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+---
+Title: '.tanh()'
+Description: 'Applies the hyperbolic tangent function to each element of a tensor, squashing values into the range −1 to 1.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags: 
+  - 'AI'
+  - 'Deep Learning'
+  - 'PyTorch'
+  - 'Neural Networks'
+CatalogContent: 
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+The **`torch.tanh`** function applies the hyperbolic tangent to each element of a tensor and returns a new tensor with values smoothly mapped to the range −1 to 1.
+
+## Syntax
+
+```pseudo
+torch.tanh(input, *, out=None)
+```
+
+**Parameters:**
+
+- `input`: The input tensor
+- `out` (optional): An optional tensor to store the result in (for memory reuse or performance control).
+
+**Return value:**
+
+Returns a new tensor of the same shape as input, with each value transformed to fall within the range −1 to 1.
+
+## Example
+
+In this example, `torch.tanh()` is applied to a randomly generated tensor to demonstrate how the function maps all values into the range −1 to 1:
+
+```py
+import torch
+
+# Create a random tensor
+x = torch.randn(3, 4)
+print("Original tensor:")
+print(x)
+
+# Apply tanh activation
+y = torch.tanh(x)
+print("\nAfter TanH activation:")
+print(y)
+
+# Verify that all values are in the range [-1, 1]
+print("\nMinimum value:", y.min().item())
+print("Maximum value:", y.max().item())
+```
+
+The output of this code is:
+
+```shell
+Original tensor:
+tensor([[ 0.5133,  0.7606, -0.4920,  0.8213],
+        [-0.4287,  1.6746,  1.4581,  1.4763],
+        [-0.8600, -0.2881,  2.4279, -0.0736]])
+
+After TanH activation:
+tensor([[ 0.4725,  0.6414, -0.4558,  0.6758],
+        [-0.4042,  0.9322,  0.8973,  0.9008],
+        [-0.6962, -0.2804,  0.9846, -0.0734]])
+
+Minimum value: -0.6962396502494812
+Maximum value: 0.9845553636550903
+```