RNN & LSTM Calculator - Construction Kit

01 · Model- params

Activation σtanh

Gate Activationσ

02 · Dimensionsx · h · y

d_x input

d_h hidden (per layer, max 20)

d_y output

T seq length

B batch

Layers

03 · Topology

Direction

Sequencing

Bias

Peephole connections (LSTM) Forget-gate bias = 1.0 Layer normalization Residual h-skip

Dense (FC) layer before output

d_u dense units

04 · Regularization

Input dropout0.00

Recurrent dropout0.00

Initialization

Gradient clip1.0

05 · Visual

Diagram view

Density

Show regularization card Show weight matrices Show formulas Detailed computation breakdown Show unrolled timeline

ModelRNN

Params- weights

Mem · fwd- MB

FLOPs / step- M

Sequence- steps

Output-

Folded recurrent cell · standard input · x hidden · h gate cell · c

Figure 1.0 - RNN cell after Olah (2015), redrawn for the construction kit.

EQ · EquationsRNN

∑ · Computedlive

W · Weight matrices- tensors

T · Unrolled timelineT = 8

RNN-LSTM-CK · §1.3 h₀, c₀ ← 0 · all values derived live from knob state rev. 06