
args = parser.parse_args()
net_type = args.net
gpu_number = args.gpu_id
img_size = args.img_size
weight_path = args.weight_path
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_number
# Argument check
if not (net_type in {"unet", "deeplab", "custom", "mrcnn"}):
raise ValueError(
"netType should be either unet, deeplab, mrcnn and custom.")
if net_type in {"unet", "deeplab", "custom"}:
# Get config
Config = cfg.Config()
# COCO instance
print("Reading COCO ground truth...")
cocoGt = COCO(Config.COCO_training_ann_path)
cocoValGt = COCO(Config.COCO_validation_ann_path)
print("Finished")
# Get all classes
classes = len(cocoGt.getCatIds())
id_to_index = dict()
# There is a wired class of 0 in the feature map of type zero
index_to_id = dict()
# Because the id of COCO dataset starts from 92, we should project those id to index so that keras
# utils can convert the segmentation map into one hot categorical encoding.
for index, id in enumerate(cocoGt.getCatIds()):
id_to_index[id] = index
index_to_id[index] = id
if net_type == "unet":
model = basic_model.unet(input_size=(img_size, img_size, 3),
classes=len(id_to_index))
elif net_type == "deeplab":
deeplab_model = basic_model.Deeplabv3(input_shape=(img_size,
img_size, 3),
classes=len(id_to_index),
backbone="xception")
output = KL.Activation("softmax")(deeplab_model.output)
model = KM.Model(deeplab_model.input, output)
elif net_type == "custom":
model = model.custom_model(input_shape=(img_size, img_size, 3),
classes=len(id_to_index))
if net_type in {"unet", "deeplab", "custom"}:
file_list = glob(Config.COCO_training_path + '*')
val_list = glob(Config.COCO_validation_path + '*')
if weight_path in None:
model.load_weights(net_type + "_" + str(img_size) + ".h5")
print("weights loaded!")
else:
model.load_weights(weight_path)
print("weights loaded!")
#model.compile(optimizer = KO.Adam(clipvalue=2.), loss="categorical_crossentropy", metrics=["accuracy"])
model.compile(optimizer=KO.Adam(),
loss="categorical_crossentropy",
metrics=["accuracy"])
print("Prediction start...")
vfunc = np.vectorize(lambda index: index_to_id[index])
anns = []
# Convert into COCO annotation
for i in trange(len(val_list)):
image = val_list[i]
image_id = int(image.replace(".jpg", '')[-12:])
cropping_image, padding_dims, original_size = utils.padding_and_cropping(
image, (img_size, img_size))
cropping_image = preprocess_input(cropping_image, mode="torch")
result = model.predict(cropping_image)
result = np.argmax(result, axis=3)
seg_result = utils.reverse_padding_and_cropping(
result, padding_dims, original_size)
seg_result = vfunc(seg_result)
COCO_ann = cocostuffhelper.segmentationToCocoResult(seg_result,
imgId=image_id)
for ann in COCO_ann:
ann["segmentation"]["counts"] = ann["segmentation"][
"counts"].decode("ascii") # json can't dump byte string
anns += COCO_ann
with open("result.json", "w") as file:
json.dump(anns, file)
# Evaluate result
resFile = "result.json"
cocoDt = cocoValGt.loadRes(resFile)
cocoEval = COCOStuffeval(cocoValGt, cocoDt)
cocoEval.evaluate()
cocoEval.summarize()
if args.command == "train":
model = model.MaskRCNN(config=config,
model_dir=args.logs,
test_flag=False)
else:
model = model.MaskRCNN(config=config,
model_dir=args.logs,
test_flag=True)
if config.GPU_COUNT:
model = model.cuda()
if args.weights.lower() != "none":
# Select weights file to load
weights_path = args.weights
# Load weights
print("Loading weights ", weights_path)
model.load_weights(weights_path)
# Train or evaluate
if args.command == "train":
print("Training...")
train(model)
elif args.command == "test":
print("Testing...")
test(model, int(args.limit.lower()), args.save.lower(),
args.bbox.lower())
else:
print("'{}' is not recognized. "
"Use 'train' or 'test'".format(args.command))
tf.keras.backend.set_session(sess)
# モデルを作成
model = model.make(tflite=False)
# 最適化を定義
optimizer = tf.keras.optimizers.Adam(lr=0.001)
model.compile(optimizer=optimizer,
loss="categorical_crossentropy",
metrics=["categorical_accuracy"])
# コールバック
class Callback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
"各エポック終了時に重みを保存する"
model.save("weight.hdf5")
cb = Callback()
# 途中から学習する場合
initial_epoch = 0
if initial_epoch >= 1:
model.load_weights("weight.hdf5")
# 学習する
model.fit_generator(g.generator(),
validation_data=g.generator_test(),
validation_steps=g.test_steps(),
callbacks=[cb],
steps_per_epoch=data.TRAIN_SIZE / data.BATCH_SIZE,
epochs=50,
initial_epoch=initial_epoch)
def full_model(pretrained_weights=None, input_size=(256, 256, 1)):
inputs = tf.keras.Input(input_size)
conv1 = tf.keras.layers.Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
inputs) # 256
conv1 = tf.keras.layers.Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool1) # 128
conv2 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool2) # 64
conv3 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool3) # 32
conv4 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool4) # 16
conv5 = Conv2D(1024,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
# conv10 = Conv2D(1, 1, activation = 'relu')(conv9)
# conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv10)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = tf.keras.models.Model(inputs=inputs, outputs=conv10)
model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-4),
loss="mean_absolute_error",
metrics=['accuracy'],
run_eagerly=True)
model.summary()
if (pretrained_weights):
model.load_weights(pretrained_weights)
return model
# load nlp model
#
nlp_model = NLPModel()
out_idx = nlp_model.load_output_term_index()
print(repr(out_idx))
dictionary, max_len = nlp_model.load_input_dict()
print repr(dictionary)
model = nlp_model.create_keras_model()
model.load_weights(KERAS_WEIGHTS_FN)
#
# main
#
db = LogicDB(session)
engine = PrologEngine(db)
prolog_builtins.register_builtins(engine)
parser = PrologParser()
while True:
line = raw_input('nlp> ')
import glob
import six.moves.cPickle
from keras.models import model_from_json
import model
tokeniser = six.moves.cPickle.load(open("tokeniser.pkl", 'rb'))
lastModel = sorted(glob.glob('model-*.h5'))[-1]
model = model_from_json(open("model.json").read())
model.load_weights(lastModel)
dictionarySize = int(open('model-dictionary-size.dat').read())
print(model.query(model, tokeniser, dictionarySize, "It is bad"))
print(model.query(model, tokeniser, dictionarySize, "It is good"))
