diff --git a/docs/assets/performance-comparison.jpg b/docs/assets/performance-comparison.jpg
new file mode 100644
index 00000000..382f7ab6
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diff --git a/src/TensorFlowNET.Core/Gradients/Tape.CallBackwardFunction.cs b/src/TensorFlowNET.Core/Gradients/Tape.CallBackwardFunction.cs
new file mode 100644
index 00000000..f4908c71
--- /dev/null
+++ b/src/TensorFlowNET.Core/Gradients/Tape.CallBackwardFunction.cs
@@ -0,0 +1,22 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Eager;
+using static Tensorflow.tensorflow;
+
+namespace Tensorflow.Gradients
+{
+    public partial class Tape
+    {
+        public Tensor[] CallBackwardFunction(BackwardFunction backward_function,
+            List<long> unneeded_gradients,
+            List<Tensor> output_gradients)
+        {
+            var grads = new Tensor[output_gradients.Count];
+            var result = backward_function(output_gradients.ToArray(), 
+                unneeded_gradients.ToArray());
+
+            return result;
+        }
+    }
+}
diff --git a/src/TensorFlowNET.Core/Gradients/Tape.ComputeGradient.cs b/src/TensorFlowNET.Core/Gradients/Tape.ComputeGradient.cs
new file mode 100644
index 00000000..94e0d3ee
--- /dev/null
+++ b/src/TensorFlowNET.Core/Gradients/Tape.ComputeGradient.cs
@@ -0,0 +1,249 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using Tensorflow.Util;
+using static Tensorflow.tensorflow;
+
+namespace Tensorflow.Gradients
+{
+    public partial class Tape
+    {
+        int kMinAggregateCount = 4;
+        int kMinAggregateBytes = 128 * 1024 * 1024;
+
+        public Tensor[] ComputeGradient(long[] target_tensor_ids,
+            long[] source_tensor_ids,
+            UnorderedMap<long, TapeTensor> sources_that_are_targets,
+            Tensor[] output_gradients)
+        {
+            var result = new List<Tensor>(source_tensor_ids.Length);
+            var sources_set = new UnorderedSet<long>(source_tensor_ids);
+            var gradients_size = new UnorderedMap<long, long>();
+
+            var state = PrepareBackprop(
+                target_tensor_ids, tensor_tape_, op_tape_, sources_set, persistent_);
+            var op_stack = InitialStack(state.op_tape, state.op_missing_tensor);
+            var gradients = InitialGradients(target_tensor_ids, sources_that_are_targets,
+                output_gradients,
+                tensor_tape_,
+                state.op_tape);
+
+            while (op_stack.Count > 0)
+            {
+                var op = op_stack.Dequeue();
+                if (!state.op_tape.find(op, out var trace))
+                    continue;
+
+                state.op_tape.erase(op);
+
+                var out_gradients = new List<Tensor>(trace.output_tensor_info.Length);
+                var unneeded_gradients = new List<long>();
+                for (int i = 0; i < trace.input_tensor_id.Length; i++)
+                {
+                    var in_tensor_id = trace.input_tensor_id[i];
+                    if (!tensor_tape_.find(in_tensor_id) &&
+                        !sources_set.find(in_tensor_id))
+                        unneeded_gradients.Add(i);
+                }
+
+                bool any_gradient_nonzero = false;
+                var zero_indices = new List<int>();
+                for (int i = 0; i < trace.output_tensor_info.Length; ++i)
+                {
+                    var id = trace.output_tensor_info[i].GetID();
+                    if (!gradients.find(id, out var grad_it))
+                    {
+                        throw new NotImplementedException("FunctionsAcceptingNoneForIndicesMap");
+                    }
+                    else
+                    {
+                        any_gradient_nonzero = true;
+                        var new_gradients = grad_it.Count == 1 ? 
+                            grad_it[0] :
+                            gen_math_ops.add_n(grad_it.ToArray()); // vspace.AggregateGradients
+
+                        if (!sources_set.find(id))
+                            gradients.Remove(id);
+                        else
+                        {
+                            grad_it.Clear();
+                            grad_it.Add(new_gradients);
+                            // vspace.MarkAsResult(new_gradients);
+                        }
+                        out_gradients.Add(new_gradients);
+                    }
+                }
+
+                Tensor[] in_gradients;
+                if (any_gradient_nonzero)
+                {
+                    foreach (var i in zero_indices)
+                        out_gradients[i] = trace.output_tensor_info[i].ZerosLike();
+
+                    in_gradients = CallBackwardFunction(trace.backward_function, 
+                        unneeded_gradients, 
+                        out_gradients);
+
+                    if (in_gradients.Count() != trace.input_tensor_id.Count())
+                        throw new RuntimeError($"Recorded operation '{trace.op_type}' returned too few gradients. Expected {trace.input_tensor_id.Length} but received {in_gradients.Count()}");
+                    if (!persistent_)
+                    {
+                        // trace.backward_function_deleter(trace.backward_function);
+                    }
+                }
+                else
+                {
+                    throw new NotImplementedException("");
+                }
+
+                for (int i = 0; i < in_gradients.Length; ++i)
+                {
+                    var id = trace.input_tensor_id[i];
+                    if (in_gradients[i] != null)
+                    {
+                        var unaggregated_grads = gradients[id];
+                        unaggregated_grads.Add(in_gradients[i]);
+                        if(unaggregated_grads.Count > kMinAggregateCount)
+                        {
+                            if(!gradients_size.ContainsKey(id))
+                            {
+                            }
+                            else
+                            {
+
+                            }
+
+                            throw new NotImplementedException("");
+                        }
+                    }
+
+                    if (!state.tensor_usage_counts.find(id))
+                        continue;
+
+                    state.tensor_usage_counts[id]--;
+                    if (state.tensor_usage_counts[id] > 0)
+                        continue;
+
+                    if (!tensor_tape_.find(id, out var tape_it))
+                    {
+                        if (gradients.find(id, out var grad_it))
+                        {
+                            // foreach (var g in grad_it)
+                                // DeleteGradient(g);
+                            gradients.erase(id);
+                        }
+                        continue;
+                    }
+
+                    var op_id = tape_it;
+                    if (op_id == -1)
+                        continue;
+
+                    if(state.op_missing_tensor.find(op_id, out var missing_it))
+                    {
+                        state.op_missing_tensor[op_id]--;
+                        if (state.op_missing_tensor[op_id] == 0)
+                            op_stack.Enqueue(op_id);
+                    }
+                }
+            }
+
+            if (state.op_tape.Count > 0)
+                throw new RuntimeError("Invalid tape state.");
+
+            var used_gradient_ids = new List<long>(source_tensor_ids.Length);
+            foreach (var id in source_tensor_ids)
+            {
+                if (!gradients.find(id, out var grad_it))
+                    result.Add(null);
+                else
+                {
+                    if(grad_it.Count > 1)
+                    {
+                        var grad = gen_math_ops.add_n(grad_it.ToArray());
+                        grad_it.Clear();
+                        grad_it.Add(grad);
+                    }
+                    result.Add(grad_it[0]);
+                    used_gradient_ids.Add(id);
+                }
+            }
+
+            /*foreach(var grad_pair in gradients)
+            {
+                if(!used_gradient_ids.Contains(grad_pair.Key))
+                {
+                    foreach(var g in grad_pair.Value)
+                    {
+                        vspace.DeleteGradient(g);
+                    }
+                }
+            }*/
+
+            return result.ToArray();
+        }
+
+        UnorderedMapEnumerable<long, List<Tensor>> InitialGradients(long[] target_tensor_ids,
+            UnorderedMap<long, TapeTensor> sources_that_are_targets,
+            Tensor[] output_gradients,
+            TensorTape tensor_tape,
+            OpTape<BackwardFunction, TapeTensor> op_tape)
+        {
+            var result = new UnorderedMapEnumerable<long, List<Tensor>>();
+            for (int i = 0; i < target_tensor_ids.Length; ++i)
+            {
+                var id = target_tensor_ids[i];
+                if (output_gradients.Length == 0 || output_gradients[i] == null)
+                {
+                    if (tensor_tape.find(id, out var tensor_id) && tensor_id != -1)
+                    {
+                        if (!op_tape.find(tensor_tape[id], out var op_it))
+                            throw new RuntimeError("Internal state of the gradient tape is invalid: " +
+                                "failed to find operation producing a tensor");
+                        bool found = false;
+                        for (int j = 0; j < op_it.output_tensor_info.Length; ++j)
+                        {
+                            if (op_it.output_tensor_info[j].GetID() == id)
+                            {
+                                found = true;
+                                var ones = op_it.output_tensor_info[j].OnesLike();
+                                result[id].Add(ones);
+                                break;
+                            }
+                        }
+
+                        if (!found)
+                        {
+                            throw new ValueError("Internal state of the gradient tape is invalid: " +
+                                "none of operations outputs match expected tensor");
+                        }
+                    }
+                    else
+                    {
+                        if (sources_that_are_targets.find(id, out var source_tensor))
+                            result[id].Add(source_tensor.OnesLike());
+                    }
+                }
+                else
+                {
+                    result[id].Add(output_gradients[i]);
+                }
+            }
+
+            return result;
+        }
+
+        Queue<long> InitialStack(OpTape<BackwardFunction, TapeTensor> op_tape,
+            UnorderedMap<long, long> op_missing_tensor)
+        {
+            var result = new Queue<long>();
+            foreach(var op_entry in op_tape)
+            {
+                if (!op_missing_tensor.find(op_entry.Key))
+                    result.Enqueue(op_entry.Key);
+            }
+            return result;
+        }
+    }
+}
diff --git a/src/TensorFlowNET.Core/Gradients/Tape.PrepareBackprop.cs b/src/TensorFlowNET.Core/Gradients/Tape.PrepareBackprop.cs
new file mode 100644
index 00000000..55e6d7e4
--- /dev/null
+++ b/src/TensorFlowNET.Core/Gradients/Tape.PrepareBackprop.cs
@@ -0,0 +1,72 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using Tensorflow.Util;
+using static Tensorflow.tensorflow;
+
+namespace Tensorflow.Gradients
+{
+    public partial class Tape
+    {
+        public BackpropInitialState PrepareBackprop(long[] target,
+            TensorTape tensor_tape,
+            OpTape<BackwardFunction, TapeTensor> op_tape,
+            UnorderedSet<long> sources_set,
+            bool persistent_tape)
+        {
+            BackpropInitialState result = new BackpropInitialState();
+            var tensor_stack = new Queue<long>(target);
+            while (tensor_stack.Count > 0)
+            {
+                var tensor_id = tensor_stack.Dequeue();
+
+                if (!tensor_tape.find(tensor_id, out var op_id))
+                    continue;
+
+                if (op_id == -1 || 
+                    !op_tape.find(op_id, out var op_it) ||
+                    result.op_tape.find(op_id, out var result_op_it))
+                    continue;
+
+                result.op_tape.emplace(op_id, op_it);
+
+                foreach (var it in op_it.input_tensor_id)
+                {
+                    if(result.tensor_usage_counts.find(it))
+                        result.tensor_usage_counts[it]++;
+                    else
+                    {
+                        result.tensor_usage_counts[it] = 1;
+                        if (tensor_tape.find(it))
+                            tensor_stack.Enqueue(it);
+                    }
+                }
+
+                if (!persistent_tape)
+                    op_tape.Remove(op_id);
+            }
+
+            foreach (var pair in result.tensor_usage_counts)
+            {
+                if (tensor_tape.find(pair.Key, out var it) && it != -1)
+                    result.op_missing_tensor[it] += 1;
+            }
+
+            if (!persistent_tape)
+            {
+                // Call destructors for all unneeded gradient functions and
+                // clear the op_tape. We can clear the tape because ownership of
+                // backward functions that will be used for gradient computation
+                // has been transferred to `result`.
+                /*for (const auto&op_pair : *op_tape) {
+                    op_pair.second.backward_function_deleter(
+                        op_pair.second.backward_function);
+                }*/
+                op_tape.Clear();
+            }
+
+            return result;
+        }
+    }
+}
diff --git a/src/TensorFlowNET.Core/Gradients/Tape.RecordOperation.cs b/src/TensorFlowNET.Core/Gradients/Tape.RecordOperation.cs
new file mode 100644
index 00000000..79595112
--- /dev/null
+++ b/src/TensorFlowNET.Core/Gradients/Tape.RecordOperation.cs
@@ -0,0 +1,51 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Util;
+using static Tensorflow.tensorflow;
+
+namespace Tensorflow.Gradients
+{
+    public partial class Tape
+    {
+        long next_op_id_ = 0;
+        UnorderedMap<long, long> tensor_usage_;
+
+        public void RecordOperation(string op_type,
+            Tensor[] input_tensors,
+            TapeTensor[] output_tensors,
+            long[] input_tensor_id,
+            TF_DataType[] input_dtypes,
+            Func<BackwardFunction> backward_function_getter)
+        {
+            if (!ShouldRecord(input_tensor_id, input_dtypes))
+            {
+                return;
+            }
+
+            long op_id = next_op_id_++;
+            var ids = new List<long>(input_tensor_id.Length);
+            foreach (var i in input_tensor_id)
+            {
+                tensor_usage_[i]++;
+                ids.Add(i);
+            }
+
+            var tensors = new List<TapeTensor>(output_tensors.Length);
+            foreach (var o in output_tensors)
+            {
+                tensor_tape_[o.GetID()] = op_id;
+                tensor_usage_[o.GetID()] = 1;
+                tensors.Add(o);
+            }
+            
+            op_tape_[op_id] = new OpTapeEntry<BackwardFunction, TapeTensor>
+            {
+                op_type = op_type,
+                output_tensor_info = tensors.ToArray(),
+                input_tensor_id = ids.ToArray(),
+                backward_function = backward_function_getter()
+            };
+        }
+    }
+}
diff --git a/src/TensorFlowNET.Core/Gradients/Tape.cs b/src/TensorFlowNET.Core/Gradients/Tape.cs
index 663b3ef2..af6134c7 100644
--- a/src/TensorFlowNET.Core/Gradients/Tape.cs
+++ b/src/TensorFlowNET.Core/Gradients/Tape.cs
@@ -1,50 +1,112 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 using Tensorflow.Util;
+using static Tensorflow.Binding;
+using static Tensorflow.tensorflow;
 
 namespace Tensorflow.Gradients
 {
-    public class Tape : ITape
+    public partial class Tape : ITape
     {
+        int nesting_id;
+        static int tape_nesting_id_counter = 0;
+        bool persistent_;
+        bool watch_accessed_variables;
+        TensorTape tensor_tape_;
+        OpTape<BackwardFunction, TapeTensor> op_tape_;
+
+        /// <summary>
+        /// A deque-backed stack, whose element references are not invalidated by
+        /// pushes and pops at the back.
+        /// </summary>
+        Stack<AccumulatorCallState> call_state_;
+
         public Tape(bool persistent, bool watch_accessed_variables)
         {
+            this.persistent_ = persistent;
+            this.watch_accessed_variables = watch_accessed_variables;
 
-        }
+            tensor_tape_ = new TensorTape();
+            op_tape_ = new OpTape<BackwardFunction, TapeTensor>();
+            tensor_usage_ = new UnorderedMap<long, long>();
 
-        public Tensor[] ComputeGradient(long[] target_tensor_ids, long[] source_tensor_ids, UnorderedMap<long, TapeTensor> sources_that_are_targets, Tensor[] output_gradients)
-        {
-            throw new NotImplementedException();
+            nesting_id = ++tape_nesting_id_counter;
+            tf.GetTapeSet().Add(this);
         }
 
-        public void PopTape(ITape tape)
+        /// <summary>
+        /// Marks this tensor to be watched by the given tape.
+        /// </summary>
+        /// <param name="x"></param>
+        public void Watch(long tensor_id)
         {
-            throw new NotImplementedException();
+            if (!CouldBackprop())
+                return;
+
+            tensor_tape_.emplace(tensor_id, -1);
         }
 
-        public void RecordOperation(string op_type, Tensor[] input_tensors, TapeTensor[] output_tensors, long[] input_tensor_id, TF_DataType[] input_dtypes, Func<tensorflow.BackwardFunction> backward_function_getter)
+        public bool ShouldRecord(long[] tensor_ids, TF_DataType[] dtypes)
         {
-            throw new NotImplementedException();
+            for (int i = 0; i < tensor_ids.Length; ++i)
+            {
+                if (tensor_tape_.find(tensor_ids[i]))
+                    if (IsDtypeTrainable(dtypes[i]))
+                        return true;
+            }
+            return false;
         }
 
-        public bool ShouldRecord(long[] tensor_ids, TF_DataType[] dtypes)
+        /// <summary>
+        /// Pops the given tape in the stack.
+        /// </summary>
+        /// <param name="tape"></param>
+        public void PopTape(ITape tape)
         {
-            throw new NotImplementedException();
+            tf.GetTapeSet().Remove(tape);
         }
 
         public void VariableAccessed(ResourceVariable variable)
         {
-            throw new NotImplementedException();
+            Watch(variable.Handle.Id);
         }
 
-        public void Watch(long tensor_id)
+        public ResourceVariable[] WatchedVariables()
         {
-            throw new NotImplementedException();
+            return null;
         }
 
-        public ResourceVariable[] WatchedVariables()
+        public bool IsDtypeTrainable(TF_DataType dtype)
         {
-            throw new NotImplementedException();
+            switch (dtype)
+            {
+                case TF_DataType.TF_HALF:
+                case TF_DataType.TF_BFLOAT16:
+                case TF_DataType.TF_FLOAT:
+                case TF_DataType.TF_DOUBLE:
+                case TF_DataType.TF_COMPLEX64:
+                case TF_DataType.TF_COMPLEX128:
+                case TF_DataType.TF_RESOURCE:
+                case TF_DataType.TF_VARIANT:
+                    return true;
+                default:
+                    return false;
+            }
         }
+
+        bool CouldForwardprop()
+            => HasAccumulator();
+
+        bool CouldBackprop()
+            => HasGradientTape();
+
+        bool HasAccumulator()
+            //return !GetAccumulatorSet()->empty();
+            => false;
+
+        bool HasGradientTape()
+            => tf.GetTapeSet().Count > 0;
     }
 }