diff --git a/.gitignore b/.gitignore
index 25cf9a4..98ff3cf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
+# Specific locations
+**/playground/**
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/src/object_condensation/tensorflow/object_condensation.py b/src/object_condensation/tensorflow/object_condensation.py
new file mode 100644
index 0000000..d08cb41
--- /dev/null
+++ b/src/object_condensation/tensorflow/object_condensation.py
@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+import tensorflow as tf
+
+
+def calculate_losses(
+        q_min: float,
+        object_id: tf.Tensor,
+        beta: tf.Tensor,
+        x: tf.Tensor,
+        weights: tf.Tensor=None,
+        noise_threshold: int=-1) -> dict[str, tf.Tensor]:
+    """
+    Calculate the object condensation loss
+    """
+    if weights is None:
+        weights = tf.ones_like(beta)
+
+    not_noise = object_id > noise_threshold
+    unique_oids, _ = tf.unique(object_id[not_noise])
+    q = tf.tanh(beta) ** 2 + q_min
+    mask_att = tf.cast(object_id[:, None] == unique_oids[None, :], tf.float32)
+    mask_rep = tf.cast(object_id[:, None] != unique_oids[None, :], tf.float32)
+    alphas = tf.argmax(beta * mask_att, axis=0)
+    beta_k = tf.gather(beta, alphas)
+    q_k = tf.gather(q, alphas)
+    x_k = tf.gather(x, alphas)
+    dist_j_k = tf.norm(x[None, :, :] - x_k[:, None, :], axis=-1)
+
+    v_att_k = tf.math.divide_no_nan(
+        tf.reduce_sum(
+            q_k
+            * tf.transpose(weights)
+            * tf.transpose(q)
+            * tf.transpose(mask_att)
+            * dist_j_k**2,
+            axis=1,
+        ),
+        tf.reduce_sum(mask_att, axis=0) + 1e-3,
+    )
+    v_att = tf.divide_no_nan(
+        tf.reduce_sum(v_att_k), tf.cast(tf.shape(unique_oids)[0] - 1.0, tf.float32)
+    )
+
+    v_rep_k = tf.math.divide_no_nan(
+        tf.reduce_sum(
+            q_k
+            * tf.transpose(weights)
+            * tf.transpose(q)
+            * tf.transpose(mask_rep)
+            * tf.math.maximum(0, 1.0 - dist_j_k),
+            axis=1,
+        ),
+        tf.reduce_sum(mask_rep, axis=0) + 1e-3,
+    )
+
+    v_rep = tf.divide_no_nan(
+        tf.reduce_sum(v_rep_k), tf.cast(tf.shape(unique_oids)[0] - 1.0, tf.float32)
+    )
+
+    noise_loss_k = 1.0 - beta_k
+    noise_loss = tf.divide_no_nan(
+        tf.reduce_sum(noise_loss_k[1:]),
+        tf.cast(tf.shape(unique_oids)[0] - 1.0, tf.float32),
+    )
+
+    coward_loss = tf.math.divide_no_nan(
+        tf.reduce_sum(beta[object_id == -1]),
+        tf.reduce_sum(tf.cast(object_id == -1, tf.float32)),
+    )
+
+    return {
+        "v_att": v_att,
+        "v_rep": v_rep,
+        "L_beta": noise_loss,
+        "L_noise": coward_loss,
+    }