순환신경망으로 IMDB리뷰 분류하기.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"순환신경망으로 IMDB리뷰 분류하기.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyOpIhYRSVjzV0MC4N3GB4zp"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hnnORnJy1f09","executionInfo":{"status":"ok","timestamp":1644652731808,"user_tz":-540,"elapsed":8687,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"ab58dcbe-3fc6-4040-8553-c12c3e7128ab"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n","17465344/17464789 [==============================] - 0s 0us/step\n","17473536/17464789 [==============================] - 0s 0us/step\n"]}],"source":["from tensorflow.keras.datasets import imdb\n","(train_input, train_target), (test_input, test_target)= imdb.load_data(num_words=500)"]},{"cell_type":"code","source":["print(train_input.shape, test_input.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Wg6xSnfW8NIg","executionInfo":{"status":"ok","timestamp":1644652746344,"user_tz":-540,"elapsed":269,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"6b60d44c-3b81-4aee-a9bf-3e63f52130ec"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["(25000,) (25000,)\n"]}]},{"cell_type":"code","source":["print(len(train_input[0]))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KTltsZnb8VJf","executionInfo":{"status":"ok","timestamp":1644652886373,"user_tz":-540,"elapsed":299,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"a53f0afc-4374-43d9-b0f5-fe9a322add36"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["218\n"]}]},{"cell_type":"code","source":["print(len(train_input[1]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pHMGdCFs83We","executionInfo":{"status":"ok","timestamp":1644652898630,"user_tz":-540,"elapsed":4,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"c44b75cd-666a-4b6d-847c-c3426d126a18"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["189\n"]}]},{"cell_type":"code","source":["print(train_input[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vdGwoddi86YO","executionInfo":{"status":"ok","timestamp":1644652910334,"user_tz":-540,"elapsed":5,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"375db973-a2c4-4b8a-a12f-e34b4cba5c2e"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["[1, 14, 22, 16, 43, 2, 2, 2, 2, 65, 458, 2, 66, 2, 4, 173, 36, 256, 5, 25, 100, 43, 2, 112, 50, 2, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 2, 17, 2, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 469, 4, 22, 71, 87, 12, 16, 43, 2, 38, 76, 15, 13, 2, 4, 22, 17, 2, 17, 12, 16, 2, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2, 2, 16, 480, 66, 2, 33, 4, 130, 12, 16, 38, 2, 5, 25, 124, 51, 36, 135, 48, 25, 2, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 2, 36, 71, 43, 2, 476, 26, 400, 317, 46, 7, 4, 2, 2, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 2, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n"]}]},{"cell_type":"code","source":["print(train_target[:20])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KYDvoNNR89Pw","executionInfo":{"status":"ok","timestamp":1644652928023,"user_tz":-540,"elapsed":329,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"2d8746e8-26c9-4da1-c609-c36baeba0efd"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["[1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1]\n"]}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","train_input, val_input, train_target, val_target = train_test_split(\n","    train_input, train_target, test_size=0.2, random_state=42\n",")"],"metadata":{"id":"6UJxsRfc9Bg8","executionInfo":{"status":"ok","timestamp":1644653014996,"user_tz":-540,"elapsed":267,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","lengths= np.array([len(x) for x in train_input])"],"metadata":{"id":"m1y3PxwE9Wwh","executionInfo":{"status":"ok","timestamp":1644653098334,"user_tz":-540,"elapsed":4,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["print(np.mean(lengrhs), np.median(lengths))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VbZBBtgh9hmS","executionInfo":{"status":"ok","timestamp":1644653099390,"user_tz":-540,"elapsed":5,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"28dbd424-fd45-43e0-fa8c-734b9114ff95"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["239.00925 178.0\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","plt.hist(lengths)\n","plt.xlabel('length')\n","plt.ylabel('frequency')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"owaWXsXY9oFp","executionInfo":{"status":"ok","timestamp":1644653163710,"user_tz":-540,"elapsed":346,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"5834a528-109c-49ee-ebc0-0cec35e9c6ea"},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["from tensorflow.keras.preprocessing.sequence import pad_sequences\n","train_seq = pad_sequences(train_input, maxlen=100)"],"metadata":{"id":"JG9KQe9N9161","executionInfo":{"status":"ok","timestamp":1644653235592,"user_tz":-540,"elapsed":664,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["print(train_seq.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sygZJOD2-MhG","executionInfo":{"status":"ok","timestamp":1644653255389,"user_tz":-540,"elapsed":4,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"56c9f66e-1db1-42c1-bf14-b273f0bad834"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["(20000, 100)\n"]}]},{"cell_type":"code","source":["print(train_seq[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aGgyvQMV-Rgz","executionInfo":{"status":"ok","timestamp":1644653265809,"user_tz":-540,"elapsed":2,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"c582250c-2434-40b0-a6a9-d14b553425e2"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["[ 10   4  20   9   2 364 352   5  45   6   2   2  33 269   8   2 142   2\n","   5   2  17  73  17 204   5   2  19  55   2   2  92  66 104  14  20  93\n","  76   2 151  33   4  58  12 188   2 151  12 215  69 224 142  73 237   6\n","   2   7   2   2 188   2 103  14  31  10  10 451   7   2   5   2  80  91\n","   2  30   2  34  14  20 151  50  26 131  49   2  84  46  50  37  80  79\n","   6   2  46   7  14  20  10  10 470 158]\n"]}]},{"cell_type":"code","source":["print(train_input[0][-10:])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fYy755_h-UDm","executionInfo":{"status":"ok","timestamp":1644653284948,"user_tz":-540,"elapsed":5,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"7caedfd5-5017-49c3-d7c8-416bde87db69"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["[6, 2, 46, 7, 14, 20, 10, 10, 470, 158]\n"]}]},{"cell_type":"code","source":["print(train_seq[5])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ml60lGhl-Ypl","executionInfo":{"status":"ok","timestamp":1644653300214,"user_tz":-540,"elapsed":4,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"738d0ef5-6019-4314-9cfe-1635abbf68c1"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["[  0   0   0   0   1   2 195  19  49   2   2 190   4   2 352   2 183  10\n","  10  13  82  79   4   2  36  71 269   8   2  25  19  49   7   4   2   2\n","   2   2   2  10  10  48  25  40   2  11   2   2  40   2   2   5   4   2\n","   2  95  14 238  56 129   2  10  10  21   2  94 364 352   2   2  11 190\n","  24 484   2   7  94 205 405  10  10  87   2  34  49   2   7   2   2   2\n","   2   2 290   2  46  48  64  18   4   2]\n"]}]},{"cell_type":"code","source":["val_seq = pad_sequences(val_input, maxlen=100)"],"metadata":{"id":"0rgWxL0G-cfB","executionInfo":{"status":"ok","timestamp":1644653329694,"user_tz":-540,"elapsed":397,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["from tensorflow import keras\n","model =keras.Sequential()\n","model.add(keras.layers.SimpleRNN(8,input_shape=(100,500)))\n","model.add(keras.layers.Dense(1,activation='sigmoid'))"],"metadata":{"id":"H5s-j0nu-jf0","executionInfo":{"status":"ok","timestamp":1644653420947,"user_tz":-540,"elapsed":680,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["train_oh=keras.utils.to_categorical(train_seq)"],"metadata":{"id":"zOwgliEX-5xm","executionInfo":{"status":"ok","timestamp":1644653462747,"user_tz":-540,"elapsed":3704,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["print(train_oh.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iLLma0C7_DPZ","executionInfo":{"status":"ok","timestamp":1644653472855,"user_tz":-540,"elapsed":256,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"6ef42f3a-e6af-4487-b4d9-b97118a8629a"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["(20000, 100, 500)\n"]}]},{"cell_type":"code","source":["print(train_oh[0][0][:12])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TNd3TT3t_Gir","executionInfo":{"status":"ok","timestamp":1644653505959,"user_tz":-540,"elapsed":266,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"2d2da69a-e3c3-4c6e-e228-9df20a8d52c5"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n"]}]},{"cell_type":"code","source":["print(np.sum(train_oh[0][0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f7uKOroo_K9c","executionInfo":{"status":"ok","timestamp":1644653527410,"user_tz":-540,"elapsed":5,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"02a6437b-9496-4923-ce13-1ecd5e037b7e"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["1.0\n"]}]},{"cell_type":"code","source":["val_oh = keras.utils.to_categorical(val_seq)"],"metadata":{"id":"vH4_Z_fJ_T5e","executionInfo":{"status":"ok","timestamp":1644653552955,"user_tz":-540,"elapsed":732,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7tK6MgBN_Z89","executionInfo":{"status":"ok","timestamp":1644653559375,"user_tz":-540,"elapsed":4,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"8d4ab28b-16e8-4067-c04b-82d8715a25ef"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," simple_rnn (SimpleRNN)      (None, 8)                 4072      \n","                                                                 \n"," dense (Dense)               (None, 1)                 9         \n","                                                                 \n","=================================================================\n","Total params: 4,081\n","Trainable params: 4,081\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n","model.compile(optimizer=rmsprop, loss='binary_corssentropy', metrics=['accuracy'])\n","checkpoint_cb=keras.callbacks.ModelCheckpoint('best-simplernn-model.h5')\n","early_stopping_cb= keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n","history = model.fit(train_oh, train_target, epochs=100, batch_size=64, validation_data=(val_oh, val_target), callbacks=[checkpoint_cb, early_stopping_cb])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":804},"id":"0dWWIbPw_bop","executionInfo":{"status":"error","timestamp":1644654692237,"user_tz":-540,"elapsed":283,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"883d4146-b66f-415f-8f50-8c62f453c6fd"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n"]},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-37-b4ae29776c33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcheckpoint_cb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelCheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'best-simplernn-model.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mearly_stopping_cb\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEarlyStopping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestore_best_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_oh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_oh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_target\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheckpoint_cb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_cb\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[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1127\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\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-> 1129\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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   1130\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[1;32m   1131\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 810, in train_step\n        y, y_pred, sample_weight, regularization_losses=self.losses)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 184, in __call__\n        self.build(y_pred)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 133, in build\n        self._losses = tf.nest.map_structure(self._get_loss_object, self._losses)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 273, in _get_loss_object\n        loss = losses_mod.get(loss)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 2134, in get\n        return deserialize(identifier)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 2093, in deserialize\n        printable_module_name='loss function')\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py\", line 709, in deserialize_keras_object\n        f'Unknown {printable_module_name}: {object_name}. Please ensure '\n\n    ValueError: Unknown loss function: binary_corssentropy. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.\n"]}]},{"cell_type":"code","source":["plt.plot(history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train','val'])\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":248},"id":"SWchFKuMAjJ3","executionInfo":{"status":"error","timestamp":1644654881866,"user_tz":-540,"elapsed":7,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"7b03dc26-f59c-428d-b577-4b7f56f3d1f6"},"execution_count":38,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-38-b3352076d127>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\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[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'epoch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'val'\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;31mNameError\u001b[0m: name 'history' is not defined"]}]},{"cell_type":"code","source":["print(train_seq.nbytes, train_oh.nbytes)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dgU01Q3KEekf","executionInfo":{"status":"ok","timestamp":1644654918380,"user_tz":-540,"elapsed":274,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"890144a1-c9be-484b-a42c-1e76e43829ba"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["8000000 4000000000\n"]}]},{"cell_type":"code","source":["model2=keras.Sequential()\n","model2.add(keras.layers.Embedding(500,16,input_length=100))\n","model2.add(keras.layers.SimpleRNN(8))\n","model2.add(keras.layers.Dense(1, activation='sigmoid'))"],"metadata":{"id":"NHSneDH5EndT","executionInfo":{"status":"ok","timestamp":1644655040765,"user_tz":-540,"elapsed":287,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}}},"execution_count":40,"outputs":[]},{"cell_type":"code","source":["model2.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FvtlXQAOFFVN","executionInfo":{"status":"ok","timestamp":1644655051451,"user_tz":-540,"elapsed":276,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"ac9fde51-5d86-47f3-b24d-babe4ebb5f92"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding (Embedding)       (None, 100, 16)           8000      \n","                                                                 \n"," simple_rnn_1 (SimpleRNN)    (None, 8)                 200       \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 9         \n","                                                                 \n","=================================================================\n","Total params: 8,209\n","Trainable params: 8,209\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n","model2.compile(optimizer=rmsprop, loss='binary_corssentropy', metrics=['accuracy'])\n","checkpoint_cb=keras.callbacks.ModelCheckpoint('best-embedding-model.h5', save_best_only=True)\n","early_stopping_cb= keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n","history = model2.fit(train_seq, train_target, epochs=100, batch_size=64, validation_data=(val_seq, val_target), callbacks=[checkpoint_cb, early_stopping_cb])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":804},"id":"_PQPg2Y2FH8F","executionInfo":{"status":"error","timestamp":1644655229545,"user_tz":-540,"elapsed":674,"user":{"displayName":"Jin Heo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiYKiOfEVjRqVXxpIJ4VaU8ICHjx_uSljL8K0Rw5w=s64","userId":"12568755051479383181"}},"outputId":"2a945430-a16f-4d5b-db9c-3cda1b7d192c"},"execution_count":46,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n"]},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-46-e8a7b1e81f38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcheckpoint_cb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelCheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'best-embedding-model.h5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_best_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mearly_stopping_cb\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEarlyStopping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestore_best_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_target\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheckpoint_cb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_cb\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[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1127\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\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-> 1129\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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   1130\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[1;32m   1131\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 810, in train_step\n        y, y_pred, sample_weight, regularization_losses=self.losses)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 184, in __call__\n        self.build(y_pred)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 133, in build\n        self._losses = tf.nest.map_structure(self._get_loss_object, self._losses)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 273, in _get_loss_object\n        loss = losses_mod.get(loss)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 2134, in get\n        return deserialize(identifier)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 2093, in deserialize\n        printable_module_name='loss function')\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py\", line 709, in deserialize_keras_object\n        f'Unknown {printable_module_name}: {object_name}. Please ensure '\n\n    ValueError: Unknown loss function: binary_corssentropy. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.\n"]}]},{"cell_type":"code","source":["plt.plot(history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train','val'])\n","plt.show()"],"metadata":{"id":"Ng5AivXRFcFi"},"execution_count":null,"outputs":[]}]}