Upload cnn.py

cnn.py

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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
+