From d9c61d9ce3de7629ec53357800b3af16902ce347 Mon Sep 17 00:00:00 2001
From: Mozzie <mozzie@Mozzies-MacBook-Pro.local>
Date: Thu, 5 Dec 2019 12:45:47 +0000
Subject: [PATCH] commit
---
.DS_Store | Bin 0 -> 8196 bytes
.idea/Task_likelihood.iml | 11 +
.../inspectionProfiles/profiles_settings.xml | 6 +
.idea/misc.xml | 4 +
.idea/modules.xml | 8 +
.idea/workspace.xml | 88 +++++++
__pycache__/configuration.cpython-36.pyc | Bin 0 -> 219 bytes
config/.DS_Store | Bin 0 -> 6148 bytes
config/__init__.py | 0
config/__pycache__/__init__.cpython-36 2.pyc | Bin 0 -> 148 bytes
config/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 135 bytes
config/__pycache__/config.cpython-36.pyc | Bin 0 -> 5969 bytes
config/config.py | 239 ++++++++++++++++++
config/configuration/default.ini | 30 +++
config/configuration/test.ini | 35 +++
configuration.py | 3 +
learn_consistent.py | 107 ++++++++
results/.DS_Store | Bin 0 -> 6148 bytes
results/mnist/result.png | Bin 0 -> 42525 bytes
utils/.DS_Store | Bin 0 -> 6148 bytes
utils/__init__.py | 0
utils/__pycache__/__init__.cpython-36 2.pyc | Bin 0 -> 147 bytes
utils/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 134 bytes
utils/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 138 bytes
utils/__pycache__/data_utils.cpython-36.pyc | Bin 0 -> 2258 bytes
utils/__pycache__/dataloader.cpython-36.pyc | Bin 0 -> 6016 bytes
utils/__pycache__/dataloader.cpython-37.pyc | Bin 0 -> 5267 bytes
utils/__pycache__/ewc_utils.cpython-36.pyc | Bin 0 -> 2170 bytes
utils/__pycache__/fig2grid.cpython-36.pyc | Bin 0 -> 2404 bytes
utils/__pycache__/layers.cpython-36.pyc | Bin 0 -> 2386 bytes
utils/__pycache__/load_data.cpython-36.pyc | Bin 0 -> 3906 bytes
utils/__pycache__/load_data.cpython-37.pyc | Bin 0 -> 3063 bytes
utils/__pycache__/model_utils.cpython-36.pyc | Bin 0 -> 890 bytes
.../__pycache__/predict_utils.cpython-36.pyc | Bin 0 -> 2195 bytes
utils/__pycache__/train_utils.cpython-36.pyc | Bin 0 -> 574 bytes
utils/__pycache__/utils.cpython-36 2.pyc | Bin 0 -> 365 bytes
utils/__pycache__/utils.cpython-36.pyc | Bin 0 -> 352 bytes
utils/data_utils.py | 133 ++++++++++
utils/dataloader.py | 193 ++++++++++++++
utils/layers.py | 59 +++++
utils/load_data.py | 164 ++++++++++++
utils/model_utils.py | 17 ++
utils/predict_utils.py | 69 +++++
utils/train_utils.py | 9 +
utils/utils.py | 8 +
45 files changed, 1183 insertions(+)
create mode 100644 .DS_Store
create mode 100644 .idea/Task_likelihood.iml
create mode 100644 .idea/inspectionProfiles/profiles_settings.xml
create mode 100644 .idea/misc.xml
create mode 100644 .idea/modules.xml
create mode 100644 .idea/workspace.xml
create mode 100644 __pycache__/configuration.cpython-36.pyc
create mode 100644 config/.DS_Store
create mode 100644 config/__init__.py
create mode 100644 config/__pycache__/__init__.cpython-36 2.pyc
create mode 100644 config/__pycache__/__init__.cpython-36.pyc
create mode 100644 config/__pycache__/config.cpython-36.pyc
create mode 100644 config/config.py
create mode 100644 config/configuration/default.ini
create mode 100644 config/configuration/test.ini
create mode 100644 configuration.py
create mode 100644 learn_consistent.py
create mode 100644 results/.DS_Store
create mode 100644 results/mnist/result.png
create mode 100644 utils/.DS_Store
create mode 100644 utils/__init__.py
create mode 100644 utils/__pycache__/__init__.cpython-36 2.pyc
create mode 100644 utils/__pycache__/__init__.cpython-36.pyc
create mode 100644 utils/__pycache__/__init__.cpython-37.pyc
create mode 100644 utils/__pycache__/data_utils.cpython-36.pyc
create mode 100644 utils/__pycache__/dataloader.cpython-36.pyc
create mode 100644 utils/__pycache__/dataloader.cpython-37.pyc
create mode 100644 utils/__pycache__/ewc_utils.cpython-36.pyc
create mode 100644 utils/__pycache__/fig2grid.cpython-36.pyc
create mode 100644 utils/__pycache__/layers.cpython-36.pyc
create mode 100644 utils/__pycache__/load_data.cpython-36.pyc
create mode 100644 utils/__pycache__/load_data.cpython-37.pyc
create mode 100644 utils/__pycache__/model_utils.cpython-36.pyc
create mode 100644 utils/__pycache__/predict_utils.cpython-36.pyc
create mode 100644 utils/__pycache__/train_utils.cpython-36.pyc
create mode 100644 utils/__pycache__/utils.cpython-36 2.pyc
create mode 100644 utils/__pycache__/utils.cpython-36.pyc
create mode 100644 utils/data_utils.py
create mode 100644 utils/dataloader.py
create mode 100644 utils/layers.py
create mode 100644 utils/load_data.py
create mode 100644 utils/model_utils.py
create mode 100644 utils/predict_utils.py
create mode 100644 utils/train_utils.py
create mode 100644 utils/utils.py
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diff --git a/.idea/Task_likelihood.iml b/.idea/Task_likelihood.iml
new file mode 100644
index 0000000..5c88ce7
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+++ b/.idea/Task_likelihood.iml
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+ <component name="TestRunnerService">
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diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
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+ <settings>
+ <option name="USE_PROJECT_PROFILE" value="false" />
+ <version value="1.0" />
+ </settings>
+</component>
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diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..281715b
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+++ b/.idea/misc.xml
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new file mode 100644
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+++ b/.idea/modules.xml
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diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
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diff --git a/config/config.py b/config/config.py
new file mode 100644
index 0000000..42f5529
--- /dev/null
+++ b/config/config.py
@@ -0,0 +1,239 @@
+import numpy as np
+import configparser
+"""
+Configuration Parser:
+
+ |- Task Setting: Describe the typel of task
+ |- task_type:
+ |- Baseline: load all the data at once
+ |- Sequential: load several tasks sequentially
+ |- _split: split the dataset
+ |- _permute: permute the dataset
+ |- OneShot (#TODO): load data for one shot training
+
+ ######################## Sequential Split Only ##########################
+ |- num_tasks: how many tasks to be trained
+ |- task_labels: list. FOR SPLIT EXPERIMENTS ONLY.
+ #########################################################################
+
+ |- dataset_name:
+ |- mnist
+ |- cifar10
+ |- cifar100
+ |- ucr(#TODO)
+ |- text(#TODO)
+ |- object detection(#TODO)
+
+ |- Model Setting:
+ |- model_type: Model Type
+ |- NN : Conventional Neural Networks
+ |- BNN: Bayesian Neural Networks
+ |- CBLN: Continual Learning Bayesian Neural Networks
+ |- GANs(#TODO): Generative Adversarial Networks
+ |- model_archi: Model Architecture
+ |- MLP: Multilayer Perceptron
+ |- CNN: Convolutioanl Neural Networks
+ |- RNN (#TODO): Recurrent Neural Networks
+ |- RL (#TODO): Reinforcement Neural Networks
+ |- hidden_dim: mlp dim per layer
+ |- num_layers: number of mlp layers
+
+ ######################## Convolutional Only ##########################
+ |- is_conv: use convolutional layers or not. False (Default).
+ |- conv_hidden_dim: convolutional dim per layer
+ |- conv_num_layers: number of convolutional layers
+ |- num_filter
+ |- stride
+ ######################################################################
+
+ |- Training Setting:
+ |- batch_size
+ |- num_epoch / num_iter
+ |- learning_rate
+ |- optimizer: which optimizer to use
+ |- enable_one_hot: True (Default)
+ |- multi_head: False (Default)
+ |- lam: balance the novel and learned knowledge
+ |- enable_iterator: use iterator or sample from data. False (Default)
+
+ |- Test Setting: to produce the uncertainty information
+ |- num_runs
+ |- num_experiment
+
+
+"""
+
+
+
+
+
+class Config(object):
+ def __init__(self,config_file=None):
+ base_path = './config/configuration/'
+ default_path = base_path + 'default.ini'
+ self.cfg = configparser.ConfigParser()
+ self.cfg.read(default_path)
+ if config_file is not None:
+ path = base_path + config_file
+ self.cfg.read(path)
+
+ def read_list(self,input_str,dtype=np.int32):
+
+ task_labels = []
+ count = -1
+ for i in input_str:
+ if i in [',',' ']:
+ pass
+ elif i is '|':
+ count += 1
+ task_labels.append([])
+ else:
+ task_labels[count].append(int(i))
+ return task_labels
+
+ def read_omniglot_labels(self):
+ return [range(10),range(10,20),range(20,30)]
+
+
+ ######################## Task Setting ##########################
+ @property
+ def task_type(self):
+ return self.cfg.get('Task Setting', 'task_type')
+
+ @property
+ def num_tasks(self):
+ try:
+ return self.cfg.getint('Task Setting', 'num_tasks')
+ except configparser.NoOptionError:
+ return len(self.task_labels)
+
+ @property
+ def task_labels(self):
+ #return self.read_omniglot_labels()
+ return self.read_list(self.cfg.get('Task Setting', 'task_labels'))
+
+ @property
+ def dataset_name(self):
+ return self.cfg.get('Task Setting', 'dataset_name')
+
+
+ ######################## Model Setting ##########################
+ @property
+ def model_type(self):
+ return self.cfg.get('Model Setting', 'model_type')
+
+ @property
+ def model_archi(self):
+ return self.cfg.get('Model Setting', 'model_archi')
+
+ @property
+ def hidden_dim(self):
+ return self.cfg.getint('Model Setting', 'hidden_dim')
+
+ @property
+ def num_layers(self):
+ return self.cfg.getint('Model Setting', 'num_layers')
+
+ @property
+ def is_conv(self):
+ return self.cfg.getboolean('Model Setting', 'is_conv')
+
+ @property
+ def conv_hidden_dim(self):
+ return self.cfg.getint('Model Setting', 'conv_hidden_dim')
+
+ @property
+ def conv_num_layers(self):
+ return self.cfg.getint('Model Setting', 'conv_num_layers')
+
+ @property
+ def num_filter(self):
+ return self.cfg.getint('Model Setting', 'num_filter')
+
+ @property
+ def stride(self):
+ return self.cfg.getint('Model Setting', 'stride')
+
+
+ ######################## Training Setting ##########################
+
+ @property
+ def batch_size(self):
+ return self.cfg.getint('Training Setting', 'batch_size')
+
+ @property
+ def num_epoch(self):
+ return self.cfg.getint('Training Setting', 'num_epoch')
+
+ @property
+ def display_frequency(self):
+ return self.cfg.getint('Training Setting', 'display_frequency')
+
+ @property
+ def num_iter(self):
+ try:
+ return int(self.num_epoch * self.num_samples / self.batch_size)
+ except configparser.NoOptionError:
+ return self.cfg.getint('Training Setting', 'num_iter')
+
+ @property
+ def learning_rate(self):
+ return self.cfg.getfloat('Training Setting', 'learning_rate')
+
+ @property
+ def optimizer(self):
+ return self.cfg.get('Training Setting', 'optimizer')
+
+ @property
+ def enable_one_hot(self):
+ return self.cfg.getboolean('Training Setting', 'enable_one_hot')
+
+ @property
+ def multi_head(self):
+ return self.cfg.getboolean('Training Setting', 'multi_head')
+
+ @property
+ def enable_iterator(self):
+ return self.cfg.getboolean('Training Setting', 'enable_iterator')
+
+ @property
+ def lam(self):
+ return self.cfg.getfloat('Training Setting', 'lam')
+
+
+ @property
+ def num_samples(self):
+ return self._num_samples
+
+ @num_samples.setter
+ def num_samples(self,value):
+ self._num_samples = value
+
+ @property
+ def num_classes(self):
+ return self._num_classes
+
+ @num_classes.setter
+ def num_classes(self,value):
+ self._num_classes = value
+
+ ######################## Test Setting ##########################
+ @property
+ def num_runs(self):
+ return self.cfg.getint('Test Setting', 'num_runs')
+
+ @property
+ def num_experiment(self):
+ return self.cfg.getint('Test Setting', 'num_experiment')
+
+
+ @property
+ def test_batch_size(self):
+ return self.cfg.getint('Test Setting', 'test_batch_size')
+
+
+
+
+
+
+
diff --git a/config/configuration/default.ini b/config/configuration/default.ini
new file mode 100644
index 0000000..49cf187
--- /dev/null
+++ b/config/configuration/default.ini
@@ -0,0 +1,30 @@
+[Task Setting]
+# Baseline, Sequential_split, Sequential_permute, OneShot(#TODO)
+task_type = Sequential_split
+
+task_labels = 0,1,2,3,4 | 5,6,7,8,9
+dataset_name = mnist
+
+[Model Setting]
+model_type = CBLN
+model_archi = MLP
+
+hidden_dim = 200
+num_layers = 3
+
+is_conv = False
+
+[Training Setting]
+batch_size = 128
+display_frequency = 200
+learning_rate = 0.01
+optimizer = Adam
+enable_one_hot = True
+multi_head = False
+enable_iterator = False
+lam = 1.
+
+[Test Setting]
+num_runs = 200
+num_experiment = 200
+
diff --git a/config/configuration/test.ini b/config/configuration/test.ini
new file mode 100644
index 0000000..f6b6cc7
--- /dev/null
+++ b/config/configuration/test.ini
@@ -0,0 +1,35 @@
+[Task Setting]
+# Baseline, Sequential_split, Sequential_permute, OneShot(#TODO)
+task_type = Sequential_split
+# num_tasks = 5
+# task_labels = |0|1|2|3|4|5|6|7|8|9
+# task_labels = |0,1|2,3|4,5|6,7|8,9
+# task_labels = |0,1,2,3,4|5,6,7,8,9
+# task_labels = |0,1|2,3|4,5|6,7|8,9
+# task_labels = |0,1,2,3,4,5,6,7,8,9
+# task_labels = |0,1,2|3,4,5|6,7|8,9
+task_labels = |0,1|2,3|4,5|6,7|8,9
+dataset_name = mnist
+
+[Model Setting]
+model_type = NN
+model_archi = MLP
+
+hidden_dim = 200
+num_layers = 4
+
+is_conv = False
+
+[Training Setting]
+batch_size = 128
+num_iter = 1000
+optimizer = Adam
+lam = 0.
+multi_head = False
+enable_one_hot = True
+
+[Test Setting]
+num_runs = 200
+num_experiment = 20
+test_batch_size = 10
+
diff --git a/configuration.py b/configuration.py
new file mode 100644
index 0000000..dcccb95
--- /dev/null
+++ b/configuration.py
@@ -0,0 +1,3 @@
+from config.config import Config
+
+conf = Config('test.ini')
diff --git a/learn_consistent.py b/learn_consistent.py
new file mode 100644
index 0000000..bd108f1
--- /dev/null
+++ b/learn_consistent.py
@@ -0,0 +1,107 @@
+from keras.models import Model
+from keras.layers import Dense, Input, Conv2D, Flatten, MaxPooling2D
+from configuration import conf
+from utils.dataloader import Sequential_loader
+import numpy as np
+from utils.model_utils import mask_layer_by_task
+from utils.layers import Probability_CLF_Mul_by_task
+from utils.train_utils import train_with_task
+from utils.predict_utils import get_task_likelihood, get_test_acc
+
+PATH = './results/%s/' % conf.dataset_name
+
+epochs = 50
+latent_dim = 250
+output_dim = 10
+verbose = 0
+
+data_loader = Sequential_loader()
+
+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.compile(loss=['categorical_crossentropy', 'mse'], optimizer='adam', metrics=['accuracy', 'mse'],
+ loss_weights=[1, 4])
+
+tlh = [] # Task Likelihood
+tlh_std = [] # Standard Deviation of Task Likelihood
+test_acc = []
+for task_idx in range(conf.num_tasks):
+ # Learn a new task
+ train_with_task(model, task_idx=task_idx, data_loader=data_loader)
+ # Get the likelihood of the current task
+ mean, std = get_task_likelihood(model, learned_task=task_idx, test_task=task_idx, data_loader=data_loader)
+ tlh.append(mean)
+ tlh_std.append(std)
+ # Get the likelihood of the next task
+ if task_idx < conf.num_tasks - 1:
+ mean, std = get_task_likelihood(model, learned_task=task_idx, test_task=task_idx+1, data_loader=data_loader)
+ tlh.append(mean)
+ tlh_std.append(std)
+ # Run 200 times to get the test accuracy (for drawing the figure)
+ for _ in range(conf.num_runs):
+ test_acc.append(get_test_acc(model,data_loader,test_on_whole_set=False))
+ # Print the average test accuracy across all the tasks
+ print('Learned %dth Task, Average test accuracy on all the task : %.3f'%(task_idx,get_test_acc(model, data_loader, test_on_whole_set=True)))
+
+
+def paa(sample, w=None):
+ w = sample.shape[0] // 20 if w is None else w
+ l = len(sample)
+ stepfloat = l / w
+ step = int(np.ceil(stepfloat))
+ start = 0
+ j = 1
+ paa = []
+ while start <= (l - step):
+ section = sample[start:start + step]
+ paa.append(np.mean(section))
+ start = int(j * stepfloat)
+ j += 1
+ return paa
+
+
+tlh_s = []
+for i in tlh:
+ tlh_s += i.tolist()
+tlh_s = np.array(tlh_s)
+
+tlh_std_s = []
+for i in tlh_std:
+ tlh_std_s += i.tolist()
+tlh_std_s = np.array(tlh_std_s)
+
+test_acc_s = np.array(test_acc).reshape(-1)
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set()
+
+tlh = np.array(paa(tlh_s))
+tlh_std = np.array(paa(tlh_std_s))
+test_acc = np.array(paa(test_acc_s, tlh.shape[0]))
+
+fig = sns.lineplot(np.arange(len(tlh)), tlh, label='Task Likelihood')
+fig.fill_between(np.arange(len(tlh)), tlh - tlh_std, tlh + tlh_std, alpha=0.3)
+fig = sns.lineplot(np.arange(len(tlh)), test_acc, label='Test Accuracy')
+a = [10, 30, 50, 70]
+for i in a:
+ fig.fill_between(np.arange(i, i + 10 + 1), 0, 0.1, alpha=0.1, color='red')
+ fig.fill_between(np.arange(i - 10, i + 1), 0, 0.1, alpha=0.1, color='green')
+fig.fill_between(np.arange(90 - 10, 90), 0, 0.1, alpha=0.1, color='green')
+# a = fig.get_xticklabels()
+fig.set_xticklabels(['', 'Task 1', 'Task 2', 'Task 3', 'Task 4', 'Task 5'])
+plt.legend(loc='center right')
+plt.savefig(PATH + 'result')
+plt.show()
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diff --git a/utils/data_utils.py b/utils/data_utils.py
new file mode 100644
index 0000000..7a20e6a
--- /dev/null
+++ b/utils/data_utils.py
@@ -0,0 +1,133 @@
+import os
+from matplotlib.pyplot import imread
+import numpy as np
+import pandas as pd
+
+def load_sub_omniglot(path,n=0):
+
+ '''
+ path => Path of train directory or test directory
+ '''
+ X=[]
+ y = []
+ cat_dict = {}
+ lang_dict = {}
+ curr_y = n
+
+ # we load every alphabet seperately so we can isolate them later
+ for alphabet in os.listdir(path):
+ if alphabet == '.DS_Store':
+ continue
+ print("loading alphabet: " + alphabet)
+ lang_dict[alphabet] = [curr_y,None]
+ alphabet_path = os.path.join(path,alphabet)
+
+ # every letter/category has it's own column in the array, so load seperately
+ for letter in os.listdir(alphabet_path):
+ if letter == '.DS_Store':
+ continue
+ cat_dict[curr_y] = (alphabet, letter)
+ category_images=[]
+ letter_path = os.path.join(alphabet_path, letter)
+
+ # read all the images in the current category
+ for filename in os.listdir(letter_path):
+ image_path = os.path.join(letter_path, filename)
+ image = imread(image_path)
+ category_images.append(image)
+ y.append(curr_y)
+ try:
+ #X.append(np.stack(category_images))
+ X += list(np.stack(category_images))
+ # edge case - last one
+ except ValueError as e:
+ print(e)
+ print("error - category_images:", category_images)
+ curr_y += 1
+ lang_dict[alphabet][1] = curr_y - 1
+ y = np.vstack(y)
+ X = np.stack(X)
+ print(X.shape,y.shape,len(np.unique(y)))
+ return X,y
+
+
+def load_omniglot(path=None):
+ if path is None:
+ train_path = './data/omniglot/images_background'
+ test_path = './data/omniglot/images_evaluation'
+ else:
+ train_path,test_path = path
+
+
+ return load_sub_omniglot(train_path),load_sub_omniglot(test_path)
+
+
+
+
+def load_tihm(path=None):
+ if path is None:
+ path = './data/timh1.5'
+
+ data = []
+ label = []
+
+ for filename in os.listdir(path):
+ if '_lag_new.csv' in filename:
+ file_path = os.path.join(path, filename)
+ d = pd.read_csv(file_path)
+ if 'halway' in list(d.columns):
+ d = d.drop(columns='halway')
+ if 'd_front' not in list(d.columns):
+ continue
+ d = np.array(d.values)
+ d = d[:,2:]
+ data += d.tolist()
+ f_label = filename.split('_lag_new.csv')[0]
+ f_label += '_class.csv'
+ file_path = os.path.join(path, f_label)
+ d = np.array(pd.read_csv(file_path,header=None))
+
+ label += d.reshape(-1).tolist()
+
+ data = np.array(data)
+ label = np.array(label)
+
+ return (data[:10000],label[:10000]),(data[10000:],label[10000:])
+
+
+
+def load_agitation(path=None):
+ if path is None:
+ path = './data/aigitation'
+
+ data = []
+ label = []
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/utils/dataloader.py b/utils/dataloader.py
new file mode 100644
index 0000000..ea1a7bb
--- /dev/null
+++ b/utils/dataloader.py
@@ -0,0 +1,193 @@
+import numpy as np
+from utils.load_data import Load_data
+import tensorflow as tf
+from configuration import conf
+
+
+class Baseline_loader(object):
+ def __init__(self):
+ try:
+ load_func = getattr(load_data,'load_'+ conf.dataset_name)
+ self.data = load_func(conf.is_conv)
+ except:
+ assert ValueError('Dataset is not available ... ')
+ conf.num_samples = self.data['X_train'].shape[0]
+ conf.shape_of_sample = self.data['X_train'].shape[1:]
+
+ @property
+ def num_classes(self):
+ return len(np.unique(self.data['y_train']))
+
+ @property
+ def num_samples(self):
+ return self.data['X_train'].shape[1]
+
+ @property
+ def shape_of_sample(self):
+ return self.data['X_train'].shape[1:]
+
+ @property
+ def is_flatten(self):
+ return len(self.data['X_train']) == 2
+
+
+ def sample(self, dataset='train', batch_size=None):
+ if batch_size is None:
+ batch_size = conf.batch_size
+ assert dataset in ['train', 'val', 'test']
+
+ N = self.data['X_' + dataset].shape[0]
+ idx_N = np.random.choice(N, batch_size, replace=False)
+
+ images, labels = self.data['X_' + dataset][idx_N], self.data['y_' + dataset][idx_N]
+
+ return images, labels
+
+ def build_iterator(self, batch_size=None):
+ if batch_size is None:
+ batch_size = conf.batch_size
+ self.init = {}
+ with tf.name_scope('data'):
+ train_data = tf.data.Dataset.from_tensor_slices(self.data['X_train']).batch(batch_size)
+ test_data = tf.data.Dataset.from_tensor_slices(self.data['X_test']).batch(batch_size)
+ iterator = tf.data.Iterator.from_structure(data.output_types,data.output_shapes)
+ img,label = iterator.get_next()
+ self.init['train'] = iterator.make_initializer(train_data)
+ self.init['test'] = iterator.make_initializer(test_data)
+
+ def get_whole_dataset(self):
+ return self.data
+
+
+
+class Sequential_loader(object):
+ def __init__(self):
+ self.data = Load_data().load()
+ self._task_idx = 0
+ num_samples = 0
+ for i in range(len(self.data)):
+ num_samples += self.data[i]['X_train'].shape[0]
+ conf.num_samples = num_samples
+
+ @property
+ def num_classes(self):
+ num_classes = 0
+ for i in range(len(self.data)):
+ num_classes += len(np.unique(self.data[i]['y_train']))
+ return num_classes
+
+ @property
+ def num_samples(self):
+ return conf.num_samples
+
+ @property
+ def shape_of_sample(self):
+ return self.data[0]['X_train'].shape[1:]
+
+ @property
+ def is_flatten(self):
+ return len(self.data[0]['X_train']) == 2
+
+
+ def sample(self, task_idx=None, dataset='train', batch_size=None,whole_set=False):
+ if batch_size is None:
+ batch_size = conf.batch_size
+ if task_idx is None:
+ task_idx = self._task_idx
+ assert dataset in ['train', 'val', 'test']
+
+ if whole_set:
+ return self.data[task_idx]['X_' + dataset], self.data[task_idx]['y_' + dataset]
+
+ N = self.data[task_idx]['X_' + dataset].shape[0]
+ idx_N = np.random.choice(N, batch_size, replace=False)
+
+ images, labels = self.data[task_idx]['X_' + dataset][idx_N], self.data[task_idx]['y_' + dataset][idx_N]
+ if labels.ndim == 1:
+ labels = labels[:,np.newaxis]
+ return images, labels
+
+
+
+ def _build_iterator(self, batch_size=None):
+ if batch_size is None:
+ batch_size = conf.batch_size
+ self.data_init = {}
+ with tf.name_scope('data'):
+ train_data = tf.data.Dataset.from_tensor_slices((self.data[0]['X_train'],self.data[0]['y_train'])).batch(batch_size)
+ test_data = tf.data.Dataset.from_tensor_slices((self.data[0]['X_test'],self.data[0]['y_test'])).batch(batch_size)
+ iterator = tf.data.Iterator.from_structure(data.output_types,data.output_shapes)
+ self.img_holder,self.label_holder = iterator.get_next()
+ self.data_init[0] = {}
+ self.data_init[0]['train'] = iterator.make_initializer(train_data)
+ self.data_init[0]['test'] = iterator.make_initializer(test_data)
+ for i in range(1,len(self.data)):
+ train_data = tf.data.Dataset.from_tensor_slices((self.data[0]['X_train'],self.data[0]['y_train'])).batch(batch_size)
+ test_data = tf.data.Dataset.from_tensor_slices((self.data[0]['X_test'],self.data[0]['y_test'])).batch(batch_size)
+ self.data_init[i] = {}
+ self.data_init[i]['train'] = iterator.make_initializer(train_data)
+ self.data_init[i]['test'] = iterator.make_initializer(test_data)
+
+ @property
+ def task_idx(self):
+ return self._task_idx
+
+
+ @task_idx.setter
+ def task_idx(self,idx):
+ self._task_idx = idx
+ print('------------ Training Task Index : %d ------------'%self._task_idx)
+
+
+ def initial_data(self,task_idx=None):
+ if not hasattr(self,'data_init'):
+ self._build_iterator()
+ if task_idx is None:
+ task_idx = self._task_idx
+
+ return self.data_init[task_idx]
+
+ def get_holder(self):
+ if conf.enable_iterator:
+ return self.img_holder, self.label_holder
+ else:
+ img_holder = tf.placeholder(dtype=tf.float32,shape=[None,self.shape_of_sample[0]])
+ label_holder = tf.placeholder(dtype=tf.float32,shape=[None,conf.num_classes] if conf.enable_one_hot else [None,1])
+
+ return img_holder, label_holder
+
+
+ def get_whole_dataset(self):
+ return self.data
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/utils/layers.py b/utils/layers.py
new file mode 100644
index 0000000..0327b34
--- /dev/null
+++ b/utils/layers.py
@@ -0,0 +1,59 @@
+import tensorflow as tf
+from keras import backend as K
+from utils.model_utils import mask_layer_by_task
+from keras.layers import Layer
+
+# Multiple Centers
+class Probability_CLF_Mul_by_task(Layer):
+ """docstring for Probability_CLF"""
+
+ def __init__(self, output_dim, num_centers=4, activation=None, sigma=1.0, **kwargs):
+ self.output_dim = output_dim
+ self.num_centers = num_centers
+ self.sigma = sigma
+ self.activation = activation
+ super(Probability_CLF_Mul_by_task, self).__init__(**kwargs)
+
+ def build(self, input_shape):
+ self.centers = {}
+ for idx in range(self.output_dim):
+ self.centers[idx] = []
+ for c in range(self.num_centers):
+ W = self.add_weight(name='center%d_%d' % (idx, c), shape=(input_shape[1][1],), initializer='uniform',
+ trainable=True)
+ self.centers[idx].append(W)
+
+ super(Probability_CLF_Mul_by_task, self).build(input_shape[1][1])
+
+ def call(self, inputs, training=None):
+ task_input = inputs[0]
+ x = inputs[1]
+ _, mask = mask_layer_by_task(task_input, x, return_mask=True)
+
+ logits = []
+ for idx in range(self.output_dim):
+ G = []
+
+ for c in range(self.num_centers):
+ G.append(self.gaussian_activation(
+ tf.boolean_mask(tf.squared_difference(x, self.centers[idx][c]), mask[0], axis=1)))
+
+ G = tf.stack(G, axis=1)
+ P = tf.reduce_sum(G, axis=1) / (
+ tf.reduce_sum(G, axis=1) + self.num_centers - tf.reduce_max(G, axis=1) * self.num_centers)
+ logits.append(P)
+
+ logits = tf.stack(logits, axis=1)
+
+ if self.activation is not None:
+ logits = self.activation(logits)
+
+ return K.in_train_phase(mask_layer_by_task(task_input, logits),
+ mask_layer_by_task(task_input, logits),
+ training=training)
+
+ def gaussian_activation(self, x):
+ return tf.exp(-tf.reduce_sum(x, axis=1) / (2. * self.sigma * self.sigma))
+
+ def compute_output_shape(self, input_shape):
+ return input_shape[1][0], self.output_dim
diff --git a/utils/load_data.py b/utils/load_data.py
new file mode 100644
index 0000000..ae9ed23
--- /dev/null
+++ b/utils/load_data.py
@@ -0,0 +1,164 @@
+import numpy as np
+import keras.datasets as Datasets
+from configuration import conf
+from keras.utils import np_utils
+
+class Load_data(object):
+
+ def __init__(self):
+ pass
+
+ def load(self):
+ task_type = conf.task_type
+ dataset_name = conf.dataset_name
+ assert task_type in ['Baseline','Sequential_split','Sequential_permute'], 'Task type is not valid'
+ assert dataset_name in ['mnist','cifar10','cifar100','omniglot','timh','fashion_mnist'], 'Dataset is not available'
+
+
+ data = self.load_dataset(dataset_name,conf.is_conv)
+
+ if task_type == 'Baseline':
+ return data
+ elif task_type == 'Sequential_split':
+ return self.split_data(data)
+ elif task_type == 'Sequential_permute':
+ return self.permute_data(data)
+
+
+
+ def load_dataset(self,data_name,is_conv):
+ if data_name in ['omniglot']:
+ from utils.data_utils import load_omniglot
+ (X_train, y_train), (X_test, y_test) = load_omniglot()
+ elif data_name in ['timh']:
+ from utils.data_utils import load_tihm
+ (X_train, y_train), (X_test, y_test) = load_tihm()
+ #self.get_description([X_train,y_train,X_test,y_test])
+ else:
+ dataset_obj = getattr(Datasets,data_name)
+ (X_train, y_train), (X_test, y_test) = dataset_obj.load_data()
+ self.nb_classes = len(np.unique(y_train))
+ conf.num_classes = self.nb_classes
+ if data_name in ['cifar10','cifar100']:
+ is_conv = True
+ if not is_conv:
+ #assert data_name not in ['cifar10','cifar100'], data_name + ' must be trained with is_conv = True'
+ X_train = X_train.reshape(X_train.shape[0],-1)
+ X_test = X_test.reshape(X_test.shape[0],-1)
+ else:
+ if data_name in ['mnist']:
+ X_train = X_train.reshape(X_train.shape[0],28,28,1)
+ X_test = X_test.reshape(X_test.shape[0],28,28,1)
+
+ elif data_name in ['cifar10','cifar100']:
+ X_train = X_train.reshape(X_train.shape[0],32,32,3)
+ X_test = X_test.reshape(X_test.shape[0],32,32,3)
+
+ elif data_name in ['omniglot']:
+ X_train = X_train.reshape(X_train.shape[0],105,105,1)
+ X_test = X_test.reshape(X_test.shape[0],105,105,1)
+
+
+ if np.max(X_train) == 255.:
+ print('Normalizing the training data ... ')
+ X_train = X_train.astype('float32') / 255
+ X_test = X_test.astype('float32') / 255
+
+ data = {
+ 'X_train': X_train,
+ 'y_train': y_train,
+ 'X_test': X_test,
+ 'y_test': y_test,
+ }
+ return data
+
+
+
+ def split_data(self,data):
+ try:
+ task_labels = conf.task_labels
+ except:
+ raise ValueError('Label is not provided ...')
+ datasets = {}
+ one_hot = conf.enable_one_hot
+ for task_idx,labels in enumerate(task_labels):
+ datasets[task_idx] = {}
+ train_idx = np.in1d(data['y_train'],labels)
+ datasets[task_idx]['X_train'] = data['X_train'][train_idx]
+
+ test_idx = np.in1d(data['y_test'],labels)
+ datasets[task_idx]['X_test'] = data['X_test'][test_idx]
+ if conf.multi_head:
+ if one_hot:
+ datasets[task_idx]['y_train'] = np_utils.to_categorical(data['y_train'][train_idx] - np.min(labels), len(labels))
+ datasets[task_idx]['y_test'] = np_utils.to_categorical(data['y_test'][test_idx] - np.min(labels), len(labels))
+ else:
+ datasets[task_idx]['y_train'] = data['y_train'][train_idx] - np.min(labels)
+ datasets[task_idx]['y_test'] = data['y_test'][test_idx] - np.min(labels)
+ else:
+ datasets[task_idx]['y_train'] = np_utils.to_categorical(data['y_train'][train_idx], int(self.nb_classes)) if one_hot else data['y_train'][train_idx]
+ datasets[task_idx]['y_test'] = np_utils.to_categorical(data['y_test'][test_idx], int(self.nb_classes)) if one_hot else data['y_test'][test_idx]
+
+ #idx = np.in1d(data['y_test'],labels)
+ #datasets[task_idx]['X_test'] = data['X_test'][idx]
+ #datasets[task_idx]['y_test'] = np_utils.to_categorical(data['y_test'][idx], int(self.nb_classes)) if one_hot else data['y_test'][idx]
+
+ return datasets
+
+ def permute_data(self,data):
+ num_tasks = conf.num_tasks
+ permutations = []
+ for i in range(num_tasks):
+ idx = np.arange(data['X_train'].shape[1],dtype=int)
+ if i > 0:
+ np.random.shuffle(idx)
+ permutations.append(idx)
+ datasets = {}
+ one_hot = conf.enable_one_hot
+ for task_idx, perm in enumerate(permutations):
+ datasets[task_idx] = {}
+
+ datasets[task_idx]['X_train'] = data['X_train'][:,perm]
+ datasets[task_idx]['X_test'] = data['X_test'][:,perm]
+ datasets[task_idx]['y_train'] = np_utils.to_categorical(data['y_train'], int(self.nb_classes)) if one_hot else data['y_train']
+ datasets[task_idx]['y_test'] = np_utils.to_categorical(data['y_test'], int(self.nb_classes)) if one_hot else data['y_test']
+
+ return datasets
+
+
+ def get_description(self,data):
+ X_train,y_train,X_test,y_test = data
+
+ print('X_train : ', X_train.shape)
+ print('X_test : ', X_test.shape)
+
+ print('y_train : ', np.unique(y_train))
+ for l in np.unique(y_train):
+ print('y_train == ',l, y_train[y_train==l].shape)
+ print('y_test == ',l, y_test[y_test==l].shape)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/utils/model_utils.py b/utils/model_utils.py
new file mode 100644
index 0000000..fa82ab4
--- /dev/null
+++ b/utils/model_utils.py
@@ -0,0 +1,17 @@
+import tensorflow as tf
+from keras.layers import Lambda
+from configuration import conf
+
+
+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
diff --git a/utils/predict_utils.py b/utils/predict_utils.py
new file mode 100644
index 0000000..0c254a9
--- /dev/null
+++ b/utils/predict_utils.py
@@ -0,0 +1,69 @@
+import numpy as np
+from configuration import conf
+
+
+def block_likelihood(res, keep_dim=False):
+ block_size = conf.test_batch_size
+ extra_index = res.shape[0] % block_size
+ extra_values = res[-extra_index:]
+ resize_values = res[:-extra_index]
+ tlh = resize_values.reshape(-1, block_size)
+ tlh_std = np.std(tlh, axis=1, keepdims=True)
+ tlh_mean = np.mean(tlh, axis=1, keepdims=True)
+ if keep_dim:
+ tlh_mean = np.repeat(tlh_mean, block_size, axis=1).reshape(-1, )
+ extra_mean = np.repeat(np.mean(extra_values), len(extra_values))
+ return np.append(tlh_mean, extra_mean)
+ else:
+ extra_mean = np.mean(extra_values)
+ extra_std = np.std(extra_values)
+ tlh_mean = tlh_mean.reshape(-1, )
+ tlh_std = tlh_std.reshape(-1, )
+ return np.append(tlh_mean, extra_mean), np.append(tlh_std, extra_std)
+
+
+def get_task_likelihood(model, learned_task, test_task, data_loader):
+ task_likelihood = []
+ task_likelihood_var = []
+ tlh_prediction = []
+ pred = []
+ x, y = data_loader.sample(test_task, whole_set=True, dataset='test')
+ for learnd_idx in range(learned_task + 1):
+ task_input = np.zeros([y.shape[0], conf.num_tasks])
+ task_input[:, learnd_idx] = 1
+ prediction = model.predict([x, task_input])
+ tlh = np.max(prediction[1], axis=1)
+ tlh, tlh_var = block_likelihood(tlh)
+ task_likelihood.append(tlh)
+ task_likelihood_var.append(tlh_var)
+ pred.append(np.argmax(prediction[0], axis=1))
+
+ task_likelihood = np.array(task_likelihood)
+ task_likelihood_var = np.array(task_likelihood_var)
+
+ return np.max(task_likelihood, axis=0), \
+ task_likelihood_var[np.argmax(task_likelihood, axis=0), np.arange(task_likelihood_var.shape[1])]
+
+
+def get_test_acc(model, data_loader, test_on_whole_set=True):
+ test_acc = []
+ for task_idx in range(conf.num_tasks):
+ if test_on_whole_set:
+ x, y = data_loader.sample(task_idx, whole_set=True, dataset='test')
+ else:
+ x, y = data_loader.sample(task_idx, batch_size=conf.test_batch_size, 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(block_likelihood(np.max(prediction[1], axis=1), keep_dim=True))
+ pred.append(np.argmax(prediction[0], axis=1))
+
+ res = np.array(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]
+ test_acc.append(acc)
+
+ return np.mean(test_acc)
diff --git a/utils/train_utils.py b/utils/train_utils.py
new file mode 100644
index 0000000..f59c91b
--- /dev/null
+++ b/utils/train_utils.py
@@ -0,0 +1,9 @@
+import numpy as np
+from configuration import conf
+
+
+def train_with_task(model, task_idx, data_loader):
+ 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)
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000..e83e59a
--- /dev/null
+++ b/utils/utils.py
@@ -0,0 +1,8 @@
+import os
+
+def mkdir(path):
+ """ Create a directory if there isn't one already. """
+ try:
+ os.mkdir(path)
+ except OSError:
+ pass
\ No newline at end of file
--
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