[MOD] defined the neural net as well as the loss function and optimization algorithm
This commit is contained in:
Eduardo Cueto Mendoza
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@ -2,25 +2,19 @@
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"cells": [
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/eddie/.pyenv/versions/3.7.6/envs/pytorch/lib/python3.7/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
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" warnings.warn(msg)\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from torch.utils.data import Dataset"
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"from torch.utils.data import Dataset\n",
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"from torch.utils.data import DataLoader\n",
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"from torch.nn import BCELoss\n",
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"from torch.optim import SGD"
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]
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},
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{
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@ -313,12 +307,187 @@
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"y.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Dataset(Dataset):\n",
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" def __init__(self,x,y):\n",
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" self.x = x\n",
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" self.y = y\n",
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" \n",
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" def __getitem__(self, index):\n",
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" return self.x[index], self.y[index]\n",
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" \n",
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" def __len__(self):\n",
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" return len(self.x)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = Dataset(x,y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"768"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(dataset)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_loader = DataLoader(dataset=dataset,\n",
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" batch_size=32,\n",
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" shuffle=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<torch.utils.data.dataloader.DataLoader at 0x12ced9810>"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_loader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"There are 24 batches in in the dataset\n",
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"For one iteration (baatch) there are:\n",
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"Data: torch.Size([32, 7])\n",
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"lables: torch.Size([32, 1])\n"
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]
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}
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],
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"source": [
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"# visualization of the train loader\n",
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"print(\"There are {} batches in in the dataset\".format(len(train_loader)))\n",
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"for (x,y) in train_loader:\n",
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" print(\"For one iteration (baatch) there are:\")\n",
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" print(\"Data: {}\".format(x.shape))\n",
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" print(\"lables: {}\".format(y.shape))\n",
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" break\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"24.0"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"768/32"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Model(nn.Module):\n",
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" def __init__(self, input_features,output_features): #,hidden_layer_1,hidden_layer_2,): caan be done this way or hard coded\n",
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" super(Model, self).__init__()\n",
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" self.fc1 = nn.Linear(input_features, 5)\n",
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" self.fc2 = nn.Linear(5,4)\n",
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" self.fc3 = nn.Linear(4,3)\n",
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" self.fc4 = nn.Linear(3,output_features)\n",
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" self.sigmoid = nn.Sigmoid()\n",
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" self.tanh = nn.Tanh()\n",
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" \n",
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" def forward(self,x):\n",
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" out = self.fc1(x)\n",
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" out = self.tanh(out)\n",
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" out = self.fc2(out)\n",
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" out = self.tanh(out)\n",
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" out = self.fc3(out)\n",
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" out = self.tanh(out)\n",
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" out = self.fc4(out)\n",
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" out = self.sigmoid(out)\n",
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" \n",
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" return out\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"# The definition of the class Model object nets\n",
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"net = Model(7,1)\n",
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"\n",
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"# Binary cross entropy was chosen as a the loss function\n",
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"criterion = BCELoss(reduction='mean')\n",
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"\n",
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"# Define the optimizer\n",
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"optimizer = SGD(net.parameters(), lr=0.1, momentum=0.9)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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" "
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]
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}
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],
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"metadata": {
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