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from tensorflow.keras import Model as KerasModel
from tensorflow.keras import layers
import tensorflow.keras as keras
from tensorflow.keras.regularizers import l2
from modellib.losses import weighted_binary_crossentropy
class Baseline(KerasModel):
def __init__(self, input_shape):
super(Baseline, self).__init__()
self._input_shape = input_shape
# First bottleneck unit
self.bn1 = layers.BatchNormalization()
self.activation1 = layers.Activation('selu')
self.conv1 = layers.Conv2D(32, kernel_size=(5, 5), padding='same', kernel_regularizer=l2(0.02))
self.bn2 = layers.BatchNormalization()
self.activation2 = layers.Activation('selu')
self.conv2 = layers.Conv2D(128, kernel_size=(3, 3), padding='same', kernel_regularizer=l2(0.02))
# Corner detection
self.bn3 = layers.BatchNormalization()
self.padding = layers.ZeroPadding2D(padding=(0, 3))
self.conv3 = layers.Conv2D(32, kernel_size=(21, 7), padding='valid', activation='tanh')
self.conv4 = layers.Conv2D(128, kernel_size=(1, 3), padding='same', activation='tanh')
# Fully-connected predictor
self.flat = layers.Flatten()
self.classify = layers.Dense(512, activation='sigmoid')
self.dropout = layers.Dropout(0.1)
self.result = layers.Dense(input_shape[1], activation='sigmoid')
def build(self, input_shape):
super(Baseline, self).build(input_shape)
self.built = True
def call(self, inputs, training=False):
# First bottleneck unit
x = self.bn1(inputs, training=training)
x = self.activation1(x)
x = self.conv1(x)
x = self.bn2(x, training=training)
x = self.activation2(x)
x = self.conv2(x)
merged = layers.add([inputs, x])
# Corner detection
x = self.bn3(merged, training=training)
x = self.padding(x)
x = self.conv3(x)
x = self.conv4(x)
# Fully-connected predictor
x = self.flat(x)
x = self.classify(x)
x = self.dropout(x, training=training)
x = self.result(x)
return x
def build_graph(self):
x = layers.Input(shape=self.input_shape)
return KerasModel(inputs=[x], outputs=self.call(x))
# Function to create the model and compile it with the custom loss function
def create_compile_model_custom_loss(input_shape, optimizer, class_weights, metrics=False):
model = Baseline(input_shape)
model.build(input_shape=(None,) + input_shape)
# Compile with custom loss function
model.compile(
loss=lambda y_true, y_pred: weighted_binary_crossentropy(y_true, y_pred, class_weights),
optimizer=optimizer,
metrics=metrics
)
return model
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