GANs mnist

mnist_gan/.gitignore

@@ -0,0 +1,11 @@
+.ipynb_checkpoints
+saved_models
+samples_posterior
+samples_csv_files
+mnist_first250_training_4s_and_9s.arm
+mnist_gan
+mnist_gan_generate
+mnist_gan_generate.o
+mnist_gan.o
+.vscode
+mnist_gan.ipynb

mnist_gan/Makefile

@@ -0,0 +1,36 @@
+
+TARGET := mnist_gan_generate
+SRC := mnist_gan_generate.cpp
+LIBS_NAME := armadillo mlpack
+
+CXX := g++
+CXXFLAGS += -std=c++11 -Wall -Wextra -O3 -DNDEBUG
+# Use these CXXFLAGS instead if you want to compile with debugging symbols and
+# without optimizations.
+# CXXFLAGS += -std=c++11 -Wall -Wextra -g -O0
+LDFLAGS  += -fopenmp
+LDFLAGS += -lboost_serialization
+LDFLAGS += -larmadillo
+LDFLAGS += -L /home/viole/mlpack/build/lib/ # /path/to/mlpack/library/  # if installed locally.
+# Add header directories for any includes that aren't on the
+# default compiler search path.
+INCLFLAGS := -I /home/viole/mlpac/build/include/
+CXXFLAGS  += $(INCLFLAGS)
+
+OBJS := $(SRC:.cpp=.o)
+LIBS := $(addprefix -l,$(LIBS_NAME))
+CLEAN_LIST := $(TARGET) $(OBJS)
+
+# default rule
+default: all
+
+$(TARGET): $(OBJS)
+	$(CXX) $(CXXFLAGS) $(OBJS) -o $(TARGET) $(LDFLAGS) $(LIBS)
+
+.PHONY: all
+all: $(TARGET)
+
+.PHONY: clean
+clean:
+	@echo CLEAN $(CLEAN_LIST)
+	@rm -f $(CLEAN_LIST)

mnist_gan/Untitled.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "81c68540",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25021e81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = np.genfromtxt(\"samples_csv_files/ouput_mnist_2.csv\", delimiter = ',', dtype = np.uint8)\n",
+    "\n",
+    "img = Image.fromarray(samples)\n",
+    "img.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "af43503a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "dataset = pd.read_csv(\"samples_csv_files/ouput_mnist_2.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47913b40",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'samples_csv_files/output_mnist_2.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-66cb548918f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"samples_csv_files/output_mnist_2.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf_in\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf_in\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'samples_csv_files/output_mnist_2.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"samples_csv_files/output_mnist_2.csv\", 'r') as f_in:\n",
+    "    for line in f_in:\n",
+    "        print(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b8584b96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 55 entries, 0 to 54\n",
+      "Columns: 280 entries, 9.94226905097128e-01 to 1.00000000000000e+00.264\n",
+      "dtypes: float64(280)\n",
+      "memory usage: 120.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a68eb523",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 55 entries, 0 to 54\n",
+      "Columns: 280 entries, 9.99801803453734e-01 to 9.96537507993331e-01\n",
+      "dtypes: float64(280)\n",
+      "memory usage: 120.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = pd.read_csv(\"samples_csv_files/samples_posterior.csv\")\n",
+    "                      \n",
+    "dataset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "338ae5db",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OSError",
+     "evalue": "samples_csv_files/output_mnist_2.csv not found.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-28815900d631>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenfromtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"samples_csv_files/output_mnist_2.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/notebook/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mgenfromtxt\u001b[0;34m(fname, dtype, comments, delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows, encoding, like)\u001b[0m\n\u001b[1;32m   1789\u001b[0m             \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1790\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1791\u001b[0;31m             \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1792\u001b[0m             \u001b[0mfid_ctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1793\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/notebook/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataSource\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnewline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/notebook/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m    529\u001b[0m                                       encoding=encoding, newline=newline)\n\u001b[1;32m    530\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s not found.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOSError\u001b[0m: samples_csv_files/output_mnist_2.csv not found."
+     ]
+    }
+   ],
+   "source": [
+    "samples = np.genfromtxt(\"samples_csv_files/output_mnist_2.csv\", delimiter=\",\", dtype = np.uint8)\n",
+    "img = Image.fromarray(samples)\n",
+    "img.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57ef526f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

mnist_gan/gan_utils.hpp

@@ -0,0 +1,42 @@
+/**
+ * @file gan_utils.cpp
+ * @author Roshan Swain
+ * @author Atharva Khandait
+ *
+ * Utility function necessary for  working with GAN models.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+
+#ifndef MODELS_GAN_UTILS_HPP
+#define MODELS_GAN_UTILS_HPP
+
+#include <mlpack/core.hpp>
+#include <mlpack/methods/ann/ffn.hpp>
+
+using namespace mlpack;
+using namespace mlpack::ann;
+
+// Sample from the output distribution and post-process the outputs(because
+// we pre-processed it before passing it to the model).
+template<typename DataType = arma::mat>
+void GetSample(DataType &input, DataType& samples, bool isBinary)
+{
+  if (isBinary)
+  {
+    samples = arma::conv_to<DataType>::from(
+        arma::randu<DataType>(input.n_rows, input.n_cols) <= input);
+    samples *= 255;
+  }
+  else
+  {
+    samples = input / 2 + 0.5;
+    samples *= 255;
+    samples = arma::clamp(samples, 0, 255);
+  }
+}
+
+#endif