def __init__(self,
model,
optimizer,
log_file_dir=None,
data_properties=None):
""" Init. method.
:param log_file_dir: If this is not None, then the training performance is stored in that log file directory.
:param model: The model used for training.
:param optimizer: Optimizer to be used for the weight updates.
"""
self._data_properties = data_properties
self._log_file_dir = log_file_dir
self._optimizer = optimizer
self._model = model
self._tp_obj = TruePositives()
self._tn_obj = TrueNegatives()
self._fp_obj = FalsePositives()
self._fn_obj = FalseNegatives()
self._pre_obj = Precision()
self._rec_obj = Recall()
self._setup_changes = {'train': [], 'valid': []}
self._loss_tt = {'train': [], 'valid': []}
self._loss_ms = {'train': [], 'valid': []}
self._loss_total = {'train': [], 'valid': []}
self._acc = {'train': [], 'valid': []}
self._tn = {'train': [], 'valid': []}
self._tp = {'train': [], 'valid': []}
self._fn = {'train': [], 'valid': []}
self._fp = {'train': [], 'valid': []}
self._rec = {'train': [], 'valid': []}
self._pre = {'train': [], 'valid': []}
class ConfusionMatrixDerivedMetric(tf.keras.metrics.Metric):
def __init__(self, f, name, **kwargs):
super(ConfusionMatrixDerivedMetric, self).__init__(name=name, **kwargs)
self.tp = TruePositives()
self.tn = TrueNegatives()
self.fn = FalseNegatives()
self.fp = FalsePositives()
self.f = f
def update_state(self, y_true, y_pred, sample_weight=None):
self.tp.update_state(y_true, y_pred, sample_weight=sample_weight)
self.tn.update_state(y_true, y_pred, sample_weight=sample_weight)
self.fp.update_state(y_true, y_pred, sample_weight=sample_weight)
self.fn.update_state(y_true, y_pred, sample_weight=sample_weight)
def reset_states(self):
self.tp.reset_states()
self.tn.reset_states()
self.fp.reset_states()
self.fn.reset_states()
def result(self):
return tf.reduce_sum(
self.f(self.tp.result(), self.fn.result(), self.tn.result(),
self.fp.result()))
def __init__(self, f, name, **kwargs):
super(ConfusionMatrixDerivedMetric, self).__init__(name=name, **kwargs)
self.tp = TruePositives()
self.tn = TrueNegatives()
self.fn = FalseNegatives()
self.fp = FalsePositives()
self.f = f
def build_triplet_classifier_model(extractor_model,
dist_type='eucl',
threshold=1.0):
anchor_in = Input(shape=(224, 224, 3), name="anchor_in")
anchor_out = extractor_model(anchor_in)
compare_in = Input(shape=(224, 224, 3), name="compare_in")
compare_out = extractor_model(compare_in)
if dist_type == 'cos':
dist = CosineDistance(name="dist")([anchor_out, compare_out])
else:
dist = EuclidianDistanceSquared(name="dist")([anchor_out, compare_out])
model = Lambda(lambda x: tf.cast((x < threshold), tf.float32))(dist)
model = Model([anchor_in, compare_in], model)
model.compile(optimizer=Adamax(),
loss=None,
metrics=[
BinaryAccuracy(),
Precision(),
Recall(),
TrueNegatives(),
FalsePositives(),
FalseNegatives(),
TruePositives()
])
return model
def build_base_model(input_size):
in_1 = Input(shape=(input_size, ), name="input_1")
in_2 = Input(shape=(input_size, ), name="input_2")
norm_1 = Lambda(lambda tensor: tf.norm(tensor, axis=1, keepdims=True),
name="norm_input_1")(in_1)
norm_2 = Lambda(lambda tensor: tf.norm(tensor, axis=1, keepdims=True),
name="norm_input_2")(in_2)
norm_mul = Multiply(name="multiply_norms")([norm_1, norm_2])
model = Multiply(name="pointwise_multiply")([in_1, in_2])
model = Lambda(lambda tensor: tf.reduce_sum(tensor, axis=1, keepdims=True),
name="sum")(model)
model = Lambda(lambda tensors: tf.divide(tensors[0], tensors[1]),
name="divide")([model, norm_mul])
model = ValueMinusInput(1, name="one_minus_input")(model)
model = LessThan(0.4)(model)
model_out = Lambda(lambda tensor: tf.cast(tensor, tf.float32),
name="cast")(model)
model = Model([in_1, in_2], model_out)
model.compile(loss=MeanSquaredError(),
optimizer=SGD(),
metrics=[
BinaryAccuracy(),
Precision(),
Recall(),
TrueNegatives(),
FalsePositives(),
FalseNegatives(),
TruePositives()
])
return model
def _compile_model(self):
self.model.compile(
optimizer=Adam(),
loss=BinaryCrossentropy(),
metrics=['accuracy', TruePositives(), TrueNegatives(), FalsePositives(), FalseNegatives(), AUC()]
)
self.model.summary()
def train_model(self, themes_weight: ThemeWeights,
dataset: TrainValidationDataset, voc_size: int,
keras_callback: LambdaCallback):
article_length = dataset.article_length
theme_count = dataset.theme_count
model = tf.keras.Sequential([
keras.layers.Embedding(input_dim=voc_size,
input_length=article_length,
output_dim=self.embedding_output_dim,
mask_zero=True),
Dropout(0.3),
keras.layers.Conv1D(filters=64,
kernel_size=3,
input_shape=(voc_size,
self.embedding_output_dim),
activation=tf.nn.relu),
#keras.layers.MaxPooling1D(3),
#keras.layers.Bidirectional(keras.layers.LSTM(64)),
keras.layers.GlobalAveragePooling1D(),
Dropout(0.3),
keras.layers.Dense(theme_count, activation=tf.nn.sigmoid)
])
model.compile(optimizer=tf.keras.optimizers.Adam(clipnorm=1),
loss=WeightedBinaryCrossEntropy(
themes_weight.weight_array()),
metrics=[
AUC(multi_label=True),
BinaryAccuracy(),
TruePositives(),
TrueNegatives(),
FalseNegatives(),
FalsePositives(),
Recall(),
Precision()
],
run_eagerly=True)
model.summary()
self.__model__ = model
if self.__plot_directory is not None:
self.plot_model(self.__plot_directory)
# Fix for https://github.com/tensorflow/tensorflow/issues/38988
model._layers = [
layer for layer in model._layers if not isinstance(layer, dict)
]
callbacks = [ManualInterrupter(), keras_callback]
model.fit(dataset.trainData,
epochs=self.epochs,
steps_per_epoch=dataset.train_batch_count,
validation_data=dataset.validationData,
validation_steps=dataset.validation_batch_count,
callbacks=callbacks)
def get_custom_metrics():
custom_metrics: dict = {
"precision": Precision(),
"recall": Recall(),
"true_positives": TruePositives(),
"true_negatives": TrueNegatives(),
"false_negatives": FalseNegatives(),
"false_positives": FalsePositives()
}
return custom_metrics
def build_cos_model(input_size,
cos_dist_lvl,
n_neurons,
n_layers,
batch_norm=True,
loss=MeanSquaredError(),
optimizer=SGD(learning_rate=0.05, momentum=0.025)):
in_1 = Input(shape=(input_size, ), name="input_1")
in_2 = Input(shape=(input_size, ), name="input_2")
if cos_dist_lvl == 0:
model = Concatenate(name="concatenate")([in_1, in_2])
else:
model = Multiply(name="pointwise_multiply")([in_1, in_2])
if cos_dist_lvl >= 2:
norm_1 = Lambda(
lambda tensor: tf.norm(tensor, axis=1, keepdims=True),
name="norm_input_1")(in_1)
norm_2 = Lambda(
lambda tensor: tf.norm(tensor, axis=1, keepdims=True),
name="norm_input_2")(in_2)
norm_mul = Multiply(name="multiply_norms")([norm_1, norm_2])
model = Lambda(lambda tensors: tf.divide(tensors[0], tensors[1]),
name="divide")([model, norm_mul])
if cos_dist_lvl >= 3:
model = Lambda(
lambda tensor: tf.reduce_sum(tensor, axis=1, keepdims=True),
name="sum")(model)
if cos_dist_lvl >= 4:
model = ValueMinusInput(1, name="one_minus_input")(model)
if batch_norm:
model = BatchNormalization(name="input_normalization")(model)
for i in range(n_layers):
model = Dense(n_neurons,
activation='sigmoid',
name="dense_{}".format(i))(model)
model_out = Dense(1, activation='sigmoid', name="classify")(model)
model = Model([in_1, in_2], model_out)
model.compile(loss=loss,
optimizer=optimizer,
metrics=[
BinaryAccuracy(),
Precision(),
Recall(),
TrueNegatives(),
FalsePositives(),
FalseNegatives(),
TruePositives()
])
return model
def build_model(self):
with tf.name_scope('Model'):
self.model = self.build()
self.model.compile(optimizer=Adam(lr=self.learning_rate),
loss=FocalLoss(alpha=.25, gamma=2),
metrics=[
'accuracy', f1,
TrueNegatives(),
FalseNegatives(),
TruePositives(),
FalsePositives()
])
print(self.model.summary())
def build_eucl_model(input_size,
eucl_dist_lvl,
n_neurons,
n_layers,
batch_norm=True,
loss=MeanSquaredError(),
optimizer=SGD(learning_rate=0.05, momentum=0.025)):
in_1 = Input(shape=(input_size, ), name="input_1")
in_2 = Input(shape=(input_size, ), name="input_2")
if eucl_dist_lvl == 0:
model = Concatenate(name="concatenate")([in_1, in_2])
else:
model = Subtract(name="subtract")([in_1, in_2])
if eucl_dist_lvl >= 2:
model = Lambda(lambda tensor: tf.square(tensor),
name="square")(model)
if eucl_dist_lvl >= 3:
model = Lambda(
lambda tensor: tf.reduce_sum(tensor, axis=1, keepdims=True),
name="sum")(model)
if eucl_dist_lvl >= 4:
model = Lambda(lambda tensor: tf.sqrt(tensor), name="root")(model)
if batch_norm:
model = BatchNormalization(name="input_normalization")(model)
for i in range(n_layers):
model = Dense(n_neurons,
activation='sigmoid',
name="dense_{}".format(i))(model)
model_out = Dense(1, activation='sigmoid', name="classify")(model)
model = Model([in_1, in_2], model_out)
model.compile(loss=loss,
optimizer=optimizer,
metrics=[
BinaryAccuracy(),
Precision(),
Recall(),
TrueNegatives(),
FalsePositives(),
FalseNegatives(),
TruePositives()
])
return model
def define_lstm(TOP_WORDS, max_sent_len):
embedding_vector_length = 16
model = Sequential()
num_neurons = 16
# Input layer
model.add(
Embedding(TOP_WORDS,
embedding_vector_length,
input_length=max_sent_len))
model.add(LSTM(16, return_sequences=True))
model.add(Dropout(0.4))
model.add(LSTM(16))
model.add(Dropout(0.4))
# model.add(Dense(units=num_neurons,
# activation = 'sigmoid'))
# model.add(Dropout(0.3))
model.add(Dense(units=num_neurons, activation='relu'))
model.add(Dropout(0.4))
# Output layer
model.add(Dense(1, activation='sigmoid'))
metrics = [
FalseNegatives(name='fn'),
FalsePositives(name='fp'),
TrueNegatives(name='tn'),
TruePositives(name='tp'),
Precision(name='precision'),
Recall(name='recall'),
]
model.summary()
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=metrics)
return model
def create_model_RNN_LSTM(optimizer='adam',
units=200,
activation='sigmoid',
EMBEDDING_DIM=25,
max_length=37,
vocab_size=350,
hidden_dims=1,
hidden_dim_units=25):
print("Parameters", "units", units, "activation", activation,
"EMBEDDING_DIM", EMBEDDING_DIM, "max_length", max_length,
"vocab_size", vocab_size, " hidden_dims ", hidden_dims)
keras_eval_metric = [[
TruePositives(name='tp'),
FalsePositives(name='fp'),
TrueNegatives(name='tn'),
FalseNegatives(name='fn'),
BinaryAccuracy(name='accuracy'),
Precision(name='precision'),
Recall(name='recall'),
AUC(name='auc'),
]]
model = Sequential()
model.add(Embedding(vocab_size, EMBEDDING_DIM, input_length=max_length))
model.add(
LSTM(units=hidden_dim_units,
dropout=0.2,
recurrent_dropout=0.2,
activation=activation))
for i in range(hidden_dims):
model.add(Dense(hidden_dim_units))
model.add(Activation(activation))
model.add(Dropout(0.2))
model.add(Dense(1))
model.add(Activation(activation))
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=keras_eval_metric)
print("\n\n", model.summary(), "\n\n", model.get_config(), "\n\n")
return model
def define_mlp_model(n_input):
"""
define the multi-layer-perceptron neural network
Args:
n_input(int): number of features
Returns:
a defined mlp model, not fitted
"""
model = Sequential()
num_neurons = 256 #256
# hidden layer
model.add(
Dense(units=num_neurons,
input_dim=n_input,
kernel_initializer='he_uniform',
activation='sigmoid'))
model.add(Dense(units=num_neurons * 2, activation='sigmoid'))
model.add(Dropout(0.3))
model.add(Dense(units=num_neurons * 2, activation='sigmoid'))
model.add(Dropout(0.3))
# output layer
model.add(Dense(units=1, activation='sigmoid'))
model.summary()
metrics = [
FalseNegatives(name='fn'),
FalsePositives(name='fp'),
TrueNegatives(name='tn'),
TruePositives(name='tp'),
Precision(name='precision'),
Recall(name='recall'),
]
#sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)
adam = Adam(1e-2)
model.compile(loss='binary_crossentropy', optimizer=adam, metrics=metrics)
return model
def perf_measure(y_te, y_pred):
TP = np.zeros(8)
FN = np.zeros(8)
FP = np.zeros(8)
TN = np.zeros(8)
for i in range(y_pred.shape[1]):
tp = TruePositives()
fn = FalseNegatives()
fp = FalsePositives()
tn = TrueNegatives()
tp.update_state(y_te[:,i], y_pred[:,i])
fn.update_state(y_te[:,i], y_pred[:,i])
fp.update_state(y_te[:,i], y_pred[:,i])
tn.update_state(y_te[:,i], y_pred[:,i])
TP[i] = tp.result().numpy()
FN[i] = fn.result().numpy()
FP[i] = fp.result().numpy()
TN[i] = tn.result().numpy()
tp.reset_states()
fn.reset_states()
fp.reset_states()
tn.reset_states()
return [TP, TN, FN, FP]
def build_model_hpconfig(args):
"""
Description:
Building models for hyperparameter Tuning
Args:
args: input arguments
Returns:
model (keras model)
"""
#parsing and assigning hyperparameter variables from argparse
conv1_filters = int(args.conv1_filters)
conv2_filters = int(args.conv2_filters)
conv3_filters = int(args.conv3_filters)
window_size = int(args.window_size)
kernel_regularizer = args.kernel_regularizer
max_pool_size = int(args.pool_size)
conv_dropout = float(args.conv_dropout)
conv1d_initializer = args.conv_weight_initializer
recurrent_layer1 = int(args.recurrent_layer1)
recurrent_layer2 = int(args.recurrent_layer2)
recurrent_dropout = float(args.recurrent_dropout)
after_recurrent_dropout = float(args.after_recurrent_dropout)
recurrent_recurrent_dropout = float(args.recurrent_recurrent_dropout)
recurrent_initalizer = args.recurrent_weight_initializer
optimizer = args.optimizer
learning_rate = float(args.learning_rate)
bidirection = args.bidirection
recurrent_layer = str(args.recurrent_layer)
dense_dropout = float(args.dense_dropout)
dense_1 = int(args.dense_1)
dense_initializer = args.dense_weight_initializer
train_data = str(args.train_input_data)
#main input is the length of the amino acid in the protein sequence (700,)
main_input = Input(shape=(700, ), dtype='float32', name='main_input')
#Embedding Layer used as input to the neural network
embed = Embedding(output_dim=21, input_dim=21,
input_length=700)(main_input)
#secondary input is the protein profile features
auxiliary_input = Input(shape=(700, 21), name='aux_input')
#get shape of input layers
print("Protein Sequence shape: ", main_input.get_shape())
print("Protein Profile shape: ", auxiliary_input.get_shape())
#concatenate input layers
concat = Concatenate(axis=-1)([embed, auxiliary_input])
#3x1D Convolutional Hidden Layers with BatchNormalization, Dropout and MaxPooling
conv_layer1 = Conv1D(conv1_filters,
window_size,
kernel_regularizer=kernel_regularizer,
padding='same',
kernel_initializer=conv1d_initializer)(concat)
batch_norm = BatchNormalization()(conv_layer1)
conv_act = activations.relu(batch_norm)
conv_dropout = Dropout(conv_dropout)(conv_act)
max_pool_1D_1 = MaxPooling1D(pool_size=max_pool_size,
strides=1,
padding='same')(conv_dropout)
conv_layer2 = Conv1D(conv2_filters,
window_size,
padding='same',
kernel_initializer=conv1d_initializer)(concat)
batch_norm = BatchNormalization()(conv_layer2)
conv_act = activations.relu(batch_norm)
conv_dropout = Dropout(conv_dropout)(conv_act)
max_pool_1D_2 = MaxPooling1D(pool_size=max_pool_size,
strides=1,
padding='same')(conv_dropout)
conv_layer3 = Conv1D(conv3_filters,
window_size,
kernel_regularizer=kernel_regularizer,
padding='same',
kernel_initializer=conv1d_initializer)(concat)
batch_norm = BatchNormalization()(conv_layer3)
conv_act = activations.relu(batch_norm)
conv_dropout = Dropout(conv_dropout)(conv_act)
max_pool_1D_3 = MaxPooling1D(pool_size=max_pool_size,
strides=1,
padding='same')(conv_dropout)
#concat pooling layers
conv_features = Concatenate(axis=-1)(
[max_pool_1D_1, max_pool_1D_2, max_pool_1D_3])
print("Shape of convolutional output: ", conv_features.get_shape())
conv_features = Dense(600, activation='relu')(conv_features)
######## Recurrent Layers ########
if (recurrent_layer == 'lstm'):
if (bidirection):
print('Entering LSTM Layers')
#Creating Bidirectional LSTM layers
lstm_f1 = Bidirectional(
LSTM(recurrent_layer1,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer))(conv_features)
lstm_f2 = Bidirectional(
LSTM(recurrent_layer2,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer))(lstm_f1)
#concatenate LSTM with convolutional layers
concat_features = Concatenate(axis=-1)(
[lstm_f1, lstm_f2, conv_features])
concat_features = Dropout(after_recurrent_dropout)(concat_features)
print('Concatenated LSTM layers')
else:
#Creating unidirectional LSTM Layers
lstm_f1 = LSTM(
recurrent_layer1,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer)(conv_features)
lstm_f2 = LSTM(recurrent_layer2,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer)(lstm_f1)
#concatenate LSTM with convolutional layers
concat_features = Concatenate(axis=-1)(
[lstm_f1, lstm_f2, conv_features])
concat_features = Dropout(after_recurrent_dropout)(concat_features)
elif (recurrent_layer == 'gru'):
if (bidirection):
#Creating Bidirectional GRU layers
gru_f1 = Bidirectional(
GRU(recurrent_layer1,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer))(conv_features)
gru_f2 = Bidirectional(
GRU(recurrent_layer2,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer))(gru_f1)
#concatenate LSTM with convolutional layers
concat_features = Concatenate(axis=-1)(
[gru_f1, gru_f2, conv_features])
concat_features = Dropout(after_recurrent_dropout)(concat_features)
else:
#Creating unidirectional GRU Layers
gru_f1 = GRU(
recurrent_layer1,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer)(conv_features)
gru_f2 = GRU(recurrent_layer1,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=recurrent_dropout,
recurrent_dropout=recurrent_recurrent_dropout,
kernel_initializer=recurrent_initalizer)(gru_f1)
#concatenate LSTM with convolutional layers
concat_features = Concatenate(axis=-1)(
[gru_f1, gru_f2, conv_features])
concat_features = Dropout(after_recurrent_dropout)(concat_features)
else:
print('Only LSTM and GRU recurrent layers are used in this model')
return
#Dense Fully-Connected DNN layers
fc_dense1 = Dense(dense_1,
activation='relu',
kernel_initializer=dense_initializer)(concat_features)
fc_dense1_dropout = Dropout(dense_dropout)(fc_dense1)
#Final Output layer with 8 nodes for the 8 output classifications
main_output = Dense(8, activation='softmax',
name='main_output')(fc_dense1_dropout)
#create model from inputs and outputs
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output])
#Set optimizer to be used with the model, default is Adam
if optimizer == 'adam':
optimizer = Adam(lr=learning_rate, name='adam')
elif optimizer == 'sgd':
optimizer = SGD(lr=0.01, momentum=0.0, nesterov=False, name='SGD')
elif optimizer == 'rmsprop':
optimizer = RMSprop(learning_rate=learning_rate,
centered=True,
name='RMSprop')
elif optimizer == 'adagrad':
optimizer = Adagrad(learning_rate=learning_rate, name='Adagrad')
elif optimizer == 'adamax':
optimizer = Adamax(learning_rate=learning_rate, name='Adamax')
else:
optimizer = 'adam'
optimizer = Adam(lr=learning_rate, name='adam')
#compile model using optimizer and the cateogorical crossentropy loss function
model.compile(optimizer=optimizer,
loss={'main_output': 'categorical_crossentropy'},
metrics=[
'accuracy',
MeanSquaredError(),
FalseNegatives(),
FalsePositives(),
TrueNegatives(),
TruePositives(),
MeanAbsoluteError(),
Recall(),
Precision()
])
#get summary of model including its layers and num parameters
model.summary()
return model
def generate_compiled_segmentation_model(
model_name,
model_parameters,
num_classes,
loss,
optimizer,
weights_to_load=None,
optimizing_threshold_class_metric=None,
optimizing_class_id=None,
optimizing_input_threshold=None,
optimized_class_thresholds=None):
# These are the only model, loss, and optimizer currently supported
assert model_name == 'Unet'
assert loss == 'cross_entropy'
assert optimizer == 'adam'
loss_fn = BinaryCrossentropyL()
all_metrics = [
] # one-hot versions are generally preferred for given metric
# make first metric a copy of loss, to continually verify `val_loss` is correct
if isinstance(loss_fn, BinaryCrossentropyL):
all_metrics.append(BinaryCrossentropyM(name='binary_ce_metric'))
else:
all_metrics.append(CategoricalCrossentropyM(name='categ_ce_metric'))
# standard thresholded version (default threshold is 0.5) also kept below, in case it's desired in certain scenario
for class_num in range(num_classes + 1):
if class_num == 0 and optimizing_threshold_class_metric is None: # all class metrics
# note, `loss_fn` for all classes placed before `all_metrics` in lineup of command window metrics and plots
if not isinstance(loss_fn, BinaryCrossentropyL):
all_metrics.extend([CategoricalCELoss()])
all_metrics[1].name = str('categ_cross_entropy_sm')
all_metrics.extend([
AccuracyTfKeras(),
# OneHotAccuracyTfKeras(), # `global_threshold` built-in
ClassBinaryAccuracyTfKeras(thresholds=global_threshold),
# OneHotClassBinaryAccuracyTfKeras(thresholds=global_threshold),
ClassBinaryAccuracySM(threshold=global_threshold),
# OneHotClassBinaryAccuracySM(threshold=global_threshold),
BinaryAccuracy(threshold=global_threshold),
CategoricalAccuracy(),
FalseNegatives(name='false_neg', thresholds=global_threshold),
# OneHotFalseNegatives(name='false_neg_1H', thresholds=global_threshold),
TrueNegatives(name='true_neg', thresholds=global_threshold),
# OneHotTrueNegatives(name='true_neg_1H', thresholds=global_threshold),
FalsePositives(name='false_pos', thresholds=global_threshold),
# OneHotFalsePositives(name='false_pos_1H', thresholds=global_threshold),
TruePositives(name='true_pos', thresholds=global_threshold),
# OneHotTruePositives(name='true_pos_1H', thresholds=global_threshold),
Recall(name='recall', thresholds=global_threshold),
# OneHotRecall(name='recall_1H', thresholds=global_threshold),
Precision(name='precision', thresholds=global_threshold),
# OneHotPrecision(name='precision_1H', thresholds=global_threshold),
FBetaScore(name='f1_score',
beta=1,
thresholds=global_threshold),
# OneHotFBetaScore(name='f1_score_1H', beta=1, thresholds=global_threshold),
IoUScore(name='iou_score', thresholds=global_threshold),
# OneHotIoUScore(name='iou_score_1H', thresholds=global_threshold)
])
elif class_num == 0 and optimizing_threshold_class_metric is not None: # all class metrics
continue
else: # per class metrics
if optimizing_threshold_class_metric is not None:
class_threshold = optimizing_input_threshold
class_num = optimizing_class_id + 1
elif optimized_class_thresholds is None:
class_threshold = global_threshold
else:
class_threshold = optimized_class_thresholds[str(
'class' + str(class_num - 1))]
all_metrics.append(CategoricalCELoss(class_indexes=class_num - 1))
all_metrics[-1].name = str('class' + str(class_num - 1) +
'_binary_cross_entropy')
all_metrics.append(
ClassBinaryAccuracySM(name=str('class' + str(class_num - 1) +
'_binary_accuracy_sm'),
class_indexes=class_num - 1,
threshold=class_threshold))
all_metrics.append(
ClassBinaryAccuracyTfKeras(
name=str('class' + str(class_num - 1) +
'_binary_accuracy_tfkeras'),
class_id=class_num - 1,
thresholds=class_threshold))
all_metrics.append(
IoUScore(name=str('class' + str(class_num - 1) + '_iou_score'),
class_id=class_num - 1,
thresholds=class_threshold))
all_metrics.append(
FBetaScore(name=str('class' + str(class_num - 1) +
'_f1_score'),
class_id=class_num - 1,
beta=1,
thresholds=class_threshold))
all_metrics.append(
Precision(name=str('class' + str(class_num - 1) +
'_precision'),
class_id=class_num - 1,
thresholds=class_threshold))
all_metrics.append(
Recall(name=str('class' + str(class_num - 1) + '_recall'),
class_id=class_num - 1,
thresholds=class_threshold))
if optimizing_threshold_class_metric is not None:
break
if num_classes == 1:
break
# strategy = tf.distribute.MirroredStrategy()
# with strategy.scope():
model = Unet(input_shape=(None, None, 1),
classes=num_classes,
**model_parameters)
model.compile(optimizer=Adam(), loss=loss_fn, metrics=all_metrics)
if weights_to_load:
model.load_weights(weights_to_load)
if optimizing_threshold_class_metric is None:
print(model.summary())
return model
for w in words:
bag.append(1) if w in pattern_words else bag.append(0)
output_row = list(output_empty)
output_row[classes.index(doc[1])] = 1
training.append([bag, output_row])
random.shuffle(training)
training = np.array(training)
train_x = list(training[:,0])
train_y = list(training[:,1])
print("Training data created")
x_train, x_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.33)
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['acc', TruePositives(), TrueNegatives(), FalsePositives(), FalseNegatives()])
history = model.fit(np.array(x_train), np.array(y_train), validation_data=(x_test, y_test), epochs=300, batch_size=5, verbose=1)
model.save('chatbot_model.h5', history)
print("model created")
'id': [],
'rating': [],
'text': [],
'time_created': [],
'url': []
}
FULL_REVIEWS_DICT = {'rating': [], 'text': []}
'''
Modeling
'''
# VOCAB_SIZE = 10386
# VOCAB_SIZE = 9566
VOCAB_SIZE = 27886
METRICS = [
TruePositives(name='tp'),
FalsePositives(name='fp'),
TrueNegatives(name='tn'),
FalseNegatives(name='fn'),
BinaryAccuracy(name='accuracy'),
Precision(name='precision'),
Recall(name='recall'),
AUC(name='auc')
]
PARAMS = {'batch_size': '', 'epochs': ''}
PREDICTIONS = {}
def build_model():
#main input is the length of the amino acid in the protein sequence (700,)
main_input = Input(shape=(700, ), dtype='float32', name='main_input')
#Embedding Layer used as input to the neural network
embed = Embedding(output_dim=21, input_dim=21,
input_length=700)(main_input)
#secondary input is the protein profile features
auxiliary_input = Input(shape=(700, 21), name='aux_input')
#get shape of input layers
print("Protein Sequence shape: ", main_input.get_shape())
print("Protein Profile shape: ", auxiliary_input.get_shape())
#concatenate 2 input layers
concat = Concatenate(axis=-1)([embed, auxiliary_input])
#3x1D Convolutional Hidden Layers with BatchNormalization and MaxPooling
conv_layer1 = Convolution1D(64, 7, kernel_regularizer="l2",
padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer1)
conv2D_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.5)(conv2D_act)
max_pool_2D_1 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
conv_layer2 = Convolution1D(128, 7, padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer2)
conv2D_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.5)(conv2D_act)
max_pool_2D_2 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
conv_layer3 = Convolution1D(256,
7,
kernel_regularizer="l2",
padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer3)
conv2D_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.5)(conv2D_act)
max_pool_2D_3 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
#concatenate convolutional layers
conv_features = Concatenate(axis=-1)(
[max_pool_2D_1, max_pool_2D_2, max_pool_2D_3])
#Dense Fully-Connected DNN layers
dense_1 = Dense(300, activation='relu')(conv_features)
dense_1_dropout = Dropout(dense_dropout)(dense_1)
dense_2 = Dense(100, activation='relu')(dense_1_dropout)
dense_2_dropout = Dropout(dense_dropout)(dense_2)
dense_3 = Dense(50, activation='relu')(dense_2_dropout)
dense_3_dropout = Dropout(dense_dropout)(dense_3)
dense_4 = Dense(16, activation='relu')(dense_3_dropout)
dense_4_dropout = Dropout(dense_dropout)(dense_4)
#Final Dense layer with 8 nodes for the 8 output classifications
main_output = Dense(8, activation='softmax',
name='main_output')(protein_features_dropout)
#create model from inputs and outputs
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output])
#use Adam optimizer
adam = Adam(lr=lr)
#Adam is fast, but tends to over-fit
#SGD is low but gives great results, sometimes RMSProp works best, SWA can easily improve quality, AdaTune
#compile model using adam optimizer and the cateogorical crossentropy loss function
model.compile(optimizer=adam,
loss={'main_output': 'categorical_crossentropy'},
metrics=[
'accuracy',
MeanSquaredError(),
FalseNegatives(),
FalsePositives(),
TrueNegatives(),
TruePositives(),
MeanAbsoluteError(),
Recall(),
Precision()
])
model.summary()
#set earlyStopping and checkpoint callback
earlyStopping = EarlyStopping(monitor='val_loss',
patience=5,
verbose=1,
mode='min')
checkpoint_path = "/3x1DConv_dnn_" + str(datetime.date(
datetime.now())) + ".h5"
checkpointer = ModelCheckpoint(filepath=checkpoint_path,
verbose=1,
save_best_only=True,
monitor='val_acc',
mode='max')
return model
def build_model():
#main input is the length of the amino acid in the protein sequence (700,)
main_input = Input(shape=(700, ), dtype='float32', name='main_input')
#Embedding Layer used as input to the neural network
embed = Embedding(output_dim=21, input_dim=21,
input_length=700)(main_input)
#secondary input is the protein profile features
auxiliary_input = Input(shape=(700, 21), name='aux_input')
#auxiliary_input = Masking(mask_value=0)(auxiliary_input)
#get shape of input layers
print("Protein Sequence shape: ", main_input.get_shape())
print("Protein Profile shape: ", auxiliary_input.get_shape())
#concatenate 2 input layers
concat = Concatenate(axis=-1)([embed, auxiliary_input])
######## Recurrent Bi-Directional Long-Short-Term-Memory Layers ########
lstm_f1 = Bidirectional(
LSTM(400,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=0.5,
recurrent_dropout=0.5))(conv_features)
lstm_f2 = Bidirectional(
LSTM(300,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=0.5,
recurrent_dropout=0.5))(lstm_f1)
#concatenate LSTM with convolutional layers
concat_features = Concatenate(axis=-1)([lstm_f1, lstm_f2, conv_features])
concat_features = Dropout(0.4)(concat_features)
#Dense Fully-Connected DNN layers
dense_1 = Dense(300, activation='relu')(conv_features)
dense_1_dropout = Dropout(dense_dropout)(dense_1)
dense_2 = Dense(100, activation='relu')(dense_1_dropout)
dense_2_dropout = Dropout(dense_dropout)(dense_2)
dense_3 = Dense(50, activation='relu')(dense_2_dropout)
dense_3_dropout = Dropout(dense_dropout)(dense_3)
dense_4 = Dense(16, activation='relu')(dense_3_dropout)
dense_4_dropout = Dropout(dense_dropout)(dense_4)
#Final Dense layer with 8 nodes for the 8 output classifications
main_output = Dense(8, activation='softmax',
name='main_output')(dense_4_dropout)
#create model from inputs and outputs
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output])
#use Adam optimizer
adam = Adam(lr=0.0003)
#Adam is fast, but tends to over-fit
#SGD is low but gives great results, sometimes RMSProp works best, SWA can easily improve quality, AdaTune
#compile model using adam optimizer and the cateogorical crossentropy loss function
model.compile(optimizer=adam,
loss={'main_output': 'categorical_crossentropy'},
metrics=[
'accuracy',
MeanSquaredError(),
FalseNegatives(),
FalsePositives(),
TrueNegatives(),
TruePositives(),
MeanAbsoluteError(),
Recall(),
Precision()
])
model.summary()
#set earlyStopping and checkpoint callback
earlyStopping = EarlyStopping(monitor='val_loss',
patience=5,
verbose=1,
mode='min')
checkpoint_path = "/blstm_3x1Dconv_dnn_" + str(
datetime.date(datetime.now())) + ".h5"
checkpointer = ModelCheckpoint(filepath=checkpoint_path,
verbose=1,
save_best_only=True,
monitor='val_acc',
mode='max')
return model
def build_model():
"""
Description:
Building DCBGRU model
Args:
None
Returns:
None
"""
#main input is the length of the amino acid in the protein sequence (700,)
main_input = Input(shape=(700, ), dtype='float32', name='main_input')
#Embedding Layer used as input to the neural network
embed = Embedding(output_dim=21, input_dim=21,
input_length=700)(main_input)
#secondary input is the protein profile features
auxiliary_input = Input(shape=(700, 21), name='aux_input')
#get shape of input layers
print("Protein Sequence shape: ", main_input.get_shape())
print("Protein Profile shape: ", auxiliary_input.get_shape())
#concatenate 2 input layers
concat = Concatenate(axis=-1)([embed, auxiliary_input])
#3x1D Convolutional Hidden Layers with BatchNormalization, Dropout and MaxPooling
conv_layer1 = Conv1D(16, 7, kernel_regularizer="l2",
padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer1)
conv_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.2)(conv_act)
max_pool_1D_1 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
conv_layer2 = Conv1D(32, 7, padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer2)
conv_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.2)(conv_act)
max_pool_1D_2 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
conv_layer3 = Conv1D(64, 7, kernel_regularizer="l2",
padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer3)
conv_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.2)(conv_act)
max_pool_1D_3 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
############################################################################################
#concatenate convolutional layers
conv_features = Concatenate(axis=-1)(
[max_pool_1D_1, max_pool_1D_2, max_pool_1D_3])
#dense layer before GRU's
gru_dense = Dense(600, activation='relu',
name="after_cnn_dense")(conv_features)
######## Recurrent Unidirectional Long-Short-Term-Memory Layers ########
gru_f1 = Bidirectional(
GRU(200,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=0.5,
recurrent_dropout=0.5))(gru_dense)
gru_f2 = Bidirectional(
GRU(200,
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
dropout=0.5,
recurrent_dropout=0.5))(gru_f1)
############################################################################################
#concatenate GRU with convolutional layers
concat_features = Concatenate(axis=-1)([gru_f1, gru_f2, gru_dense])
concat_features = Dropout(0.4)(concat_features)
#Dense Fully-Connected DNN layers
after_gru_dense = Dense(600, activation='relu')(concat_features)
after_gru_dense_dropout = Dropout(0.3)(after_gru_dense)
#Final Dense layer with 8 nodes for the 8 output classifications
main_output = Dense(8, activation='softmax',
name='main_output')(after_gru_dense_dropout)
#create model from inputs and outputs
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output])
#use Adam optimizer
adam = Adam(lr=0.00015)
#compile model using adam optimizer and the cateogorical crossentropy loss function
model.compile(optimizer=adam,
loss={'main_output': 'categorical_crossentropy'},
metrics=[
'accuracy',
MeanSquaredError(),
FalseNegatives(),
FalsePositives(),
TrueNegatives(),
TruePositives(),
MeanAbsoluteError(),
Recall(),
Precision()
])
#print model summary
model.summary()
return model
def build_model():
"""
Description:
Building PSP-CD model
Args:
None
Returns:
None
"""
#main input is the length of the amino acid in the protein sequence (700,)
main_input = Input(shape=(700, ), dtype='float32', name='main_input')
#Embedding Layer used as input to the neural network
embed = Embedding(output_dim=21, input_dim=21,
input_length=700)(main_input)
#secondary input is the protein profile features
auxiliary_input = Input(shape=(700, 21), name='aux_input')
#get shape of input layers
print("Protein Sequence shape: ", main_input.get_shape())
print("Protein Profile shape: ", auxiliary_input.get_shape())
#concatenate 2 input layers
concat_features = Concatenate(axis=-1)([embed, auxiliary_input])
############################################################################################
#Dense Fully-Connected DNN layers
dense_1 = Dense(512, activation='relu')(concat_features)
dense_1_dropout = Dropout(0.3)(dense_1)
dense_2 = Dense(256, activation='relu')(dense_1_dropout)
dense_2_dropout = Dropout(0.3)(dense_2)
dense_3 = Dense(128, activation='relu')(dense_2_dropout)
dense_3_dropout = Dropout(0.3)(dense_3)
dense_4 = Dense(64, activation='relu')(dense_3_dropout)
dense_4_dropout = Dropout(0.3)(dense_4)
dense_5 = Dense(32, activation='relu')(dense_4_dropout)
dense_5_dropout = Dropout(0.3)(dense_5)
dense_6 = Dense(16, activation='relu')(dense_5_dropout)
dense_6_dropout = Dropout(0.3)(dense_6)
#Final Dense layer with 8 nodes for the 8 output classifications
main_output = Dense(8, activation='softmax',
name='main_output')(dense_6_dropout)
#create model from inputs and outputs
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output])
#use Adam optimizer
adam = Adam(lr=0.00015)
#compile model using adam optimizer and the cateogorical crossentropy loss function
model.compile(optimizer=adam,
loss={'main_output': 'categorical_crossentropy'},
metrics=[
'accuracy',
MeanSquaredError(),
FalseNegatives(),
FalsePositives(),
TrueNegatives(),
TruePositives(),
MeanAbsoluteError(),
Recall(),
Precision()
])
#print model summary
model.summary()
return model
def build_model():
"""
Description:
Building dummy model
Args:
None
Returns:
None
"""
print("Building dummy model....")
#main input is the length of the amino acid in the protein sequence (700,)
main_input = Input(shape=(700, ), dtype='float32', name='main_input')
#Embedding Layer used as input to the neural network
embed = Embedding(output_dim=21, input_dim=21,
input_length=700)(main_input)
#secondary input is the protein profile features
auxiliary_input = Input(shape=(700, 21), name='aux_input')
#get shape of input layers
print("Protein Sequence shape: ", main_input.get_shape())
print("Protein Profile shape: ", auxiliary_input.get_shape())
#concatenate 2 input layers
concat = Concatenate(axis=-1)([embed, auxiliary_input])
######## 1x1D-Convolutional Layers with BatchNormalization, Dropout and MaxPooling ########
conv_layer1 = Conv1D(16, 7, kernel_regularizer="l2",
padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer1)
# conv2D_act = activations.relu(batch_norm)
conv2D_act = ReLU()(batch_norm)
conv_dropout = Dropout(0.2)(conv2D_act)
############################################################################################
#Final Dense layer with 8 nodes for the 8 output classifications
main_output = Dense(8, activation='softmax',
name='main_output')(conv_dropout)
#create model from inputs and outputs
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output])
#use Adam optimizer
adam = Adam(lr=0.00015)
#compile model using adam optimizer and the cateogorical crossentropy loss function
model.compile(optimizer=adam,
loss={'main_output': 'categorical_crossentropy'},
metrics=[
'accuracy',
MeanSquaredError(),
FalseNegatives(),
FalsePositives(),
TrueNegatives(),
TruePositives(),
MeanAbsoluteError(),
Recall(),
Precision(),
AUC()
])
#print model summary
model.summary()
return model
def main():
args = cmd_parser()
if_fast_run = False
print(f"TensorFlow version: {tf.__version__}.")
print(f"Keras version: {keras.__version__}.")
print("If in eager mode: ", tf.executing_eagerly())
assert tf.__version__[0] == "2"
model_type = "three_conv2d_net"
# data path
competition_name = "dogs-vs-cats-redux-kernels-edition"
data_dir = os.path.expanduser(f"~/.kaggle/competitions/{competition_name}")
# experiment time
date_time = datetime.now().strftime("%Y%m%d-%H%M%S")
ckpt_dir = os.path.expanduser(
f"~/Documents/DeepLearningData/{competition_name}/ckpts/{model_type}/{date_time}"
)
log_dir = os.path.expanduser(
f"~/Documents/DeepLearningData/{competition_name}/logs/{model_type}/{date_time}"
)
makedir_exist_ok(ckpt_dir)
makedir_exist_ok(log_dir)
# Input parameters
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_SIZE = (IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS = 3
input_shape = (IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)
# Create model
metrics = [
TruePositives(name='tp'), # thresholds=0.5
FalsePositives(name='fp'),
TrueNegatives(name='tn'),
FalseNegatives(name='fn'),
BinaryAccuracy(name='accuracy'),
# AUC0(name='auc_cat_0'), # 以 good 为 positive 的 AUC
AUC(name='auc_dog_1') # 以 bad 为 positive 的 AUC
]
model = three_conv2d_net(input_shape=input_shape, metrics=metrics)
model.summary()
earlystop = EarlyStopping(patience=10)
learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',
patience=2,
verbose=1,
factor=0.5,
min_lr=0.00001)
model_name = "%s-epoch-{epoch:02d}-val_accuracy-{val_accuracy:.4f}.h5" % model_type
filepath = os.path.join(ckpt_dir, model_name)
checkpoint = ModelCheckpoint(filepath=filepath,
monitor="val_accuracy",
verbose=1,
period=1)
callbacks = [earlystop, learning_rate_reduction, checkpoint]
# Prepare DataFrame
filenames = os.listdir(os.path.join(data_dir, "train"))
random.shuffle(filenames)
categories = []
for filename in filenames:
category = filename.split('.')[0]
if category == 'dog':
categories.append(1)
elif category == 'cat':
categories.append(0)
df = pd.DataFrame({'filename': filenames, 'category': categories})
df["category"] = df["category"].replace({0: 'cat', 1: 'dog'})
# Show sample image
# sample = np.random.choice(filenames)
# image = load_img("./data/train/"+sample)
# plt.imshow(image)
# plt.show()
""" 这里用来自动划分 train 集和 val 集 """
train_df, validate_df = train_test_split(df,
test_size=0.20,
random_state=42)
train_df = train_df.reset_index(drop=True)
validate_df = validate_df.reset_index(drop=True)
# train_df['category'].value_counts().plot.bar()
total_train = train_df.shape[0]
total_validate = validate_df.shape[0]
classes = ["cat", "dog"]
# Traning Generator
train_datagen = ImageDataGenerator(rotation_range=15,
rescale=1. / 255,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
width_shift_range=0.1,
height_shift_range=0.1)
train_generator = train_datagen.flow_from_dataframe(
train_df,
os.path.join(data_dir, "train"),
x_col='filename',
y_col='category',
target_size=IMAGE_SIZE,
class_mode='categorical',
batch_size=args.batch_size)
# Validation Generator
validation_datagen = ImageDataGenerator(rescale=1. / 255)
validation_generator = validation_datagen.flow_from_dataframe(
validate_df,
os.path.join(data_dir, "train"),
x_col='filename',
y_col='category',
target_size=IMAGE_SIZE,
class_mode='categorical',
batch_size=args.batch_size)
# Example Generation
example_df = train_df.sample(n=1).reset_index(drop=True)
example_generator = train_datagen.flow_from_dataframe(
example_df,
os.path.join(data_dir, "train"),
x_col='filename',
y_col='category',
target_size=IMAGE_SIZE,
class_mode='categorical')
# Example Generation Ploting
# plt.figure(figsize=(12, 12))
# for i in range(0, 15):
# plt.subplot(5, 3, i+1)
# for X_batch, Y_batch in example_generator:
# image = X_batch[0]
# plt.imshow(image)
# break
# plt.tight_layout()
# plt.show()
# Fit model
epochs = 3 if if_fast_run else args.train_epochs
history = model.fit(train_generator,
epochs=epochs,
validation_data=validation_generator,
callbacks=callbacks,
initial_epoch=args.start_epoch)
# Save last model ckpt
model.save_weights(f"./{model_type}-last_ckpt.h5")
def build_model():
#main input is the length of the amino acid in the protein sequence (700,)
main_input = Input(shape=(700, ), dtype='float32', name='main_input')
#Embedding Layer used as input to the neural network
embed = Embedding(output_dim=21, input_dim=21,
input_length=700)(main_input)
#secondary input is the protein profile features
auxiliary_input = Input(shape=(700, 21), name='aux_input')
#get shape of input layers
print("Protein Sequence shape: ", main_input.get_shape())
print("Protein Profile shape: ", auxiliary_input.get_shape())
#concatenate 2 input layers
concat = Concatenate(axis=-1)([embed, auxiliary_input])
#3x1D Convolutional Hidden Layers with BatchNormalization and MaxPooling
conv_layer1 = Conv1D(64, 7, kernel_regularizer="l2",
padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer1)
conv2D_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.5)(conv2D_act)
# ave_pool_1 = AveragePooling1D(2, 1, padding='same')(conv_dropout)
max_pool_1D_1 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
conv_layer2 = Conv1D(128, 7, padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer2)
conv2D_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.5)(conv2D_act)
# ave_pool_2 = AveragePooling1D(2, 1, padding='same')(conv_dropout)
max_pool_1D_2 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
conv_layer3 = Conv1D(256, 7, kernel_regularizer="l2",
padding='same')(concat)
batch_norm = BatchNormalization()(conv_layer3)
conv2D_act = activations.relu(batch_norm)
conv_dropout = Dropout(0.5)(conv2D_act)
max_pool_1D_3 = MaxPooling1D(pool_size=2, strides=1,
padding='same')(conv_dropout)
# ave_pool_3 = AveragePooling1D(2, 1, padding='same')(conv_dropout)
#concatenate convolutional layers
conv_features = Concatenate(axis=-1)(
[max_pool_1D_1, max_pool_1D_2, max_pool_1D_3])
#output node is 1D convolutional layer with 8 filters for the 8 different categories
main_output = Conv1D(8,
7,
padding='same',
activation='softmax',
name='main_output')(conv_features)
#create model from inputs and outputs
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output])
#use Adam optimizer
adam = Adam(lr=0.0003)
#compile model using adam optimizer and the cateogorical crossentropy loss function
model.compile(optimizer=adam,
loss={'main_output': 'categorical_crossentropy'},
metrics=[
'accuracy',
MeanSquaredError(),
FalseNegatives(),
FalsePositives(),
TrueNegatives(),
TruePositives(),
MeanAbsoluteError(),
Recall(),
Precision()
])
model.summary()
#set earlyStopping and checkpoint callback
earlyStopping = EarlyStopping(monitor='val_loss',
patience=5,
verbose=1,
mode='min')
checkpoint_path = "checkpoints/3xConv_cnn_" + str(
datetime.date(datetime.now())) + ".h5"
checkpointer = ModelCheckpoint(filepath=checkpoint_path,
verbose=1,
save_best_only=True,
monitor='val_acc',
mode='max')
return model
output = Conv2DTranspose(filters=OUTPUT_CHANNELS,
kernel_size=[1, 1],
activation='sigmoid',
padding='same')(model)
model = Model(inputs, output)
print(model.summary())
model.compile(optimizer='adam',
loss=TverskyLoss(alpha=0.2, smooth=1e3),
metrics=[
'acc',
FalseNegatives(),
FalsePositives(),
TruePositives(),
TrueNegatives()
])
def show_predictions(dataset=None, num=1):
if dataset:
for image, mask in dataset.take(num):
pred_mask = model.predict(image)
display([image[0], mask[0], pred_mask[0]])
else:
display([
sample_image, sample_mask,
model.predict(sample_image[tf.newaxis, ...])[0]
],
with_colorbar=True)
train_dataloader = Dataloader(train_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=True)
valid_dataloader = Dataloader(valid_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=False)
loss = cfg.loss
optim = tf.keras.optimizers.SGD(lr=cfg.LR, momentum=0.9, nesterov=True)
metrics = [
IOUScore(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
FScore(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
Precision(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
Recall(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
TruePositives(thresholds=cfg.metric_threshold),
TrueNegatives(thresholds=cfg.metric_threshold),
FalsePositives(thresholds=cfg.metric_threshold),
FalseNegatives(thresholds=cfg.metric_threshold)
]
# with mirrored_strategy.scope():
model = sm.Unet(backbone_name=cfg.backbone_name,
input_shape=cfg.input_shape,
classes=n_classes,
activation='sigmoid' if n_classes == 1 else 'softmax',
weights=None,
encoder_weights=cfg.encoder_weights,
encoder_freeze=cfg.encoder_freeze,
encoder_features=cfg.encoder_features,
decoder_block_type=cfg.decoder_block_type,
def train_model(self, themes_weight: ThemeWeights,
dataset: TrainValidationDataset,
voc_size: int,
keras_callback: LambdaCallback):
conv_reg = 0#0.015
dense_reg = 0#0.015
dropout_conv = 0.1#0.2
dropout_dense = 0.1#0.2
article_length = dataset.article_length
theme_count = dataset.theme_count
input = keras.layers.Input(shape=(dataset.article_length,))
layer = keras.layers.Embedding(input_dim=voc_size, input_length=article_length, output_dim=self.embedding_output_dim,
mask_zero=True)(input)
layer = Dropout(dropout_conv)(layer)
# Each convolution will have filter looking for some specific word combinations. For example a filter might
# return a small value except for "apple iphone".
conv1 = keras.layers.Conv1D(filters=64, kernel_size=2, input_shape=(voc_size, self.embedding_output_dim),
activation=tf.nn.relu, kernel_regularizer=l2(l=conv_reg))(layer)
conv1 = keras.layers.GlobalMaxPooling1D()(conv1)
conv1 = keras.layers.Dense(32, activation=tf.nn.relu)(conv1)
conv1 = Dropout(dropout_conv)(conv1)
conv2 = keras.layers.Conv1D(filters=64, kernel_size=3, input_shape=(voc_size, self.embedding_output_dim),
activation=tf.nn.relu, kernel_regularizer=l2(l=conv_reg))(layer)
conv2 = keras.layers.GlobalMaxPooling1D()(conv2)
conv2 = keras.layers.Dense(32, activation=tf.nn.relu)(conv2)
conv2 = Dropout(dropout_conv)(conv2)
conv3 = keras.layers.Conv1D(filters=64, kernel_size=1, input_shape=(voc_size, self.embedding_output_dim),
activation=tf.nn.relu, kernel_regularizer=l2(l=conv_reg))(layer)
conv3 = keras.layers.GlobalMaxPooling1D()(conv3)
conv3 = keras.layers.Dense(32, activation=tf.nn.relu)(conv3)
conv3 = Dropout(dropout_conv)(conv3)
conv4 = keras.layers.Conv1D(filters=40, kernel_size=5, input_shape=(voc_size, self.embedding_output_dim),
activation=tf.nn.relu, kernel_regularizer=l2(l=conv_reg))(layer)
conv4 = keras.layers.GlobalMaxPooling1D()(conv4)
conv4 = keras.layers.Dense(20, activation=tf.nn.relu)(conv4)
conv4 = Dropout(dropout_conv)(conv4)
layer = keras.layers.Concatenate()([conv1, conv2, conv3, conv4])
layer = keras.layers.Dense(32, activation=tf.nn.relu, kernel_regularizer=l2(l=conv_reg))(layer)
layer = keras.layers.Dropout(dropout_dense)(layer)
# layer = keras.layers.Dense(64, activation=tf.nn.relu, kernel_regularizer=l2(l=conv_reg))(layer)
# layer = keras.layers.Dropout(dropout_dense)(layer)
layer = keras.layers.Dense(theme_count, activation=tf.nn.sigmoid, kernel_regularizer=l2(l=dense_reg))(layer)
model = keras.Model(inputs=input, outputs=layer)
model.compile(optimizer=tf.keras.optimizers.Adam(clipnorm=1, learning_rate=0.00009),
loss=WeightedBinaryCrossEntropy(themes_weight.weight_array()),
metrics=[AUC(multi_label=True), BinaryAccuracy(), TruePositives(),
TrueNegatives(), FalseNegatives(), FalsePositives(),
Recall(), Precision()],
run_eagerly=None)
model.summary()
self._model = model
if self.__plot_directory is not None:
self.plot_model(self.__plot_directory)
# Fix for https://github.com/tensorflow/tensorflow/issues/38988
model._layers = [layer for layer in model._layers if not isinstance(layer, dict)]
callbacks = [ManualInterrupter(), keras_callback]
model.fit(dataset.trainData,
epochs=self.epochs,
steps_per_epoch=dataset.train_batch_count,
validation_data=dataset.validationData,
validation_steps=dataset.validation_batch_count,
callbacks=callbacks,
)
class TrainingController:
""" Custom training loop with custom loss.
"""
def __init__(self,
model,
optimizer,
log_file_dir=None,
data_properties=None):
""" Init. method.
:param log_file_dir: If this is not None, then the training performance is stored in that log file directory.
:param model: The model used for training.
:param optimizer: Optimizer to be used for the weight updates.
"""
self._data_properties = data_properties
self._log_file_dir = log_file_dir
self._optimizer = optimizer
self._model = model
self._tp_obj = TruePositives()
self._tn_obj = TrueNegatives()
self._fp_obj = FalsePositives()
self._fn_obj = FalseNegatives()
self._pre_obj = Precision()
self._rec_obj = Recall()
self._setup_changes = {'train': [], 'valid': []}
self._loss_tt = {'train': [], 'valid': []}
self._loss_ms = {'train': [], 'valid': []}
self._loss_total = {'train': [], 'valid': []}
self._acc = {'train': [], 'valid': []}
self._tn = {'train': [], 'valid': []}
self._tp = {'train': [], 'valid': []}
self._fn = {'train': [], 'valid': []}
self._fp = {'train': [], 'valid': []}
self._rec = {'train': [], 'valid': []}
self._pre = {'train': [], 'valid': []}
def _tb_update(self,
grads,
y_true,
y_pred,
m_idx,
emb,
attn,
epoch,
batch,
batch_size,
prefix='train/'):
step = epoch * batch_size + batch
if grads is not None:
for var, grad in zip(self._model.trainable_variables, grads):
tf.summary.histogram(prefix + var.name + '/gradient',
grad,
step=step)
if attn is not None:
self._plot_attention_weights(
attention=attn,
step=step,
prefix=prefix + 'layer{}/enc_pad/'.format(0),
description='x: input jobs | y: output jobs')
m_idx = tf.tile(m_idx[:, :, tf.newaxis, tf.newaxis],
[1, 1, tf.shape(m_idx)[1], 1])
tf.summary.image(prefix + "selected_machine", m_idx, step=step)
for var in self._model.trainable_variables:
tf.summary.histogram(prefix + var.name + '/weight', var, step=step)
for m in tf.range(tf.shape(y_true)[1]):
tf.summary.image(prefix + "y_true_m{}".format(m),
tf.expand_dims(y_true[:, m, :, :], -1),
step=step)
tf.summary.image(prefix + "y_pred_m{}".format(m),
tf.expand_dims(y_pred[:, m, :, :], -1),
step=step)
@staticmethod
def _plot_attention_weights(attention,
step,
description='x: input, y: output',
prefix='train/'):
for head in range(attention.shape[1]):
data = []
for attn_matrix in tf.unstack(attention, axis=0):
attn_matrix = attn_matrix.numpy()
cmap = cm.get_cmap('Greens')
norm = Normalize(vmin=attn_matrix.min(),
vmax=attn_matrix.max())
data.append(cmap(norm(attn_matrix)))
tf.summary.image(prefix + "head{}".format(head),
np.array(data, np.float32)[:, head, :, :, :],
step=step,
description=description)
def train(self,
train_data,
val_data=None,
epochs=1,
steps_per_epoch=100,
checkpoint_path=None,
validation_steps=10):
""" Custom training loop with custom loss.
:param train_data: training data set
:param val_data: validation data set
:param epochs: number of training epochs
:param steps_per_epoch: steps per epochs (required if generator used).
If set to None, the number will be computed automatically.
:param checkpoint_path: save checkpoints epoch-wise if directory provided.
:param validation_steps:
:return accumulated loss and accuracy
"""
log_path = self._log_file_dir + '/' + datetime.now().strftime(
"%y%m%d-%H%M%S")
Path(log_path).parent.mkdir(parents=True, exist_ok=True)
writer = tf.summary.create_file_writer(log_path)
writer.set_as_default()
for step in tf.range(steps_per_epoch * epochs, dtype=tf.int64):
tf.summary.scalar('learning_rate',
self._optimizer.lr(tf.cast(step, tf.float32)),
step=step)
for epoch in range(epochs):
for batch, (x, y_true) in enumerate(train_data):
if batch == 0:
self._target_shape = x.shape
if batch >= steps_per_epoch:
break
loss_total, grads, y_pred, m, emb, attn = iterative_optimize(
optimizer=self._optimizer,
model=self._model,
x=x,
y_true=y_true,
data_properties=self._data_properties,
training=True)
loss_tt = lateness(x, y_pred)
loss_ms = makespan(x, y_pred)
setup_changes = count_setup_changes(x, y_pred)
self._update_metric('train', y_true, y_pred,
(loss_tt, loss_ms, loss_total),
setup_changes, batch)
self._print('train', epoch, epochs, batch, steps_per_epoch)
if batch % TB_LOG_INV_FREQ == 0:
self._tb_update(grads, y_true, y_pred, m, emb, attn, epoch,
batch, steps_per_epoch, 'train/')
self._log('train', epoch * steps_per_epoch + batch)
self._validation_loop(val_data, validation_steps, epoch)
self._empty_metric()
if checkpoint_path and (epoch % CKPT_SAVE_INV_FREQ == 0):
Path(checkpoint_path).parent.mkdir(parents=True, exist_ok=True)
self._model.save_weights(checkpoint_path.format(epoch=epoch))
writer.close()
def _empty_metric(self):
""" This will empty all metric dict, to avoid memory overflows.
"""
for key in ['train', 'valid']:
self._loss_tt.get(key).clear()
self._loss_ms.get(key).clear()
self._loss_total.get(key).clear()
self._acc.get(key).clear()
self._setup_changes.get(key).clear()
self._tp_obj.reset_states()
self._tp.get(key).clear()
self._tn_obj.reset_states()
self._tn.get(key).clear()
self._fp_obj.reset_states()
self._fp.get(key).clear()
self._fn_obj.reset_states()
self._fn.get(key).clear()
self._pre_obj.reset_states()
self._pre.get(key).clear()
self._rec_obj.reset_states()
self._rec.get(key).clear()
def _print(self, key: str, epoch: int, epochs_max: int, batch: int,
batch_max: int):
""" Prints the performance results in the console.
:param key:
:param epoch:
:param epochs_max:
:param batch:
:param batch_max:
"""
mean_loss = tf.reduce_mean(self._loss_total.get(key))
mean_acc = tf.reduce_mean(self._acc.get(key))
mean_pre = tf.reduce_mean(self._pre.get(key))
mean_rec = tf.reduce_mean(self._rec.get(key))
if key == 'train':
tf.print(
'\r[Train] [E {0}/{1}] [B {2}/{3}] Loss: {4} Acc: {5} Pre: {6} Rec: {7}'
.format(epoch + 1, epochs_max, batch + 1, batch_max, mean_loss,
mean_acc, mean_pre, mean_rec),
end='')
else:
tf.print(
'\n[Valid] [E {0}/{1}] [B {2}/{3}] Loss: {4} Acc: {5} Pre: {6} Rec: {7}\n'
.format(epoch, epochs_max, batch + 1, batch_max, mean_loss,
mean_acc, mean_pre, mean_rec))
def _validation_loop(self, validation_data, validation_steps: int,
epoch: int):
""" Looping through the validation set and ouputs the validation performance
results in a final step.
:param validation_data:
:param validation_steps:
"""
for batch, (x, y_true) in enumerate(validation_data):
if batch >= validation_steps:
break
optimizer = optimizer,
loss_total, grads, y_pred, m, emb, attn = iterative_optimize(
optimizer=self._optimizer,
model=self._model,
x=x,
y_true=y_true,
data_properties=self._data_properties,
training=False)
loss_tt = lateness(x, y_pred)
loss_ms = makespan(x, y_pred)
setup_changes = count_setup_changes(x, y_pred)
self._update_metric('valid', y_true, y_pred,
(loss_tt, loss_ms, loss_total), setup_changes,
batch)
if batch % (TB_LOG_INV_FREQ * 0.1) == 0:
self._tb_update(None, y_true, y_pred, m, emb, attn, epoch,
batch, validation_steps, 'valid/')
self._log('valid', epoch * validation_steps + batch)
self._print('valid', 0, 0, validation_steps - 1, validation_steps)
def _update_metric(self,
key: str,
y_true: tf.Tensor,
y_pred: tf.Tensor,
loss: tuple,
setup_changes: tf.Tensor,
step=0):
""" Updates the metrics.
:param key:
:param y_true:
:param y_pred:
:param loss:
:param grads:
"""
loss_tt, loss_ms, loss_total = loss
self._loss_tt.get(key).append(loss_tt)
self._loss_ms.get(key).append(loss_ms)
self._loss_total.get(key).append(loss_total)
self._setup_changes.get(key).append(setup_changes)
self._tp_obj.update_state(y_true, y_pred)
self._tp.get(key).append(self._tp_obj.result())
self._tn_obj.update_state(y_true, y_pred)
self._tn.get(key).append(self._tn_obj.result())
self._fp_obj.update_state(y_true, y_pred)
self._fp.get(key).append(self._fp_obj.result())
self._fn_obj.update_state(y_true, y_pred)
self._fn.get(key).append(self._fn_obj.result())
self._pre_obj.update_state(y_true, y_pred)
self._pre.get(key).append(self._pre_obj.result())
self._rec_obj.update_state(y_true, y_pred)
self._rec.get(key).append(self._rec_obj.result())
shape = tf.shape(y_true)
y_true = tf.squeeze(tf.transpose(y_true, [0, 2, 1, 3]))
y_pred = tf.squeeze(tf.transpose(y_pred, [0, 2, 1, 3]))
y_pred = tf.reshape(y_pred, [shape[0], shape[2], -1])
y_true = tf.reshape(y_true, [shape[0], shape[2], -1])
self._acc.get(key).append(categorical_accuracy(y_true, y_pred))
def _log(self, key: str, epoch: int):
""" Logs the training progress in a log file. If the log file dir parameter is set.
:param key:
:param epoch:
"""
if not self._log_file_dir:
return
if not os.path.exists(self._log_file_dir):
os.mkdir(self._log_file_dir)
tf.summary.scalar(key + '/tardiness',
data=tf.reduce_mean(self._loss_ms.get(key)),
step=epoch)
tf.summary.scalar(key + '/makespan',
data=tf.reduce_mean(self._loss_tt.get(key)),
step=epoch)
tf.summary.scalar(key + '/loss',
data=tf.reduce_mean(self._loss_total.get(key)),
step=epoch)
tf.summary.scalar(key + '/acc',
data=tf.reduce_mean(self._acc.get(key)),
step=epoch)
tf.summary.scalar(key + '/setup_changes',
data=tf.reduce_mean(self._setup_changes.get(key)),
step=epoch)
tf.summary.scalar(key + '/tp',
data=tf.reduce_mean(self._tp.get(key)),
step=epoch)
tf.summary.scalar(key + '/fp',
data=tf.reduce_mean(self._fp.get(key)),
step=epoch)
tf.summary.scalar(key + '/tn',
data=tf.reduce_mean(self._tn.get(key)),
step=epoch)
tf.summary.scalar(key + '/fn',
data=tf.reduce_mean(self._fn.get(key)),
step=epoch)
tf.summary.scalar(key + '/pre',
data=tf.reduce_mean(self._pre.get(key)),
step=epoch)
tf.summary.scalar(key + '/rec',
data=tf.reduce_mean(self._rec.get(key)),
step=epoch)