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model.py

@@ -0,0 +1,80 @@
+import tensorflow as tf
+
+def mynet(inp, reuse=False):
+    with tf.variable_scope("model"):
+        print(inp.shape)
+        with tf.variable_scope("conv1") as scope:
+            net = tf.contrib.layers.conv2d(inp, 32, 15, 
+                                        stride=1,
+                                        activation_fn=tf.nn.relu, 
+                                        padding='VALID',
+                                        weights_initializer=tf.keras.initializers.he_normal(),
+                                        scope=scope,
+                                        reuse=reuse)
+
+            print(net.shape)
+
+            net = tf.contrib.layers.max_pool2d(net, kernel_size=2, stride=2)
+
+            net = tf.contrib.layers.batch_norm(net, reuse=reuse, scope=scope)
+
+            print(net.shape)
+
+
+        with tf.variable_scope("conv2") as scope:
+            net = tf.contrib.layers.conv2d(net, 64, 8,
+                                        stride=1,
+                                        activation_fn=tf.nn.relu, 
+                                        padding='VALID',
+                                        weights_initializer=tf.keras.initializers.he_normal(),
+                                        scope=scope,
+                                        reuse=reuse)
+
+            print(net.shape)
+
+            net = tf.contrib.layers.max_pool2d(net, kernel_size=3, stride=3)
+
+            net = tf.contrib.layers.batch_norm(net, reuse=reuse, scope=scope)
+
+            print(net.shape)
+
+
+        with tf.variable_scope("conv3") as scope:
+            net = tf.contrib.layers.conv2d(net, 256, 5, 
+                                        stride=1,
+                                        activation_fn=tf.nn.relu,
+                                        padding='VALID',
+                                        weights_initializer=tf.keras.initializers.he_normal(),
+                                        scope=scope,
+                                        reuse=reuse)
+
+            print(net.shape)
+
+            net = tf.contrib.layers.max_pool2d(net, kernel_size=2, stride=2)
+
+            net = tf.contrib.layers.batch_norm(net, reuse=reuse, scope=scope)
+
+            print(net.shape)
+
+
+        with tf.variable_scope("fc1") as scope:
+            net = tf.contrib.layers.flatten(net)
+            print(net.shape)
+
+        with tf.variable_scope("fc2") as scope:
+            net = tf.contrib.layers.fully_connected(net, 64, 
+                                                    activation_fn=tf.nn.relu, 
+                                                    reuse=reuse, 
+                                                    scope=scope)
+            print(net.shape)
+
+
+    return net
+
+
+def contrastive_loss(model1, model2, y, margin):
+    with tf.name_scope("contrastive-loss"):
+        d = tf.sqrt(tf.reduce_sum(tf.pow(model1-model2, 2), 1, keepdims=True))
+        tmp = y * tf.square(d)    
+        tmp2 = (1 - y) * tf.square(tf.maximum((margin - d),0))
+    return tf.reduce_mean(tmp + tmp2)/2