Day1_4_Session.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Day1_4_Session.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"qiXqB5sceNrC","colab_type":"text"},"source":["# use session \n","<b> session을 기본으로 하는 간단한 logistic regression을 만든다.  \n","<b> source 설명 이후 실습을 진행한다.  "]},{"cell_type":"code","metadata":{"id":"5KtT9r76Payi","colab_type":"code","colab":{}},"source":["sample = ['너 오늘 이뻐 보인다',\n","          '나는 오늘 기분이 더러워',\n","          '끝내주는데, 좋은 일이 있나봐',\n","          '나 좋은 일이 생겼어',\n","          '아 오늘 진짜 짜증나',\n","          '환상적인데, 정말 좋은거 같아']\n","\n","label =[[1], [0], [1], [1], [0], [1]]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"IQZ0G4TXSBUr","colab_type":"code","colab":{}},"source":["import os\n","import tensorflow as tf\n","import numpy as np\n","\n","from tensorflow.keras import preprocessing"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"N3haV_wvSC7i","colab_type":"code","colab":{}},"source":["# default 5000이고 Keras 데이터 사전 처리 유틸리티\n","# Text tokenization utility class.\n","tokenizer = preprocessing.text.Tokenizer() \n","# Updates internal vocabulary based on a list of texts.\n","tokenizer.fit_on_texts(sample) \n","# Transforms each text in texts to a sequence of integers.\n","sequence = tokenizer.texts_to_sequences(sample)\n","#  Yields individual sequences.\n","word_index = tokenizer.word_index"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"C8BIwT2-SFap","colab_type":"code","colab":{}},"source":["sequences = tf.placeholder(dtype=tf.int32, shape=[None, 4], name='sequences')\n","labels = tf.placeholder(dtype=tf.int32, shape=[None,1], name='labels')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nMxqXzghSLMF","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":190},"outputId":"47252f33-31ba-4612-e2f6-c11cf2be33b4","executionInfo":{"status":"ok","timestamp":1559362246315,"user_tz":-540,"elapsed":903,"user":{"displayName":"조중현","photoUrl":"","userId":"04275587646849861321"}}},"source":["EPOCH_SIZE = 100\n","BATCH_SIZE = 2\n","\n","VOCAB_SIZE = len(word_index) +1\n","EMB_SIZE = 128\n","\n","\n","# Embedding\n","embed_input = tf.keras.layers.Embedding(VOCAB_SIZE, EMB_SIZE)(sequences)\n","embed_input = tf.reduce_mean(embed_input, axis=-1)\n","\n","# Model\n","hidden_layer = tf.keras.layers.Dense(128, activation=tf.nn.relu)(embed_input)\n","output_layer = tf.keras.layers.Dense(1)(hidden_layer)\n","output = tf.nn.sigmoid(output_layer)\n","\n","# # Loss\n","loss = tf.losses.mean_squared_error(output, labels)\n","\n","# Optimizer\n","train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)\n"],"execution_count":5,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: 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","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/losses/losses_impl.py:667: to_float (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","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-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"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JtfngFG_SOZl","colab_type":"code","outputId":"504d2da3-bd65-4fab-93dc-84dc2172038c","executionInfo":{"status":"ok","timestamp":1559362471570,"user_tz":-540,"elapsed":1260,"user":{"displayName":"조중현","photoUrl":"","userId":"04275587646849861321"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["shuffle_sequence = []\n","shuffle_label = []\n","index = 0\n","\n","sess = tf.Session()\n","#print(\"start time : \", start_time)\n","sess.run(tf.global_variables_initializer())\n","\n","\n","for step in range(EPOCH_SIZE):\n","  random_index = np.random.choice(range(len(sequence)), len(sequence), replace=False)\n","  for i in random_index:\n","    shuffle_sequence.append(sequence[i])\n","    shuffle_label.append(label[i])\n","    \n","  sess.run(train_op, feed_dict={sequences: shuffle_sequence, labels: shuffle_label})\n","  if (step+1) % 10 == 0:\n","    print(step + 1, sess.run(loss, feed_dict={sequences: shuffle_sequence, labels: shuffle_label}))\n","\n","  shuffle_sequence.clear()\n","  shuffle_label.clear()"],"execution_count":7,"outputs":[{"output_type":"stream","text":["10 0.24220967\n","20 0.23208906\n","30 0.21854645\n","40 0.2008347\n","50 0.17887805\n","60 0.15264888\n","70 0.12390856\n","80 0.09487412\n","90 0.06884839\n","100 0.047975272\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vNVGTyGEPtJ8","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}