Oktai15

import torch
import torch.nn as nn
import torch.optim as optim

from torch.distributions.normal import Normal

num_gaussian = 6
gaussian_dim = 1

device = torch.device("cuda")

Oktai15

name: "SimpleCaffeNet"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 10 dim: 1 dim: 28 dim: 28 } }
}
layer {
  name: "fc1"
  type: "InnerProduct"

Oktai15

import torch

h0 = torch.randn(10)
x = torch.randn(5, 10)
h = [h0]

for i in range(5):
    h_i = h[-1] * x[i]
    h.append(h_i)

Oktai15

# update for https://gist.github.com/Oktai15/4b6617b916c0fa4feecab35be09c1bd6 
a = tf.constant(10)

data = tf.placeholder(tf.float32, shape=(data_size, input_size))
h1_w1 = tf.placeholder(tf.float32, shape=(input_size, hidden1_output))
h2_w1 = tf.placeholder(tf.float32, shape=(input_size, hidden1_output))
 
def first(): return tf.matmul(data, h1_w1)
def second(): return tf.matmul(data, h2_w1)
 

Oktai15

import torch
import torch.nn as nn
import torch.nn.functional as fun

data_size = 10

input_size = 28 * 28
hidden1_output = 200
output_size = 1

Oktai15

import numpy as np
import tensorflow as tf

data_size = 10

input_size = 28 * 28
hidden1_output = 200
output_size = 1

data = np.random.randn(data_size, input_size)

Oktai15

import numpy as np
import tensorflow as tf

data_size = 10

input_size = 28 * 28
hidden1_output = 200
output_size = 1

data = tf.placeholder(tf.float32, shape=(data_size, input_size))