rafalpronko

modelpy = NeuralNet(10)
criterion = nn.CrossEntropyLoss()
optim = torch.optim.Adam(modelpy.parameters())

modelpy

rafalpronko

modeltf = keras.Sequential([
    keras.layers.Conv2D(input_shape=(28,28,1), filters=16, kernel_size=5, strides=1, padding="same", activation=tf.nn.relu),
    keras.layers.BatchNormalization(), 
    keras.layers.MaxPooling2D(pool_size=2, strides=2),
    keras.layers.Conv2D(32, kernel_size=5, strides=1, padding="same", activation=tf.nn.relu),
    keras.layers.BatchNormalization(), 
    keras.layers.MaxPooling2D(pool_size=2, strides=2),
    keras.layers.Flatten(),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

rafalpronko

# for Pytorch
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

# for tensorflow
import tensorflow as tf
from tensorflow import keras