This paper introduces dynActivation, a per-layer trainable activation function that dynamically interpolates between base nonlinearities and linear paths using lightweight learned scalars. The proposed method is evaluated across vision tasks (CIFAR-10, MNIST) and language modeling tasks, demonstrating significant improvements in training efficiency (up to 54% faster) and performance. On CIFAR-10, dynActivation(Mish) achieves up to 14.02% improvement over static Mish, with 24% reduction in convergence time. In deep network scaling experiments (up to 75 layers), dynActivation maintains robust performance (95.3-99.3% accuracy) while ReLU collapses below 80%, demonstrating that adaptive nonlinearity linearization in deep layers enhances both training stability and final model quality.