tez.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayses\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, Dense, MaxPooling1D, Flatten\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "import numpy\n",
    "import sys\n",
    "import os\n",
    "from matplotlib import pyplot\n",
    "from keras.models import model_from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7451, 1200, 1)\n",
      "(7451, 1200)\n",
      "data loaded\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# fix random seed for reproducibility\n",
    "numpy.random.seed(7)\n",
    "\n",
    "# load input dataset\n",
    "dataset = numpy.loadtxt(\"tez_input_data.data\", delimiter=\",\")\n",
    "\n",
    "# split into input (X) variables\n",
    "#InputValues = dataset.T\n",
    "InputValues = dataset\n",
    "nrows, ncols = InputValues.shape\n",
    "InputValues = InputValues.reshape(nrows, ncols, 1)\n",
    "print(InputValues.shape)\n",
    "\n",
    "# load output dataset\n",
    "dataset = numpy.loadtxt(\"tez_output_data.data\", delimiter=\",\")\n",
    "#OutputValues = dataset.T\n",
    "OutputValues = dataset\n",
    "nrows, ncols = OutputValues.shape\n",
    "OutputValues = OutputValues.reshape(nrows, ncols)\n",
    "print(OutputValues.shape)\n",
    "\n",
    "print(\"data loaded\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ayses\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 1200, 50)          200       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 1200, 50)          200       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1200, 50)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 1200, 50)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 1200, 50)          2550      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1200, 50)          200       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1200, 50)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 1200, 50)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 1200, 50)          2550      \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1200, 50)          200       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 1200, 50)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 1200, 50)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 60000)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 200)               12000200  \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1200)              241200    \n",
      "=================================================================\n",
      "Total params: 12,247,300\n",
      "Trainable params: 12,247,000\n",
      "Non-trainable params: 300\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(kernel_size = 3, filters = 50, input_shape=(1200,1), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling1D(1, padding='same'))\n",
    "\n",
    "#c2\n",
    "model.add(Conv1D(kernel_size = (1), filters = 50))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling1D(1, padding='same'))\n",
    "\n",
    "#c3\n",
    "model.add(Conv1D(kernel_size = (1), filters = 50))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling1D(1, padding='same'))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dense(1200, activation='softmax'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running\n",
      "WARNING:tensorflow:From C:\\Users\\ayses\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/5\n",
      " - 149s - loss: 0.0932 - acc: 0.9821\n",
      "Epoch 2/5\n",
      " - 151s - loss: 0.0862 - acc: 0.9821\n",
      "Epoch 3/5\n",
      " - 152s - loss: 0.0842 - acc: 0.9821\n",
      "Epoch 4/5\n",
      " - 149s - loss: 0.0825 - acc: 0.9821\n",
      "Epoch 5/5\n",
      " - 149s - loss: 0.0812 - acc: 0.9821\n",
      "7451/7451 [==============================] - 16s 2ms/step\n",
      "\n",
      "evaluate -> acc: 98.21%\n"
     ]
    }
   ],
   "source": [
    "print(\"running\")\n",
    "\n",
    "# Fit the model\n",
    "history = model.fit(InputValues, OutputValues, epochs=5, batch_size=10, verbose=2)\n",
    "\n",
    "\n",
    "# evaluate the model\n",
    "scores = model.evaluate(InputValues, OutputValues)\n",
    "print(\"\\nevaluate -> %s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEDCAYAAADOc0QpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB4ZXIwaHfmSSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29263c804a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "\n",
    "pyplot.plot(history.history['acc'])\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "# save model\n",
    "\n",
    "# serialize model to JSON\n",
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "    \n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model.h5\")\n",
    "\n",
    "print(\"Saved model to disk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load json and create model\n",
    "\n",
    "json_file = open('model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"model.h5\")\n",
    "\n",
    "print(\"Loaded model from disk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "#test folders\n",
    "\n",
    "test_folders = []\n",
    "\n",
    "test_folders.append(\"ALU\\\\120kure alu 268.dxf\\\\\") #0\n",
    "test_folders.append(\"ALU\\\\alüminyumdetaybuyuk1 268 alu.dxf\\\\\") #1\n",
    "test_folders.append(\"ALU\\\\dırektaban 268 alu.dxf\\\\\") #2\n",
    "test_folders.append(\"ALU\\\\huniemsan550 alu 272.dxf\\\\\")#3\n",
    "test_folders.append(\"ALU\\\\luva127pul34 alu 272.dxf\\\\\")#4\n",
    "test_folders.append(\"ALU\\\\luva117253ekim alu 272.dxf\\\\\")#5\n",
    "test_folders.append(\"ALU\\\\mobesecap34cm alu 272.dxf\\\\\")#6\n",
    "test_folders.append(\"ALU\\\\nikielektronik1 alu 272.dxf\\\\\")#7\n",
    "test_folders.append(\"ALU\\\\yunus alu 272.dxf\\\\\")#8\n",
    "test_folders.append(\"DKP\\\\52ayak dkp 268 .dxf\\\\\")#9\n",
    "test_folders.append(\"DKP\\\\570x5cmbombe 265 dkp.dxf\\\\\")#10\n",
    "test_folders.append(\"DKP\\\\akımmetal600cap9cm 268 dkp.dxf\\\\\")#11\n",
    "test_folders.append(\"DKP\\\\bizimmasaçap500 dkp 268.dxf\\\\\")#12\n",
    "test_folders.append(\"DKP\\\\bugas143 dkp 266.dxf\\\\\")#13\n",
    "test_folders.append(\"DKP\\\\canmetal dkp 266.dxf\\\\\")#14\n",
    "test_folders.append(\"DKP\\\\huni335mm dkp 268.dxf\\\\\")#15\n",
    "test_folders.append(\"DKP\\\\hunisehpayenikalıpsıvama dkp 268.dxf\\\\\")#16\n",
    "test_folders.append(\"DKP\\\\küçüknugas dkp 266.dxf\\\\\")#17\n",
    "test_folders.append(\"DKP\\\\metalikssaksı450 dkp 268.dxf\\\\\")#18\n",
    "test_folders.append(\"DKP\\\\saksıaltdemirmakara dkp 268.dxf\\\\\")#19\n",
    "test_folders.append(\"DKP\\\\ünalabi485pul huni dkp 272.dxf\\\\\")#20\n",
    "test_folders.append(\"PAS\\\\350lavabo ayak pas 265.dxf\\\\\")#21\n",
    "test_folders.append(\"PAS\\\\alirozet pas 266.dxf\\\\\")#22\n",
    "test_folders.append(\"PAS\\\\huni175  pas 267.dxf\\\\\")#23\n",
    "test_folders.append(\"PAS\\\\karalarpaslanmaz 267.dxf\\\\\")#24\n",
    "test_folders.append(\"PAS\\\\kardesanpaslanmaz330 265.dxf\\\\\")#25\n",
    "test_folders.append(\"PAS\\\\lavabo17ocaknearc pas 267.dxf\\\\\")#26\n",
    "test_folders.append(\"PAS\\\\lavabodiketek pas 267.dxf\\\\\")#27\n",
    "test_folders.append(\"PAS\\\\ömerkarasu1 pas 268.dxf\\\\\")#28\n",
    "test_folders.append(\"PAS\\\\paslamazkapak1  pas 268.dxf\\\\\")#29\n",
    "test_folders.append(\"PAS\\\\paslanmaz108şapka 266.dxf\\\\\")#30\n",
    "test_folders.append(\"PAS\\\\paslanmaztaban 268.dxf\\\\\")#31\n",
    "test_folders.append(\"PAS\\\\salimlavabo pas 266.dxf\\\\\")#32\n",
    "test_folders.append(\"PAS\\\\tevrat ayakkücük pas 268.dxf\\\\\")#33\n",
    "test_folders.append(\"PAS\\\\tevratabipaslanmaz 268.dxf\\\\\")#34\n",
    "\n",
    "print(len(test_folders))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\120kure alu 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\120kure alu 268.dxf\\tez_output_testdata.test\n",
      "(21, 1200, 1)\n",
      "(21, 1200)\n",
      "test data loaded\n",
      "(21, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\alüminyumdetaybuyuk1 268 alu.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\alüminyumdetaybuyuk1 268 alu.dxf\\tez_output_testdata.test\n",
      "(28, 1200, 1)\n",
      "(28, 1200)\n",
      "test data loaded\n",
      "(28, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\dırektaban 268 alu.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\dırektaban 268 alu.dxf\\tez_output_testdata.test\n",
      "(32, 1200, 1)\n",
      "(32, 1200)\n",
      "test data loaded\n",
      "(32, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\huniemsan550 alu 272.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\huniemsan550 alu 272.dxf\\tez_output_testdata.test\n",
      "(78, 1200, 1)\n",
      "(78, 1200)\n",
      "test data loaded\n",
      "(78, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\luva127pul34 alu 272.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\luva127pul34 alu 272.dxf\\tez_output_testdata.test\n",
      "(20, 1200, 1)\n",
      "(20, 1200)\n",
      "test data loaded\n",
      "(20, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\luva117253ekim alu 272.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\luva117253ekim alu 272.dxf\\tez_output_testdata.test\n",
      "(56, 1200, 1)\n",
      "(56, 1200)\n",
      "test data loaded\n",
      "(56, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\mobesecap34cm alu 272.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\mobesecap34cm alu 272.dxf\\tez_output_testdata.test\n",
      "(37, 1200, 1)\n",
      "(37, 1200)\n",
      "test data loaded\n",
      "(37, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\nikielektronik1 alu 272.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\nikielektronik1 alu 272.dxf\\tez_output_testdata.test\n",
      "(14, 1200, 1)\n",
      "(14, 1200)\n",
      "test data loaded\n",
      "(14, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\yunus alu 272.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\ALU\\yunus alu 272.dxf\\tez_output_testdata.test\n",
      "(25, 1200, 1)\n",
      "(25, 1200)\n",
      "test data loaded\n",
      "(25, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\52ayak dkp 268 .dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\52ayak dkp 268 .dxf\\tez_output_testdata.test\n",
      "(17, 1200, 1)\n",
      "(17, 1200)\n",
      "test data loaded\n",
      "(17, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\570x5cmbombe 265 dkp.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\570x5cmbombe 265 dkp.dxf\\tez_output_testdata.test\n",
      "(28, 1200, 1)\n",
      "(28, 1200)\n",
      "test data loaded\n",
      "(28, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\akımmetal600cap9cm 268 dkp.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\akımmetal600cap9cm 268 dkp.dxf\\tez_output_testdata.test\n",
      "(45, 1200, 1)\n",
      "(45, 1200)\n",
      "test data loaded\n",
      "(45, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\bizimmasaçap500 dkp 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\bizimmasaçap500 dkp 268.dxf\\tez_output_testdata.test\n",
      "(43, 1200, 1)\n",
      "(43, 1200)\n",
      "test data loaded\n",
      "(43, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\bugas143 dkp 266.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\bugas143 dkp 266.dxf\\tez_output_testdata.test\n",
      "(60, 1200, 1)\n",
      "(60, 1200)\n",
      "test data loaded\n",
      "(60, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\canmetal dkp 266.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\canmetal dkp 266.dxf\\tez_output_testdata.test\n",
      "(50, 1200, 1)\n",
      "(50, 1200)\n",
      "test data loaded\n",
      "(50, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\huni335mm dkp 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\huni335mm dkp 268.dxf\\tez_output_testdata.test\n",
      "(51, 1200, 1)\n",
      "(51, 1200)\n",
      "test data loaded\n",
      "(51, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\hunisehpayenikalıpsıvama dkp 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\hunisehpayenikalıpsıvama dkp 268.dxf\\tez_output_testdata.test\n",
      "(68, 1200, 1)\n",
      "(68, 1200)\n",
      "test data loaded\n",
      "(68, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\küçüknugas dkp 266.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\küçüknugas dkp 266.dxf\\tez_output_testdata.test\n",
      "(100, 1200, 1)\n",
      "(100, 1200)\n",
      "test data loaded\n",
      "(100, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\metalikssaksı450 dkp 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\metalikssaksı450 dkp 268.dxf\\tez_output_testdata.test\n",
      "(62, 1200, 1)\n",
      "(62, 1200)\n",
      "test data loaded\n",
      "(62, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\saksıaltdemirmakara dkp 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\saksıaltdemirmakara dkp 268.dxf\\tez_output_testdata.test\n",
      "(32, 1200, 1)\n",
      "(32, 1200)\n",
      "test data loaded\n",
      "(32, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\ünalabi485pul huni dkp 272.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\DKP\\ünalabi485pul huni dkp 272.dxf\\tez_output_testdata.test\n",
      "(35, 1200, 1)\n",
      "(35, 1200)\n",
      "test data loaded\n",
      "(35, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\350lavabo ayak pas 265.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\350lavabo ayak pas 265.dxf\\tez_output_testdata.test\n",
      "(3, 1200, 1)\n",
      "(3, 1200)\n",
      "test data loaded\n",
      "(3, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\alirozet pas 266.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\alirozet pas 266.dxf\\tez_output_testdata.test\n",
      "(22, 1200, 1)\n",
      "(22, 1200)\n",
      "test data loaded\n",
      "(22, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\huni175  pas 267.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\huni175  pas 267.dxf\\tez_output_testdata.test\n",
      "(55, 1200, 1)\n",
      "(55, 1200)\n",
      "test data loaded\n",
      "(55, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\karalarpaslanmaz 267.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\karalarpaslanmaz 267.dxf\\tez_output_testdata.test\n",
      "(32, 1200, 1)\n",
      "(32, 1200)\n",
      "test data loaded\n",
      "(32, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\kardesanpaslanmaz330 265.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\kardesanpaslanmaz330 265.dxf\\tez_output_testdata.test\n",
      "(49, 1200, 1)\n",
      "(49, 1200)\n",
      "test data loaded\n",
      "(49, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\lavabo17ocaknearc pas 267.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\lavabo17ocaknearc pas 267.dxf\\tez_output_testdata.test\n",
      "(18, 1200, 1)\n",
      "(18, 1200)\n",
      "test data loaded\n",
      "(18, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\lavabodiketek pas 267.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\lavabodiketek pas 267.dxf\\tez_output_testdata.test\n",
      "(13, 1200, 1)\n",
      "(13, 1200)\n",
      "test data loaded\n",
      "(13, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\ömerkarasu1 pas 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\ömerkarasu1 pas 268.dxf\\tez_output_testdata.test\n",
      "(71, 1200, 1)\n",
      "(71, 1200)\n",
      "test data loaded\n",
      "(71, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\paslamazkapak1  pas 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\paslamazkapak1  pas 268.dxf\\tez_output_testdata.test\n",
      "(25, 1200, 1)\n",
      "(25, 1200)\n",
      "test data loaded\n",
      "(25, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\paslanmaz108şapka 266.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\paslanmaz108şapka 266.dxf\\tez_output_testdata.test\n",
      "(33, 1200, 1)\n",
      "(33, 1200)\n",
      "test data loaded\n",
      "(33, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\paslanmaztaban 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\paslanmaztaban 268.dxf\\tez_output_testdata.test\n",
      "(35, 1200, 1)\n",
      "(35, 1200)\n",
      "test data loaded\n",
      "(35, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\salimlavabo pas 266.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\salimlavabo pas 266.dxf\\tez_output_testdata.test\n",
      "(4, 1200, 1)\n",
      "(4, 1200)\n",
      "test data loaded\n",
      "(4, 1200)\n",
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\tevrat ayakkücük pas 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\tevrat ayakkücük pas 268.dxf\\tez_output_testdata.test\n",
      "(25, 1200, 1)\n",
      "(25, 1200)\n",
      "test data loaded\n",
      "(25, 1200)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data saved\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\tevratabipaslanmaz 268.dxf\\tez_input_testdata.test\n",
      "C:\\WORK\\Projects\\Sivama\\dxf\\test_data\\PAS\\tevratabipaslanmaz 268.dxf\\tez_output_testdata.test\n",
      "(38, 1200, 1)\n",
      "(38, 1200)\n",
      "test data loaded\n",
      "(38, 1200)\n",
      "test data saved\n"
     ]
    }
   ],
   "source": [
    "#test\n",
    "\n",
    "base_dir = \"C:\\\\WORK\\\\Projects\\\\Sivama\\\\dxf\\\\test_data\\\\\"\n",
    "for j in range(len(test_folders)):\n",
    "    path = os.path.join(base_dir, test_folders[j]) \n",
    "    filenameInput = os.path.join(path, \"tez_input_testdata.test\")\n",
    "    filenameOutput = os.path.join(path, \"tez_output_testdata.test\")\n",
    "    filenamePredict = os.path.join(path, \"tez_result_data.predict\")\n",
    "\n",
    "    print(filenameInput)\n",
    "    print(filenameOutput)\n",
    "\n",
    "    # load input dataset\n",
    "    dataset = numpy.loadtxt(filenameInput, delimiter=\"\\t\")\n",
    "\n",
    "    # split into input (X) variables\n",
    "    InputTestValues = dataset\n",
    "    nrows, ncols = InputTestValues.shape\n",
    "    InputTestValues = InputTestValues.reshape(nrows, ncols, 1)\n",
    "    print(InputTestValues.shape)\n",
    "\n",
    "    # load output dataset\n",
    "    dataset = numpy.loadtxt(filenameOutput, delimiter=\"\\t\")\n",
    "    OutputTestValues = dataset\n",
    "    nrows, ncols = OutputTestValues.shape\n",
    "    OutputTestValues = OutputTestValues.reshape(nrows, ncols)\n",
    "    print(OutputTestValues.shape)\n",
    "\n",
    "    print(\"test data loaded\")\n",
    "\n",
    "    # calculate predictions\n",
    "    predictions = model.predict(InputTestValues)\n",
    "\n",
    "    result = numpy.zeros(shape=(nrows, ncols))\n",
    "    print(result.shape)\n",
    "\n",
    "    for j in range(len(predictions)):\n",
    "        for i in range(len(predictions[0])):\n",
    "            if predictions[j][i] * 100 > 1: \n",
    "                result[j][i] = 1\n",
    "            else:\n",
    "                result[j][i] = 0            \n",
    "\n",
    "\n",
    "    numpy.savetxt(filenamePredict, result, fmt='%1d', delimiter='\\t', newline='\\n', header='', footer='', comments='# ', encoding=None)\n",
    "    print(\"test data saved\")           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Y:0 R:0\n",
      "1 Y:0 R:0\n",
      "2 Y:0 R:0\n",
      "3 Y:0 R:0\n",
      "4 Y:0 R:0\n",
      "5 Y:0 R:0\n",
      "6 Y:0 R:0\n",
      "7 Y:0 R:0\n",
      "8 Y:0 R:0\n",
      "9 Y:0 R:0\n",
      "10 Y:0 R:0\n",
      "11 Y:0 R:0\n",
      "12 Y:0 R:0\n",
      "13 Y:0 R:0\n",
      "14 Y:0 R:0\n",
      "15 Y:0 R:0\n",
      "16 Y:0 R:0\n",
      "17 Y:0 R:0\n",
      "18 Y:0 R:0\n",
      "19 Y:0 R:0\n",
      "20 Y:0 R:0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "index = 5\n",
    "for j in range(nrows):\n",
    "    print(\"%d Y:%d R:%d\" % (j, OutputTestValues[index][j], result[index][j]))\n",
    "                    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}