# Discrepancy in BatchNorm2d Gradient Calculation Between TensorFlow and PyTorch

I’ve compared the gradient calculations of a `BatchNorm2d` layer across different deep learning frameworks, specifically TensorFlow and PyTorch. While doing so, I’ve encountered a significant discrepancy in the gradients computed by the two frameworks. More specifically, the gradient computed by PyTorch is near zero, while TensorFlow returns something close to one.

Here’s the code I used for comparison:

``````import numpy as np

# TensorFlow/Keras
import tensorflow as tf

# PyTorch
import torch
import torch.nn as nn

# Set a common random seed for reproducibility
seed = 0
np.random.seed(seed)
tf.random.set_seed(seed)
torch.manual_seed(seed)

# Create a random input tensor with the same shape for all frameworks
input_shape = (4, 3, 5, 5)
x_np = np.random.randn(*input_shape).astype(np.float32)

# TensorFlow/Keras BatchNorm2d
class TFModel(tf.keras.Model):
def __init__(self):
super(TFModel, self).__init__()
self.bn = tf.keras.layers.BatchNormalization(axis=1, epsilon=1e-05, momentum=0.1)

def call(self, x):
return self.bn(x)

# Instantiate the model
tf_model = TFModel()

# Convert the numpy array to a tensor and ensure it's being watched
x_tf = tf.convert_to_tensor(x_np)
x_tf = tf.Variable(x_tf)

y_tf = tf_model(x_tf)
y_tf_sum = tf.reduce_sum(y_tf)

# Compute the gradient of the output with respect to the input

# Convert the gradient to a numpy array for comparison

# PyTorch BatchNorm2d
class TorchModel(nn.Module):
def __init__(self):
super(TorchModel, self).__init__()
self.bn = nn.BatchNorm2d(3)

def forward(self, x):
return self.bn(x)

torch_model = TorchModel()
y_torch = torch_model(x_torch)
y_torch.sum().backward()

# Calculate the difference between TensorFlow and PyTorch gradients

# Print the differences
print(f"Difference between TensorFlow and PyTorch gradients: {diff_tf_torch:.6f}")
• `grad_pytorch`: Shows a gradient of all zeros (10^-10).
• `grad_tf`: Shows non-zero gradients.