Hmmm, I tried on Keras batch_size==50000, I assume results are equivalent to what I see with Pytorch:
history = network.fit(train_img[:-10000], train_lbl[:-10000], epochs=20, batch_size=len(train_img[:-10000]),
                      validation_data=(train_img[-10000:], train_lbl[-10000:]))

Train on 50000 samples, validate on 10000 samples
Epoch 1/20
50000/50000 [==============================] - 1s 18us/step - loss: 0.0942 - acc: 0.0904 - val_loss: 0.0871 - val_acc: 0.2476
Epoch 2/20
50000/50000 [==============================] - 0s 5us/step - loss: 0.0872 - acc: 0.2480 - val_loss: 0.0788 - val_acc: 0.4158
Epoch 3/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0785 - acc: 0.4221 - val_loss: 0.0766 - val_acc: 0.4163
Epoch 4/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0763 - acc: 0.4157 - val_loss: 0.0861 - val_acc: 0.3423
Epoch 5/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0857 - acc: 0.3420 - val_loss: 0.1197 - val_acc: 0.2763
Epoch 6/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.1179 - acc: 0.2858 - val_loss: 0.0955 - val_acc: 0.3331
Epoch 7/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0944 - acc: 0.3383 - val_loss: 0.1169 - val_acc: 0.2952
Epoch 8/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.1149 - acc: 0.3047 - val_loss: 0.0852 - val_acc: 0.3785
Epoch 9/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0836 - acc: 0.3928 - val_loss: 0.0884 - val_acc: 0.4178
Epoch 10/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0866 - acc: 0.4252 - val_loss: 0.0941 - val_acc: 0.3858
Epoch 11/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0926 - acc: 0.3962 - val_loss: 0.0699 - val_acc: 0.4926
Epoch 12/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0693 - acc: 0.4972 - val_loss: 0.0704 - val_acc: 0.4493
Epoch 13/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0696 - acc: 0.4587 - val_loss: 0.0641 - val_acc: 0.5260
Epoch 14/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0636 - acc: 0.5310 - val_loss: 0.0627 - val_acc: 0.5468
Epoch 15/20
50000/50000 [==============================] - 0s 5us/step - loss: 0.0624 - acc: 0.5495 - val_loss: 0.0596 - val_acc: 0.5798
Epoch 16/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0589 - acc: 0.5861 - val_loss: 0.0572 - val_acc: 0.5897
Epoch 17/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0569 - acc: 0.5889 - val_loss: 0.0564 - val_acc: 0.6069
Epoch 18/20
50000/50000 [==============================] - 0s 5us/step - loss: 0.0559 - acc: 0.6139 - val_loss: 0.0604 - val_acc: 0.5876
Epoch 19/20
50000/50000 [==============================] - 0s 4us/step - loss: 0.0602 - acc: 0.5884 - val_loss: 0.0587 - val_acc: 0.5717
Epoch 20/20
50000/50000 [==============================] - 0s 5us/step - loss: 0.0581 - acc: 0.5802 - val_loss: 0.0588 - val_acc: 0.5824