def generateHP(self):
activations = [
"elu", "relu", "selu", "tanh", "sigmoid", "exponential", "linear"
]
print("Generating Hyperparameters \n")
hp = Hyperparameter()
hp.add("fc1_neurons", random.randint(5, 50))
hp.add("fc2_neurons", random.randint(5, 50))
hp.add("fc1_activ", activations[random.randint(0,
len(activations) - 1)])
hp.add("fc2_activ", activations[random.randint(0,
len(activations) - 1)])
hp.add("learning_rate", random.uniform(0.0001, 0.01))
print("Finished generating hyperparameters \n")
print(hp)
return hp
def prediction(self, path_test, path_train, path_bestModel):
self.hyperpara = Hyperparameter()
eval_test = Eval()
ner = NER()
reader = Reader()
traininsts = reader.readfiles(path_train)
testinsts = reader.readfiles(path_test)
ner.create_alphabet(traininsts)
self.hyperpara.tag_size = ner.hyperpara.tag_size
self.hyperpara.embedding_num = ner.hyperpara.embedding_num
self.model = BiLstm(self.hyperpara) # BiLstm模型
if self.hyperpara.loadModel == 1 and\
self.hyperpara.load_pattern == 1:
try:
self.model.load_state_dict(torch.load(path_bestModel))
except Exception:
print('模型参数不匹配')
else:
pass
elif self.hyperpara.loadModel == 1 and\
self.hyperpara.load_pattern == 0 :
try:
self.model = torch.load(path_bestModel)
except Exception:
print('模型参数不匹配')
else:
pass
testExamples = ner.change(testinsts)
for idx in range(len(testExamples)):
test_list = []
test_list.append(testExamples[idx])
x, y = ner.variable(test_list)
lstm_feats = self.model(x)
predict = ner.getMaxIndex(lstm_feats)
predictLabels = []
for idy in range(len(predict)):
predictLabels.append(ner.label_AlphaBet.list[predict[idy]])
testinsts[idx].evalPRF(predictLabels, eval_test)
a, e = testinsts[idx].extractA_and_E()
self.Attr.append(a)
self.Eval.append(e)
def __init__(self):
self.word_AlphaBet = AlphaBet()
self.tag_AlphaBet = AlphaBet()
self.hyperpara = Hyperparameter()
def run(model_name, train_file, val_file, num_classes, filename, dropout, input_shape_arg = (224,224,3)):
"""
fit dataset and run training process
Arguments:\n
train_file --> h5 file, fit to model\n
val_file --> h5 file, for validation\n
num_classes --> int, total classes \n
dropout_value --> float, range 0 - 1 for dropout\n
epoch --> int\n
batch_size --> int, [8, 16, 32, 64, 128, 256, etc.]\n
input_shape_arg --> shape of image (W,H,C)\n
lr_value --> learning rate value\n
optimizer --> Adam, SGD\n
Returns:\n
model\n
x_test\n
y_test
"""
# preprocessing data
X_train, Y_train, X_val, Y_val = dataset_preprocess(num_classes, train_file, val_file)
_epoch = 80
lr_value_array = [1e-3, 1e-4]
if model_name == "resnet50":
batch_size_array = [8, 16, 32]
LABEL = ["e-3(8)", "e-3(16)", "e-3(32)", "e-4(8)", "e-4(16)", "e-4(32)"]
elif model_name == "resnet18":
batch_size_array = [16, 32, 64]
LABEL = ["e-3(16)", "e-3(32)", "e-3(64)", "e-4(16)", "e-4(32)", "e-4(64)"]
HP = []
for lr in lr_value_array:
for bs in batch_size_array:
hp = Hyperparameter("adam", lr, bs, dropout)
HP.append(hp)
HISTORY = []
ROC = []
for hp in HP:
K.clear_session()
model = None
# compile model
if model_name == "resnet50":
print("resnet50")
model = ResNet50(input_shape=input_shape_arg, classes=int(num_classes), dropout_value=hp.dropout)
model.compile(optimizer=hp.get_optimizer(), loss='categorical_crossentropy', metrics=['accuracy', metrics.AUC(), metrics.Precision(), metrics.Recall()])
elif model_name == "resnet18":
print("resnet18")
model = ResNet18(input_shape=input_shape_arg, classes=int(num_classes), dropout_value=hp.dropout)
model.compile(optimizer=hp.get_optimizer(), loss='categorical_crossentropy', metrics=['accuracy', metrics.AUC(), metrics.Precision(), metrics.Recall()])
elif model_name == "vgg19":
print("VGG19")
# configure model input
base_model = applications.vgg19.VGG19(weights= None, include_top=False, input_shape= input_shape_arg)
# configure model output
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(hp.dropout(x))
out = Dense(int(num_classes), activation= 'softmax')(x)
# combine model then compile
model = Model(inputs = base_model.input, outputs = out)
model.compile(optimizer= hp.get_optimizer(), loss='categorical_crossentropy', metrics=['accuracy'])
elif model_name == "vgg16":
print("VGG16")
# configure model input
base_model = applications.vgg16.VGG16(weights= None, include_top=False, input_shape= input_shape_arg)
# configure model output
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(hp.dropout(x))
out = Dense(int(num_classes), activation= 'softmax')(x)
# combine model then compile
model = Model(inputs = base_model.input, outputs = out)
model.compile(optimizer= hp.get_optimizer(), loss='categorical_crossentropy', metrics=['accuracy'])
# optimizer == adam
# train the model
history = model.fit(X_train, Y_train, epochs = _epoch, batch_size = hp.batch_size,
validation_data=(X_val, Y_val),
shuffle=True)
HISTORY.append(history)
del model
print(f"DONE for: {hp.optim}-{hp.lr_value}-{hp.batch_size}-{hp.dropout}")
plt.figure(1)
mpl.style.use('seaborn')
i = 0
for history in HISTORY:
plt.plot(history.history["val_accuracy"], f"C{i}", label=LABEL[i])
i = i+1
plt.ylabel('val_acc')
plt.xlabel('epoch')
plt.title(f"Accuracy {filename}")
plt.legend()
plt.savefig(f"ACC-{filename}.png")
print("VAL ACC:")
i = 0
for history in HISTORY:
print(LABEL[i])
i = i + 1
print("auc: {}" .format(statistics.mean( history.history["val_auc_1"] )) )
print("recall: {}" .format(statistics.mean( history.history["val_recall_1"] )) )
print("Prec: {}" .format(statistics.mean( history.history["val_precision_1"] )) )
print("MEAN: {}" .format(statistics.mean( history.history["val_accuracy"] )) )
print("STD: {}" .format(statistics.pstdev( history.history['val_accuracy'] )) )
def __init__(self):
self.feature_alphabet = feature_alphabet()
self.hyperparameter_1 = Hyperparameter()
self.bias = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
self.average_bias = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
def __init__(self):
self.feature_alphabet = feature_alphabet()
self.all_inst = all_inst()
self.hyperparameter_1 = Hyperparameter()
def __init__(self):
self.word_state = collections.OrderedDict()
self.word_AlphaBet = AlphaBet()
self.label_AlphaBet = AlphaBet()
self.hyperpara = Hyperparameter()