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Li, Honglin (PG/R - Elec Electronic Eng)
Task_Likelihood
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224ddd18
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224ddd18
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5 years ago
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Mozzie
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import tensorflow as tf
from keras.models import Model
from keras.layers import Dense, Input, Lambda, Dropout
from configuration import conf
from utils.dataloader import Sequential_loader
import numpy as np
from utils.layers import Probability_CLF_Mul_by_task
def mask_layer_by_task(task_input, input_tensor, name=None, return_mask=False):
mask = tf.expand_dims(task_input, axis=-1)
mask = tf.tile(mask, multiples=[1, 1, input_tensor.shape[1] // conf.num_tasks])
mask = tf.keras.layers.Flatten()(mask)
if name is None:
out = Lambda(lambda x: x
*
mask)(input_tensor)
else:
out = Lambda(lambda x: x
*
mask, name=name)(input_tensor)
if return_mask:
return out, mask
else:
return out
def get_model_keras_mask(output_dim, label=None):
inputs = Input(shape=(784,))
task_input = Input(shape=(5,))
archi = Dense(1000, activation='relu')(inputs)
archi = mask_layer_by_task(task_input, archi)
archi = Dense(1000, activation='relu')(archi)
archi = mask_layer_by_task(task_input, archi)
task_output = Probability_CLF_Mul_by_task(conf.num_tasks, num_centers=output_dim // conf.num_tasks)(
[task_input, archi])
task_output = mask_layer_by_task(task_input, task_output, 'task_out')
clf = Dense(output_dim, activation='softmax')(archi)
clf = mask_layer_by_task(task_input, clf, 'clf_out')
model = Model(inputs=[inputs, task_input], outputs=[clf, task_output])
model_latent = Model(inputs=inputs, outputs=archi)
model.compile(loss=['categorical_crossentropy', 'mse'], optimizer='adam', metrics=['accuracy', 'mse'],
loss_weights=[1, 4])
return model, model_latent
data_loader = Sequential_loader()
model, model_latent = get_model_keras_mask(10)
for task_idx in range(conf.num_tasks):
x, y = data_loader.sample(task_idx=task_idx, whole_set=True)
task_input = np.zeros([y.shape[0], conf.num_tasks])
task_input[:, task_idx] = 1
model.fit([x, task_input], [y, task_input], epochs=10, batch_size=conf.batch_size, verbose=0)
if task_idx == 0:
model.layers[1].trainable = False
model.compile(loss=['categorical_crossentropy', 'mse'], optimizer='adam', metrics=['accuracy', 'mse'],
loss_weights=[1, 4])
for task_idx in range(conf.num_tasks):
x, y = data_loader.sample(task_idx, whole_set=True, dataset='test')
for test_idx in range(conf.num_tasks):
task_input = np.zeros([y.shape[0], conf.num_tasks])
task_input[:, test_idx] = 1
res = np.max(model.predict([x, task_input])[1], axis=1)
block_size = conf.test_batch_size
def block_likelihood(res):
block_likelihood = []
for r in res:
extra_index = r.shape[0] % block_size
extra_values = r[-extra_index:]
resize_values = r[:-extra_index]
r = resize_values.reshape(-1, block_size)
r = np.mean(r, axis=1, keepdims=True)
r = np.repeat(r, block_size, axis=1).reshape(-1, )
extra = np.repeat(np.mean(extra_values), len(extra_values))
final = np.append(r, extra)
block_likelihood.append(final)
return block_likelihood
test_acc = []
for task_idx in range(conf.num_tasks):
x, y = data_loader.sample(task_idx, whole_set=True, dataset='test')
res = []
pred = []
for test_idx in range(conf.num_tasks):
task_input = np.zeros([y.shape[0], conf.num_tasks])
task_input[:, test_idx] = 1
prediction = model.predict([x, task_input])
res.append(np.max(prediction[1], axis=1))
pred.append(np.argmax(prediction[0], axis=1))
res = block_likelihood(res)
pred = np.array(pred)
acc = np.sum(pred[np.argmax(res, axis=0), np.arange(pred.shape[1])] == np.argmax(y, axis=1)) / y.shape[0]
print('Task %d, Accuracy %.3f' % (task_idx, acc))
test_acc.append(acc)
print('Average of Test Accuracy : %.3f' % np.mean(test_acc))
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