Reshape & View Validity Checker
Verify a reshape preserves the element count, solve the -1 wildcard, and learn when view() fails but reshape() works.
Defaults flatten attention scores (32 batch, 12 heads, 64×64) for a softmax. Remember: reshape validity is necessary but not sufficient for view() — non-contiguous tensors need reshape()/contiguous().
Formula
About Reshape & View Validity Checker
Element count in, element count out — reshape's only law, yet RuntimeError: shape is invalid for input of size remains one of PyTorch's most-Googled errors. This checker multiplies out both shapes, solves the -1 wildcard the way the framework does (total divided by the product of known dims, must divide exactly), and pinpoints the mismatch when there is one. The note covers the second-order trap: a count-valid reshape can still fail as view() if a transpose made the tensor non-contiguous — that error asks for .contiguous() or .reshape(), not different numbers.
How to use Reshape & View Validity Checker
- 1Enter your values into Reshape & View Validity Checker — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Reshape & View Validity Checker?
- ✓Computes Reshape & View Validity Checker instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: ∏ target dims must equal ∏ source dims; a single -1 is solved as total ÷ ∏(known dims), and must divide exactly.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
What does -1 in a reshape actually compute?+
total_elements ÷ product(other_dims). For 98,304 elements reshaped to (32, 12, -1): 98304/(32·12) = 256. If the division isn't exact you get the invalid-shape error — usually meaning an upstream batch or sequence dim isn't what you assumed.
view() raised an error but reshape() worked — why?+
view() requires the new shape to be expressible over the existing memory strides — true for contiguous tensors, often false after transpose/permute/slicing. reshape() silently copies when needed. The element-count math (this tool) is identical; only memory layout differs.
Does reshape ever reorder my data?+
Never — it reinterprets the same linear (row-major) order under new dimensions. If you need actual reordering — (B,S,H,D) → (B,H,S,D) for attention — that is permute/transpose, NOT reshape; confusing the two corrupts data silently and is a notorious attention-implementation bug.
Why do attention implementations reshape so much?+
Multi-head attention is bookkeeping: split H·D into (heads, head_dim), move heads next to batch for batched matmul, undo afterwards. Each step is a reshape or transpose; this checker plus a strides mental model is how you audit them without printing shapes every line.
Related ML & AI tools
ROC-AUC Calculator (from TPR/FPR points)
Trapezoidal area under the ROC curve from your (FPR, TPR) operating points — the threshold-independent ranking score.
● LiveClassification Threshold Cost Calculator
Find the probability cutoff that minimizes expected cost given your false-positive and false-negative penalties.
● LiveSilhouette Score Calculator
Cluster cohesion vs separation for one point — the building block of the silhouette metric for choosing K.
● Live