def train_model(model: Model, base_model: Model,
train_gen: classifier_sequence.ClassifierSequence,
val_gen: classifier_sequence.ClassifierSequence) -> None:
"""Trains the model on the given data sets."""
for layer in base_model.layers:
layer.trainable = False
opt = optimizers.SGD(learning_rate=0.00001, momentum=0.8, clipnorm=1)
# opt = optimizers.Adam(learning_rate=0.000001)
model.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=[
'accuracy',
metrics.Recall(),
metrics.Precision(),
metrics.FalsePositives(),
metrics.FalseNegatives()
])
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = callbacks.TensorBoard(log_dir=log_dir,
histogram_freq=1)
model.fit(train_gen,
validation_data=val_gen,
epochs=60,
callbacks=[tensorboard_callback])
for layer in base_model.layers:
layer.trainable = True
opt = optimizers.SGD(learning_rate=0.00001, momentum=0.8, clipnorm=1)
model.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=[
'accuracy',
metrics.Recall(),
metrics.Precision(),
metrics.FalsePositives(),
metrics.FalseNegatives()
])
model.fit(train_gen,
validation_data=val_gen,
epochs=120,
initial_epoch=60,
callbacks=[tensorboard_callback])
def test_eval(model, test_df, y_test):
test_data = BertPreprocessing(
test_df[["sentence1", "sentence2"]].values.astype("str"),
y_test,
batch_size=config.batch_size,
shuffle=False,
)
y_pred = model.predict(test_data)
size = y_pred.shape[0]
y_test = y_test[:size, :]
accuracy = metrics.CategoricalAccuracy()
accuracy.update_state(y_test, y_pred)
precision = metrics.Precision()
precision.update_state(y_test, y_pred)
recall = metrics.Recall()
recall.update_state(y_test, y_pred)
f1 = tfa.metrics.F1Score(num_classes=3, average="macro")
f1.update_state(y_test, y_pred)
auc = metrics.AUC()
auc.update_state(y_test, y_pred)
print(f"""
Accuracy: {accuracy.result().numpy()}
Precision: {precision.result().numpy()}
Recall: {recall.result().numpy()}
F1 score: {f1.result().numpy()}
AUC: {auc.result().numpy()}
""")
def confusion_matrix_other_metric(self):
return [
metrics.Accuracy(name='acc'),
metrics.Precision(name='precision'),
metrics.Recall(name='recall'),
metrics.AUC(name='auc'),
]
def evaluate(dataset,
model,
loss,
metrics=[metrics.Recall(), metrics.Precision()]):
"""
Evaluate keras model.
Args:
dataset: Tensorflow Dataset object for evaluate the model.
model: Keras model.
loss: Loss function.
metrics: List of tensorflow mertics. Default contains Recall and Precision.
Returns:
Dict with keys 'loss_value', 'metric_name_1', 'metric_name_2' and etc.
"""
for x, y in dataset:
loss_value = __calculate_loss(model, x, y, loss=loss,
training=False)['loss']
for metric in metrics:
y_pred = predict_classes(model, x, training=False)
metric.update_state(y_true=y, y_pred=y_pred)
result = {
'loss_value': loss_value,
}
for metric in metrics:
result[__get_name(metric)['name']] = metric.result()
return result
def __init__(self, chip_size):
self.chip_size = chip_size
self.metrics = [
metrics.Precision(top_k=1, name='precision'),
metrics.Recall(top_k=1, name='recall'),
CustomMeanIOU(num_classes=6, name='mIOU'),
]
def main(args):
# Used for memory error in RTX2070
physical_devices = tf.config.experimental.list_physical_devices('GPU')
config = tf.config.experimental.set_memory_growth(physical_devices[0],
True)
input_size = (None, None)
# Load model from JSON file if file path was provided...
if os.path.exists(args.model):
try:
with open(args.model, 'r') as f:
json = f.read()
model = model_from_json(json)
args.model = os.path.splitext(os.path.split(args.model)[-1])[0]
except JSONDecodeError:
raise ValueError(
"JSON decode error found. File path %s exists but could not be decoded; verify if JSON encoding was "
"performed properly." % args.model)
# ...Otherwise, create model from this project by using a proper key name
else:
model = models_dict[args.model]((input_size[0], input_size[1], 1))
try:
# Model name should match with the name of a model from
# https://www.tensorflow.org/api_docs/python/tf/keras/applications/
# This assumes you used a model with RGB inputs as the first part of your model,
# therefore your input data should be preprocessed with the corresponding
# 'preprocess_input' function
m = importlib.import_module('tensorflow.keras.applications.%s' %
model.name)
rgb_preprocessor = getattr(m, "preprocess_input")
except ModuleNotFoundError:
rgb_preprocessor = None
# Load trained weights
model.load_weights(args.pretrained_weights)
# Model is compiled to provide the desired metrics
model.compile(optimizer=Adam(lr=1e-4),
loss=custom_losses.bce_dsc_loss(3.0),
metrics=[
custom_losses.dice_coef,
keras_metrics.Precision(),
keras_metrics.Recall()
])
# Here we find to paths to all images from the selected datasets
paths = data.create_image_paths(args.dataset_names, args.dataset_paths)
for path in paths[0, :]:
[im, gt, pred] = data.test_image_from_path(model, path, None,
rgb_preprocessor)
name = os.path.split(path)[-1]
name, extension = os.path.splitext(name)
extension = ".png"
if not os.path.exists(args.save_to) and path != '':
os.makedirs(args.save_to)
cv2.imwrite(os.path.join(args.save_to, name + extension),
255 * np.where(pred > 0.5, 1.0, 0.0))
def eva_metric(self, y_label, y_class):
recall = metrics.Recall()
recall.update_state(y_label, y_class)
print('Recall result: ', recall.result().numpy())
pre = metrics.Precision()
pre.update_state(y_label, y_class)
print('Precision result: ', pre.result().numpy())
def build_model(self,
embedding_size=EMBEDDING_SIZE,
input_length=MAX_DOCUMENT_LENGTH,
link_embedding_size=LINK_EMBEDDING_SIZE,
link_input_length=LINK_INPUT_LENGTH):
he_inputs = keras.Input(shape=(input_length, ), name="hebrew")
en_inputs = keras.Input(shape=(input_length, ), name="english")
link_inputs = keras.Input(shape=(link_input_length, ), name="links")
assert getattr(
self, 'he_embed_model', None
) is not None, "CnnClfEnsemble.load_all_embedding_models() needs to be called before calling build_model()"
he_embed = self.he_embed_model(he_inputs)
en_embed = self.en_embed_model(en_inputs)
link_embed = self.link_embed_model(link_inputs)
self.model_head = self.get_model_head()
outputs = self.model_head([he_embed, en_embed, link_embed])
self.model = keras.Model(inputs=[he_inputs, en_inputs, link_inputs],
outputs=outputs)
self.model.compile(
optimizer=optimizers.Adam(), #learning_rate=0.001),
loss=losses.CategoricalCrossentropy(from_logits=False),
metrics=[
metrics.CategoricalAccuracy(),
metrics.Recall(class_id=0),
metrics.Precision(class_id=0)
])
def evaluate(dataset,
model,
loss,
metrics=[metrics.Recall(), metrics.Precision()]):
"""
Evaluate keras model.
Args:
dataset: Tensorflow Dataset object for evaluate the model.
model: Keras model.
loss: Loss function.
metrics: List of tensorflow mertics. Default contains Recall and Precision.
"""
_data = api.evaluate(dataset=dataset,
model=model,
loss=loss,
metrics=metrics)
for name, val in _data.items():
print('{}: {:.6f}'.format(name, val), end=', ')
print()
def build(self):
model = tf.keras.Sequential()
model.add(layers.Flatten(input_shape=(42, 4)))
for i in range(self.layers):
if self.reg:
model.add(
layers.Dense(self.sizes[i],
activation='elu',
kernel_regularizer=regularizers.l2(
self.reg[i])))
else:
model.add(layers.Dense(self.sizes[i], activation='elu'))
if self.dropout:
model.add(layers.Dropout(self.dropout[i]))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=optimizers.Adam(learning_rate=self.lr),
loss=losses.BinaryCrossentropy(),
metrics=[
'binary_accuracy',
metrics.TruePositives(name='tp'),
metrics.FalseNegatives(name='fn'),
metrics.TrueNegatives(name='tn'),
metrics.FalsePositives(name='fp'),
metrics.Recall(name='recall'),
metrics.Precision(name='precision')
])
return model
def __init__(self, vocab_size, embedding_size, input_length,
n_punct_classes) -> None:
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.input_length = input_length
inputs = keras.Input((self.input_length, ))
model_head = self.get_model_head()(inputs)
has_punct_out = self.get_mlp_model(2, 'has_p')(model_head)
punct_out = self.get_mlp_model(n_punct_classes, 'p')(model_head)
# punct_mask = self.get_punct_mask(n_punct_classes, 'p')({'has_p': has_punct_out, 'p_inter': punct_out})
start_quote_out = self.get_mlp_model(2, 'sq')(model_head)
end_quote_out = self.get_mlp_model(2, 'eq')(model_head)
dash_out = self.get_mlp_model(2, 'd')(model_head)
self.model = keras.Model(inputs=inputs,
outputs={
'has_p': has_punct_out,
'p': punct_out,
'sq': start_quote_out,
'eq': end_quote_out,
'd': dash_out,
})
my_losses = {
"has_p": losses.BinaryCrossentropy(from_logits=False),
"p": losses.CategoricalCrossentropy(from_logits=False),
"sq": losses.BinaryCrossentropy(from_logits=False),
"eq": losses.BinaryCrossentropy(from_logits=False),
"d": losses.BinaryCrossentropy(from_logits=False),
}
my_metrics = {
"has_p":
[metrics.Recall(class_id=1),
metrics.Precision(class_id=1)],
"p": [metrics.Recall(class_id=1),
metrics.Precision(class_id=1)],
"sq": [metrics.Recall(class_id=1),
metrics.Precision(class_id=1)],
"eq": [metrics.Recall(class_id=1),
metrics.Precision(class_id=1)],
"d": [metrics.Recall(class_id=1),
metrics.Precision(class_id=1)],
}
self.model.compile(
optimizer=optimizers.Adam(), #learning_rate=0.001),
loss=my_losses,
metrics=my_metrics)
self.model.summary()
def evaluate(x_test, y_test):
model = keras.models.load_model(WEIGHTS_PATH)
model.compile(loss='categorical_crossentropy',
metrics=[metrics.CategoricalAccuracy(), metrics.Precision(), metrics.Recall()])
loss, accuracy, precision, recall = model.evaluate(x_test, y_test, verbose=1)
F1_Score = 2 * (precision * recall) / (precision + recall)
print('loss:%.4f accuracy:%.4f precision:%.4f recall:%.4f F1_Score:%.4f'
% (loss, accuracy, precision, recall, F1_Score))
def getCompiledModel(self, optimizer=None, loss=None, runEagerly=False):
self.model.compile(
optimizer='adam' if optimizer is None else optimizer,
loss='categorical_crossentropy' if loss is None else loss,
metrics=['accuracy',
metrics.Recall(),
metrics.Precision()],
run_eagerly=runEagerly)
def train(model, x_train, y_train):
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(lr=LEARNING_RATE),
metrics=[metrics.BinaryAccuracy(), metrics.Precision(), metrics.Recall()])
checkpoint = ModelCheckpoint(NN_ATTACK_WEIGHTS_PATH, monitor='precision', verbose=1, save_best_only=True,
mode='max')
model.fit(x_train, y_train,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
callbacks=[checkpoint])
def test_metric_direction_inference():
# Test min metrics.
assert metrics_tracking.infer_metric_direction("MAE") == "min"
assert (
metrics_tracking.infer_metric_direction(metrics.binary_crossentropy) == "min"
)
assert metrics_tracking.infer_metric_direction(metrics.FalsePositives()) == "min"
# All losses in keras.losses are considered as 'min'.
assert metrics_tracking.infer_metric_direction("squared_hinge") == "min"
assert metrics_tracking.infer_metric_direction(losses.hinge) == "min"
assert (
metrics_tracking.infer_metric_direction(losses.CategoricalCrossentropy())
== "min"
)
# Test max metrics.
assert metrics_tracking.infer_metric_direction("binary_accuracy") == "max"
assert (
metrics_tracking.infer_metric_direction(metrics.categorical_accuracy)
== "max"
)
assert metrics_tracking.infer_metric_direction(metrics.Precision()) == "max"
# Test unknown metrics.
assert metrics_tracking.infer_metric_direction("my_metric") is None
def my_metric_fn(x, y):
return x
assert metrics_tracking.infer_metric_direction(my_metric_fn) is None
class MyMetric(metrics.Metric):
def update_state(self, x, y):
return 1
def result(self):
return 1
assert metrics_tracking.infer_metric_direction(MyMetric()) is None
# Test special cases.
assert metrics_tracking.infer_metric_direction("loss") == "min"
assert metrics_tracking.infer_metric_direction("acc") == "max"
assert metrics_tracking.infer_metric_direction("val_acc") == "max"
assert metrics_tracking.infer_metric_direction("crossentropy") == "min"
assert metrics_tracking.infer_metric_direction("ce") == "min"
assert metrics_tracking.infer_metric_direction("weighted_acc") == "max"
assert metrics_tracking.infer_metric_direction("val_weighted_ce") == "min"
assert (
metrics_tracking.infer_metric_direction("weighted_binary_accuracy") == "max"
)
def build_simple_model(dataset='Fashion Mnist',
opt='sgd',
hidden=None,
funcs=None,
loss=None,
metrics_list=None):
model = models.Sequential()
if dataset == 'CIFAR-10':
model.add(layers.Flatten(input_shape=[32, 32, 3]))
elif ('Fashion Mnist'):
model.add(layers.Flatten(input_shape=[28, 28]))
for i in hidden.keys():
model.add(layers.Dense(hidden[i], activation=funcs[i].lower()))
model.add(layers.Dense(10, activation="softmax"))
loss_dict = {
'Categorical Crossentropy': 'categorical_crossentropy',
'Binary Crossentropy': 'binary_crossentropy',
'Categorical Hinge': 'categorical_hinge',
'Huber loss': 'huber_loss'
}
metrics_dict = {
'auc':
metrics.AUC(),
'recall':
metrics.Recall(),
'accuracy':
metrics.CategoricalAccuracy()
if loss.startswith('Categorical') else metrics.Accuracy(),
'precision':
metrics.Precision(),
'categorical Hinge':
metrics.CategoricalHinge(),
'squared Hinge':
metrics.SquaredHinge(),
'Kullback-Leibler divergence':
metrics.KLDivergence(),
'mean absolute error':
metrics.MeanAbsoluteError(),
'mean squared error':
metrics.MeanSquaredError()
}
if metrics_list is not None and len(metrics_list) > 0:
metrics_list = [metrics_dict.get(m, m) for m in metrics_list]
else:
metrics_list = ['accuracy']
loss_f = loss_dict.get(loss)
model.compile(loss=loss_f, optimizer=opt, metrics=metrics_list)
return model
def inference(tfrecords_path, weights_path, wts_root):
""" Inference function to reproduce original model scores. This
script can be run as a standalone using python inference.py.
For more information try: `python inference.py -h`
Parameters
----------
tfrecords_path: str
The path to directory containing preprocessed tfrecords.
weights_path: str
The path to the combined model weights. A copy of the
weights can be found here:
https://gin.g-node.org/shashankbansal56/nondefaced-detector-reproducibility/src/master/pretrained_weights/combined
wts_root: str
The path to the root directory of all the model weights.
A copy of the weights can be found here:
https://gin.g-node.org/shashankbansal56/nondefaced-detector-reproducibility/src/master/pretrained_weights
"""
model = CombinedClassifier(input_shape=(128, 128),
dropout=0.4,
wts_root=wts_root,
trainable=False)
model.load_weights(os.path.abspath(weights_path))
model.trainable = False
dataset_test = get_dataset(
file_pattern=os.path.join(tfrecords_path, "data-test_*"),
n_classes=2,
batch_size=16,
volume_shape=(128, 128, 128),
plane="combined",
mode="test",
)
METRICS = [
metrics.BinaryAccuracy(name="accuracy"),
metrics.Precision(name="precision"),
metrics.Recall(name="recall"),
metrics.AUC(name="auc"),
]
model.compile(
loss=tf.keras.losses.binary_crossentropy,
optimizer=Adam(learning_rate=1e-3),
metrics=METRICS,
)
model.evaluate(dataset_test)
def main(args):
input_size = (None, None)
# Load model from JSON file if file path was provided...
if os.path.exists(args.model):
try:
with open(args.model, 'r') as f:
json = f.read()
model = model_from_json(json)
args.model = os.path.splitext(os.path.split(args.model)[-1])[0]
except JSONDecodeError:
raise ValueError(
"JSON decode error found. File path %s exists but could not be decoded; verify if JSON encoding was "
"performed properly." % args.model)
# ...Otherwise, create model from this project by using a proper key name
else:
model = models_dict[args.model]((input_size[0], input_size[1], 1))
try:
# Model name should match with the name of a model from
# https://www.tensorflow.org/api_docs/python/tf/keras/applications/
# This assumes you used a model with RGB inputs as the first part of your model,
# therefore your input data should be preprocessed with the corresponding
# 'preprocess_input' function
m = importlib.import_module('tensorflow.keras.applications.%s' % model.name)
rgb_preprocessor = getattr(m, "preprocess_input")
except ModuleNotFoundError:
rgb_preprocessor = None
# Load trained weights
model.load_weights(args.pretrained_weights)
# Model is compiled to provide the desired metrics
model.compile(optimizer=Adam(lr=1e-4), loss=custom_losses.bce_dsc_loss(3.0),
metrics=[custom_losses.dice_coef, keras_metrics.Precision(), keras_metrics.Recall()])
# Here we find to paths to all images from the selected datasets
paths = data.create_image_paths(args.dataset_names, args.dataset_paths)
print("Evaluating the model...")
data.save_results_on_paths(model, paths, args.save_to)
metrics = model.evaluate(x=data.validation_image_generator(paths, batch_size=1, rgb_preprocessor=rgb_preprocessor),
steps=paths.shape[1])
result_string = "Dataset: %s\nModel: %s\n" % ("/".join(args.dataset_names), args.model)
for idx, metric in enumerate(model.metrics_names):
result_string += "{}: {:.4f}\n".format(metric, metrics[idx])
for attribute in args.__dict__.keys():
result_string += "\n--%s: %s" % (attribute, str(args.__getattribute__(attribute)))
with open(os.path.join(args.save_to, "results.txt"), "w") as f:
f.write(result_string.strip())
def test_metric_direction_inference():
# Test min metrics.
assert metrics_tracking.infer_metric_direction('MAE') == 'min'
assert metrics_tracking.infer_metric_direction(
metrics.binary_crossentropy) == 'min'
assert metrics_tracking.infer_metric_direction(
metrics.FalsePositives()) == 'min'
# All losses in keras.losses are considered as 'min'.
assert metrics_tracking.infer_metric_direction('squared_hinge') == 'min'
assert metrics_tracking.infer_metric_direction(losses.hinge) == 'min'
assert metrics_tracking.infer_metric_direction(
losses.CategoricalCrossentropy()) == 'min'
# Test max metrics.
assert metrics_tracking.infer_metric_direction('binary_accuracy') == 'max'
assert metrics_tracking.infer_metric_direction(
metrics.categorical_accuracy) == 'max'
assert metrics_tracking.infer_metric_direction(
metrics.Precision()) == 'max'
# Test unknown metrics.
assert metrics_tracking.infer_metric_direction('my_metric') is None
def my_metric_fn(x, y):
return x
assert metrics_tracking.infer_metric_direction(my_metric_fn) is None
class MyMetric(metrics.Metric):
def update_state(self, x, y):
return 1
def result(self):
return 1
assert metrics_tracking.infer_metric_direction(MyMetric()) is None
# Test special cases.
assert metrics_tracking.infer_metric_direction('loss') == 'min'
assert metrics_tracking.infer_metric_direction('acc') == 'max'
assert metrics_tracking.infer_metric_direction('val_acc') == 'max'
assert metrics_tracking.infer_metric_direction('crossentropy') == 'min'
assert metrics_tracking.infer_metric_direction('ce') == 'min'
assert metrics_tracking.infer_metric_direction('weighted_acc') == 'max'
assert metrics_tracking.infer_metric_direction('val_weighted_ce') == 'min'
assert metrics_tracking.infer_metric_direction(
'weighted_binary_accuracy') == 'max'
def load_badword_model() -> Model:
"""
학습된 모델을 불러옵니다. 불러온 모델은 compile 작업을 마친 상태입니다.
return: 사전학습된 tf.keras.Model 객체가 compile된 상태로 반환됩니다.
"""
model = load_model(get_path('model.h5'))
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=[
metrics.BinaryAccuracy(name="acc"),
metrics.Recall(name="recall"),
metrics.Precision(name="prec"),
])
return model
def __get_metric(self, metric):
if metric == "auc":
return m.AUC()
elif metric == "accuracy":
return m.Accuracy()
elif metric == "binary_accuracy":
return m.BinaryAccuracy()
elif metric == "categorical_accuracy":
return m.CategoricalAccuracy()
elif metric == "binary_crossentropy":
return m.BinaryCrossentropy()
elif metric == "categorical_crossentropy":
return m.CategoricalCrossentropy()
elif metric == "sparse_categorical_crossentropy":
return m.SparseCategoricalCrossentropy()
elif metric == "kl_divergence":
return m.KLDivergence()
elif metric == "poisson":
return m.Poission()
elif metric == "mse":
return m.MeanSquaredError()
elif metric == "rmse":
return m.RootMeanSquaredError()
elif metric == "mae":
return m.MeanAbsoluteError()
elif metric == "mean_absolute_percentage_error":
return m.MeanAbsolutePercentageError()
elif metric == "mean_squared_logarithm_error":
return m.MeanSquaredLogarithmError()
elif metric == "cosine_similarity":
return m.CosineSimilarity()
elif metric == "log_cosh_error":
return m.LogCoshError()
elif metric == "precision":
return m.Precision()
elif metric == "recall":
return m.Recall()
elif metric == "true_positive":
return m.TruePositives()
elif metric == "true_negative":
return m.TrueNegatives()
elif metric == "false_positive":
return m.FalsePositives()
elif metric == "false_negative":
return m.FalseNegatives()
else:
raise Exception("specified metric not defined")
def train(model, x_train, y_train):
"""
Train the target model and save the weight of the model
:param model: the model that will be trained
:param x_train: the image as numpy format
:param y_train: the label for x_train
:param weights_path: path to save the model file
:return: None
"""
model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.Adam(lr=5e-5),
metrics=[metrics.CategoricalAccuracy(), metrics.Precision(), metrics.Recall()])
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
epochs=EPOCHS)
model.save(WEIGHTS_PATH)
def __init__(self, n_features, n_classes):
print("##################### Init NN #####################")
self.N_FEATURES = n_features
self.N_CLASSES = n_classes
self.METRICS = [
'accuracy',
tkm.TruePositives(),
tkm.FalsePositives(name='fp'),
tkm.TrueNegatives(name='tn'),
tkm.FalseNegatives(name='fn'),
#tkm.BinaryAccuracy(name='accuracy'),
tkm.Precision(name='precision'),
tkm.Recall(name='recall'),
tkm.AUC(name='auc')
]
self.DATE = datetime.now().strftime("%d-%m_%H%M%S")
create_dir(self.DATE)
def load_simple_model(model_path='',
weights_path='',
opt='sgd',
loss=None,
metrics_list=None):
model = models.load_model(model_path)
model.load_weights(weights_path)
loss_dict = {
'Categorical Crossentropy': 'categorical_crossentropy',
'Binary Crossentropy': 'binary_crossentropy',
'Categorical Hinge': 'categorical_hinge',
'Huber loss': 'huber_loss'
}
metrics_dict = {
'auc':
metrics.AUC(),
'recall':
metrics.Recall(),
'accuracy':
metrics.CategoricalAccuracy()
if loss.startswith('Categorical') else metrics.Accuracy(),
'precision':
metrics.Precision(),
'categorical Hinge':
metrics.CategoricalHinge(),
'squared Hinge':
metrics.SquaredHinge(),
'Kullback-Leibler divergence':
metrics.KLDivergence(),
'mean absolute error':
metrics.MeanAbsoluteError(),
'mean squared error':
metrics.MeanSquaredError()
}
if metrics_list is not None and len(metrics_list) > 0:
metrics_list = [metrics_dict.get(m, m) for m in metrics_list]
else:
metrics_list = ['accuracy']
loss_f = loss_dict.get(loss)
model.compile(loss=loss_f, optimizer=opt, metrics=metrics_list)
return model
def __init__(self, vocab_size, embedding_size, input_length) -> None:
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.input_length = input_length
inputs = keras.Input((self.input_length,))
model_head = self.get_model_head()(inputs)
has_diff_out = self.get_mlp_model(2, 'has_diff')(model_head)
self.model = keras.Model(inputs=inputs, outputs={
'has_diff': has_diff_out,
})
my_losses = {
"has_diff": losses.BinaryCrossentropy(from_logits=False),
}
my_metrics = {
"has_diff": [metrics.Recall(), metrics.Precision()],
}
self.model.compile(optimizer=optimizers.Adam(), #learning_rate=0.001),
loss=my_losses,
metrics=my_metrics)
self.model.summary()
def experiment(self, under=False, ratio=3, plot=False):
METRICS = [
metrics.TruePositives(name='tp'),
metrics.FalsePositives(name='fp'),
metrics.TrueNegatives(name='tn'),
metrics.FalseNegatives(name='fn'),
metrics.BinaryAccuracy(name='accuracy'),
metrics.Precision(name='precision'),
metrics.Recall(name='recall'),
metrics.AUC(name='auc')
]
data = DataLoader()
model = LeNet(data.X, METRICS)
augmenter = Augmenter(data.X, data.Y)
if under:
data.X, data.Y = augmenter.undersample(ratio=ratio)
if self.augmentation.type == 1 or self.augmentation.type == 2:
data.X, data.Y = augmenter.duplicate(noise=self.augmentation.noise,
sigma=self.augmentation.sigma)
elif self.augmentation.type == 3:
data.X, data.Y = augmenter.SMOTE()
#data.normalize()
#print(len(data.X))
#print(len(data.valX))
data.summarize(test=False)
his = model.fit(data.X, data.Y, data.valX, data.valY)
RES, fpr, tpr = model.predict(data.testX, data.testY)
#self.model_summary(RES)
if plot:
self.plot(his)
self.ROC(fpr, tpr)
return RES
def compile(self, model, train_generator, valid_generator):
""":arg
This function contain model compile and model fit process, input a model and output history and trained model
"""
start_time = time()
print("*" * 40, "Start {} Processing".format(model._name), "*" * 40)
# we use a lot of metric to evalute our binary classification result
METRICS = [
metrics.TruePositives(name='tp'),
metrics.FalsePositives(name='fp'),
metrics.TrueNegatives(name='tn'),
metrics.FalseNegatives(name='fn'),
metrics.BinaryAccuracy(name='binary_accuracy'),
#metrics.CategoricalAccuracy(name='accuracy'),
metrics.Precision(name='precision'),
metrics.Recall(name='recall'),
metrics.AUC(name='auc'),
# F1Score(num_classes = int(y_train.shape[1]), name='F1')
]
# define a optimizer
opt_rms = optimizers.RMSprop(lr = 1e-4, decay = 1e-5)
# define compile parameters
model.compile(loss = 'binary_crossentropy', optimizer = opt_rms, metrics = ['accuracy'])
# start to fit
history = model.fit(
train_generator,
steps_per_epoch=20,
epochs=5,
validation_data=valid_generator,
validation_steps=20
)
return history
name='DenseCell-2'),
# final output layer
Dense(1, activation='sigmoid', name='output'),
],
name='Binary-Classifier')
NAME = 'SSBML-Transfer-Model'
OPTIMIZER = 'adam'
LOSS = Focal()
METRICS = [
metrics.BinaryAccuracy(name='accuracy'),
metrics.Precision(),
metrics.Recall(),
# this is an ugly hack but it is neccessary as
# keras does not have simply a "specificity" metric
metrics.SpecificityAtSensitivity(
sensitivity=.01, # this doesn't matter
num_thresholds=1, # so we only get score at threshold = .5
name='specificity')
]
def remove_head(base_model, trainable=False):
'''
Returns a copy of the base model with the head removed.
BATCH_SIZE = int(args.batch_size)
DROPOUT = float(args.dropout)
IMGSIZE = (int(args.imgsize[0]), int(args.imgsize[1]))
LOGDIR = args.logdir
DATA = args.data
BACKBONE = args.backbone
NAME = args.model
# --- define model metrics ---
METRICS = [
metrics.TruePositives(name="True_Positives"),
metrics.FalsePositives(name="False_Positives"),
metrics.TrueNegatives(name="True_Negatives"),
metrics.FalseNegatives(name="False_Negatives"),
metrics.BinaryAccuracy(name="Binary_Accuracy"),
metrics.Precision(name="Precision"),
metrics.Recall(name="Recall"),
metrics.AUC(name="AUC")
]
# --- tensorflow calbacks ---
date = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
if platform.system().lower() == "windows":
LOGDIR = LOGDIR + "\\" + NAME + "\\" + date
else:
LOGDIR = LOGDIR + "/" + NAME + "/" + date
if not os.path.isdir(LOGDIR):
os.makedirs(LOGDIR, exist_ok=True)
tensorboard = callbacks.TensorBoard(log_dir=LOGDIR,
histogram_freq=1,
def train_model(self, themes_weight: List[float],
dataset: TrainValidationDataset, voc_size: int,
keras_callback: LambdaCallback):
article_length = dataset.article_length
theme_count = dataset.theme_count
model = tf.keras.Sequential([
# 1
# keras.layers.Embedding(input_dim=voc_size, output_dim=firstLayoutOutputDim),
# keras.layers.Dropout(0.2),
# keras.layers.Conv1D(200,3,input_shape=(ARTICLE_MAX_WORD_COUNT,firstLayoutOutputDim), activation=tf.nn.relu),
# keras.layers.GlobalAveragePooling1D(),
# keras.layers.Dense(250, activation=tf.nn.relu),
# keras.layers.Dense(theme_count, activation=tf.nn.softmax)
# 2
# keras.layers.Embedding(input_dim=voc_size, output_dim=firstLayoutOutputDim),
# keras.layers.LSTM(ltsmOutputDim, dropout=0.2, recurrent_dropout=0.2, activation='tanh'),
# keras.layers.Dense(theme_count, activation=tf.nn.softmax)
# 3
# keras.layers.Embedding(input_dim=self.voc_size, output_dim=embedding_output_dim),
# keras.layers.Bidirectional(keras.layers.LSTM(intermediate_dim, return_sequences=True)),
# # keras.layers.Dropout(0.1),
# keras.layers.Bidirectional(keras.layers.LSTM(last_dim, dropout=0.05, recurrent_dropout=0.05)),
# keras.layers.Dense(last_dim, activation=tf.nn.relu),
# keras.layers.Dense(self.theme_count, activation=tf.nn.softmax)
# 4
# keras.layers.Embedding(input_dim=self.voc_size, input_length=self.article_length, output_dim=embedding_output_dim),
# keras.layers.Bidirectional(keras.layers.LSTM(intermediate_dim, return_sequences=True, dropout=0.2, recurrent_dropout=0.2)),
# keras.layers.Dropout(0.2),
# keras.layers.Bidirectional(keras.layers.LSTM(last_dim * 2, recurrent_dropout=0.2)), #was last_dim * 2
# keras.layers.Dense(last_dim, activation=tf.nn.relu),
# keras.layers.Dense(self.theme_count, activation=tf.nn.sigmoid)
# 5
#keras.layers.Embedding(input_dim=self.voc_size, input_length=self.article_length, output_dim=embedding_output_dim),
# keras.layers.Conv1D(filters=64, kernel_size=5, input_shape=(self.voc_size, embedding_output_dim), activation="relu"),
# keras.layers.MaxPool1D(4),
#keras.layers.Bidirectional(keras.layers.LSTM(intermediate_dim, recurrent_dropout=0.1)),
#keras.layers.Dense(last_dim, activation=tf.nn.relu),
#keras.layers.Dense(self.theme_count, activation=tf.nn.sigmoid)
#6
keras.layers.Embedding(input_dim=voc_size,
input_length=article_length,
output_dim=128,
mask_zero=True),
keras.layers.Bidirectional(
keras.layers.LSTM(128, recurrent_dropout=0.2, dropout=0.2)),
#keras.layers.Dropout(0.2),
#keras.layers.Dense(last_dim, activation=tf.nn.relu),
# keras.layers.Dense(self.theme_count, activation=tf.nn.sigmoid, use_bias=True,bias_initializer=tf.keras.initializers.Constant(-1.22818328))
keras.layers.Dense(theme_count,
activation=tf.nn.sigmoid,
kernel_regularizer=regularizers.l2(0.1),
activity_regularizer=regularizers.l1(0.05))
# 7
# keras.layers.Embedding(input_dim=self.voc_size, input_length=self.article_length,
# output_dim=embedding_output_dim),
# keras.layers.GlobalAvgPool1D(),
# keras.layers.Dense(last_dim, activation=tf.nn.relu),
# keras.layers.Dense(self.theme_count, activation=tf.nn.sigmoid)
])
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(clipnorm=1, clipvalue=0.5),
#loss=WeightedBinaryCrossEntropy(themes_weight, from_logits=True),
loss=keras.losses.BinaryCrossentropy(from_logits=True),
metrics=[
metrics.AUC(),
metrics.BinaryAccuracy(),
metrics.TruePositives(),
metrics.TrueNegatives(),
metrics.FalseNegatives(),
metrics.FalsePositives(),
metrics.Recall(),
metrics.Precision()
],
run_eagerly=self.run_eagerly)
keras.utils.plot_model(model, 'Model1.png', show_shapes=True)
cb_list = [ManualInterrupter, keras_callback]
model.fit(dataset.trainData,
epochs=10,
steps_per_epoch=dataset.train_batch_count,
validation_data=dataset.validationData,
validation_steps=dataset.validation_batch_count,
callbacks=cb_list,
class_weight={
0: 1,
1: themes_weight[0]
})
model.save("output/" + self.get_model_name() + ".h5")
model.save_weights("output/" + self.get_model_name() + "_weight.h5")
self.__model__ = model
