commit c6bd717d27301b36745f6863db66e0a5c659fcbd
parent a035c45f6d5cc69836ee8a624d84f8482bfe6fca
Author: Cody Lewis <codymlewis@protonmail.com>
Date: Fri, 19 Apr 2019 20:40:13 +1000
Added callbacks to ANN + some small refactoring
Diffstat:
6 files changed, 58 insertions(+), 39 deletions(-)
diff --git a/.gitignore b/.gitignore
@@ -396,3 +396,4 @@ Session.vim
*.pdf
*.gv
*.h5
+logs/*
diff --git a/README.md b/README.md
@@ -8,6 +8,11 @@ pip3 install -r requirements.txt
```
## Running
+To run every using an ANN as the predictor:
```
python3 TrustModel.py
```
+
+Running `python3 TrustModel.py -h` will give a help menu with the available options,
+when the ANN is training you may run `tensorboard --logdir ./logs` to get a live graph
+of the error curve and accuracy.
diff --git a/TrustManager/ANN.py b/TrustManager/ANN.py
@@ -12,28 +12,33 @@ Date: 2019-04-12
'''
-def create_and_train_ann(train_data, train_labels, test_data, test_labels):
+def create_and_train_ann(train_data, train_labels, test_data, test_labels, model=None):
'''
Create a neural network and train it on the given data.
'''
- model = keras.models.Sequential()
- model.add(keras.layers.Dense(128, input_shape=(3,)))
- model.add(keras.layers.Activation('relu'))
- model.add(keras.layers.Dense(128))
- model.add(keras.layers.Activation('relu'))
- model.add(keras.layers.Dense(128))
- model.add(keras.layers.Activation('relu'))
- model.add(keras.layers.Dense(64))
- model.add(keras.layers.Activation('relu'))
- model.add(keras.layers.Dropout(0.5))
- model.add(keras.layers.Dense(3))
- model.add(keras.layers.Activation('sigmoid'))
+ if not model:
+ model = keras.models.Sequential()
+ model.add(keras.layers.Dense(128, input_shape=(4,)))
+ model.add(keras.layers.Activation('relu'))
+ model.add(keras.layers.Dense(128))
+ model.add(keras.layers.Activation('relu'))
+ model.add(keras.layers.Dense(128))
+ model.add(keras.layers.Activation('relu'))
+ model.add(keras.layers.Dense(64))
+ model.add(keras.layers.Activation('relu'))
+ model.add(keras.layers.Dropout(0.5))
+ model.add(keras.layers.Dense(3))
+ model.add(keras.layers.Activation('sigmoid'))
- adam = keras.optimizers.Adam(lr=0.0001)
- model.compile(loss="mean_squared_error", optimizer=adam, metrics=['accuracy'])
+ adam = keras.optimizers.Adam(lr=0.0001)
+ model.compile(loss="mean_squared_error", optimizer=adam, metrics=['accuracy'])
data = np.array(train_data + test_data)
labels = skp.label_binarize(np.array(train_labels + test_labels), ["-1", "0", "1"])
- model.fit(x=data, y=labels, epochs=4000, validation_split=0.5)
+ tensorboard = keras.callbacks.TensorBoard(log_dir="./logs", histogram_freq=0, write_graph=True, write_images=False)
+ checkpointer = keras.callbacks.ModelCheckpoint("data/ANN.h5", monitor='val_loss', verbose=1,
+ save_best_only=False, save_weights_only=False,
+ mode='auto', period=1)
+ model.fit(x=data, y=labels, epochs=500, validation_split=0.5, callbacks=[tensorboard, checkpointer])
return model
diff --git a/TrustManager/__init__.py b/TrustManager/__init__.py
@@ -137,7 +137,7 @@ class TrustManager:
f"{reports_from_node_i[0]},{reports_on_node_j[0]},{reports_on_node_j[1].csv_output()}\n"
)
- def train(self):
+ def train(self, cont):
'''
Train the predictor.
'''
@@ -145,7 +145,7 @@ class TrustManager:
self.evolve_svm()
self.load_svms()
else:
- self.train_ann()
+ self.train_ann(cont)
self.load_ann()
def evolve_svm(self):
@@ -173,18 +173,17 @@ class TrustManager:
joblib.dump(svms, "data/SVMs.pkl")
print()
- def train_ann(self):
+ def train_ann(self, cont):
'''
Train the artificial neural network.
'''
- train_data, train_notes = read_data(self.__train_filename)
- test_data, test_notes = read_data(self.__test_filename)
+ train_data, train_notes = read_data(self.__train_filename, dict_mode=False)
+ test_data, test_notes = read_data(self.__test_filename, dict_mode=False)
- if not os.path.exists("data/ANN"):
- os.makedirs("data/ANN")
- for reporter_id, _ in train_data.items():
- ANN.create_and_train_ann(train_data[reporter_id], train_notes[reporter_id],
- test_data[reporter_id], test_notes[reporter_id]).save(f"data/ANN/{reporter_id}.h5")
+ if cont and os.path.exists("data/ANN.h5"):
+ self.load_ann()
+ ANN.create_and_train_ann(train_data, train_notes, test_data, test_notes,
+ model=self.__predictor).save("data/ANN.h5")
def load_svms(self):
'''
@@ -197,8 +196,7 @@ class TrustManager:
Load the neural network classifier.
'''
self.__predictor = dict()
- for node_id in range(len(self.__network)):
- self.__predictor[node_id] = keras.models.load_model(f"data/ANN/{node_id}.h5")
+ self.__predictor = keras.models.load_model(f"data/ANN.h5")
def get_all_recommendations(self, service_target, capability_target):
'''
@@ -367,22 +365,30 @@ def load(train_filename, test_filename, use_svm):
return trust_manager
-def read_data(filename, delimiter=","):
+def read_data(filename, delimiter=",", dict_mode=True):
'''
Read data from a csv of reports.
'''
- train_data = dict()
- notes = dict()
+ if dict_mode:
+ train_data = dict()
+ notes = dict()
+ else:
+ train_data = []
+ notes = []
with open(filename) as report_csv:
csv_reader = csv.reader(report_csv, delimiter=delimiter)
for row in csv_reader:
- reporter_id = int(row[0])
- if train_data.get(reporter_id):
- train_data[reporter_id].append(row[1:-1])
- notes[reporter_id].append(row[-1])
+ if dict_mode:
+ reporter_id = int(row[0])
+ if train_data.get(reporter_id):
+ train_data[reporter_id].append(row[1:-1])
+ notes[reporter_id].append(row[-1])
+ else:
+ train_data[reporter_id] = [row[1:-1]]
+ notes[reporter_id] = [row[-1]]
else:
- train_data[reporter_id] = [row[1:-1]]
- notes[reporter_id] = [row[-1]]
+ train_data.append(row[:-1])
+ notes.append(row[-1])
return train_data, notes
diff --git a/TrustModel.py b/TrustModel.py
@@ -27,6 +27,8 @@ if __name__ == '__main__':
help="Use an ann as the predictor [default predictor]")
PARSER.add_argument("-t", "--train", dest="train", action="store_const", const=True, default=False,
help="Train the predictor on the previously generated data")
+ PARSER.add_argument("-co", "--continue", dest="cont", action="store_const", const=True, default=False,
+ help="Continue training the ann.")
PARSER.add_argument("-tr", "--transact", dest="transact", action="store", nargs=3, type=int,
metavar=("ID", "SERVICE", "CAPABILITY"),
help="Simulate a single transaction for node ID for SERVICE at CAPABILITY and print out the trusted list.")
@@ -53,7 +55,7 @@ if __name__ == '__main__':
train_filename=TRAIN_FILENAME, test_filename=TEST_FILENAME, use_svm=ARGS.use_svm
)
TRUST_MANAGER.bootstrap(ARGS.epochs)
- TRUST_MANAGER.train()
+ TRUST_MANAGER.train(ARGS.cont)
TRUST_MANAGER.graph_recommendations(1, 1, 1)
BAD_PER, OK_PER, GOOD_PER = TRUST_MANAGER.simulate_transactions(ARGS.epochs)
print(f"Percentage of bad transactions: {BAD_PER}")
@@ -78,7 +80,7 @@ if __name__ == '__main__':
if ARGS.train:
print("Training...")
TRUST_MANAGER = TrustManager.load(TRAIN_FILENAME, TEST_FILENAME, ARGS.use_svm)
- TRUST_MANAGER.train()
+ TRUST_MANAGER.train(ARGS.cont)
if ARGS.transact:
TRUST_MANAGER = TrustManager.load(TRAIN_FILENAME, TEST_FILENAME, ARGS.use_svm)
diff --git a/logs/events.out.tfevents.1555664174.ostium-veritas b/logs/events.out.tfevents.1555664174.ostium-veritas
Binary files differ.
