File size: 46,347 Bytes
bffa47d |
1 |
{"metadata":{"accelerator":"TPU","colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":7297075,"sourceType":"datasetVersion","datasetId":4232737},{"sourceId":7302768,"sourceType":"datasetVersion","datasetId":4236811}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import tensorflow as tf\nimport string\nimport requests","metadata":{"execution":{"iopub.status.busy":"2023-12-29T13:56:37.898860Z","iopub.execute_input":"2023-12-29T13:56:37.899229Z","iopub.status.idle":"2023-12-29T13:56:48.948150Z","shell.execute_reply.started":"2023-12-29T13:56:37.899199Z","shell.execute_reply":"2023-12-29T13:56:48.947150Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"file_path = \"/kaggle/input/shakespeare1-txt/shakespeare1.txt\"\nwith open(file_path, \"r\", encoding=\"utf-8\") as file1:\n response = file1.read()","metadata":{"id":"yhsiyzKdD7cj","execution":{"iopub.status.busy":"2023-12-29T13:56:48.949988Z","iopub.execute_input":"2023-12-29T13:56:48.950559Z","iopub.status.idle":"2023-12-29T13:56:49.008071Z","shell.execute_reply.started":"2023-12-29T13:56:48.950531Z","shell.execute_reply":"2023-12-29T13:56:49.007139Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"response[0]","metadata":{"colab":{"background_save":true},"id":"1RhGm4XfD7gP","outputId":"77af50b0-0e52-4d21-bd3f-d87d16db58ab","execution":{"iopub.status.busy":"2023-12-29T13:56:49.009239Z","iopub.execute_input":"2023-12-29T13:56:49.009538Z","iopub.status.idle":"2023-12-29T13:56:49.016723Z","shell.execute_reply.started":"2023-12-29T13:56:49.009504Z","shell.execute_reply":"2023-12-29T13:56:49.015811Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"'T'"},"metadata":{}}]},{"cell_type":"code","source":"data = response.split('\\n')\ndata[0]","metadata":{"colab":{"background_save":true},"id":"P8pQeQhSD7jt","outputId":"81beef7f-8486-436a-ede3-c8229cdfda6d","execution":{"iopub.status.busy":"2023-12-29T13:56:49.017877Z","iopub.execute_input":"2023-12-29T13:56:49.018228Z","iopub.status.idle":"2023-12-29T13:56:49.042684Z","shell.execute_reply.started":"2023-12-29T13:56:49.018203Z","shell.execute_reply":"2023-12-29T13:56:49.041725Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"'This is the 100th Etext file presented by Project Gutenberg, and'"},"metadata":{}}]},{"cell_type":"code","source":"data = data[253:]","metadata":{"colab":{"background_save":true},"id":"ycZ6ZHQaD7lX","execution":{"iopub.status.busy":"2023-12-29T13:56:49.047145Z","iopub.execute_input":"2023-12-29T13:56:49.047665Z","iopub.status.idle":"2023-12-29T13:56:49.053276Z","shell.execute_reply.started":"2023-12-29T13:56:49.047634Z","shell.execute_reply":"2023-12-29T13:56:49.052354Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"data[0]","metadata":{"colab":{"background_save":true},"id":"Z1phy6B8D7na","outputId":"1d086b45-b14d-4667-b6a4-4f3aa589d79f","execution":{"iopub.status.busy":"2023-12-29T13:56:49.054706Z","iopub.execute_input":"2023-12-29T13:56:49.055084Z","iopub.status.idle":"2023-12-29T13:56:49.063962Z","shell.execute_reply.started":"2023-12-29T13:56:49.055053Z","shell.execute_reply":"2023-12-29T13:56:49.062927Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"' From fairest creatures we desire increase,'"},"metadata":{}}]},{"cell_type":"code","source":"len(data)","metadata":{"colab":{"background_save":true},"id":"pPn-2tkWD7pc","outputId":"6b4be9e7-d8a6-437a-b110-cf85ee9157e9","execution":{"iopub.status.busy":"2023-12-29T13:56:49.065583Z","iopub.execute_input":"2023-12-29T13:56:49.066027Z","iopub.status.idle":"2023-12-29T13:56:49.072531Z","shell.execute_reply.started":"2023-12-29T13:56:49.065994Z","shell.execute_reply":"2023-12-29T13:56:49.071518Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"124204"},"metadata":{}}]},{"cell_type":"code","source":"data = \" \".join(data)","metadata":{"colab":{"background_save":true},"id":"aYe7fz3vD7sA","execution":{"iopub.status.busy":"2023-12-29T13:56:49.073821Z","iopub.execute_input":"2023-12-29T13:56:49.074140Z","iopub.status.idle":"2023-12-29T13:56:49.089018Z","shell.execute_reply.started":"2023-12-29T13:56:49.074088Z","shell.execute_reply":"2023-12-29T13:56:49.088290Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"data[100]","metadata":{"colab":{"background_save":true},"id":"FUyYrECGHfQ3","outputId":"3f5c0522-f2a9-4edc-b805-fe406f2c7065","execution":{"iopub.status.busy":"2023-12-29T13:56:49.090192Z","iopub.execute_input":"2023-12-29T13:56:49.090449Z","iopub.status.idle":"2023-12-29T13:56:49.098592Z","shell.execute_reply.started":"2023-12-29T13:56:49.090427Z","shell.execute_reply":"2023-12-29T13:56:49.097696Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"'t'"},"metadata":{}}]},{"cell_type":"code","source":"def cleantext(doc):\n tokens = doc.split()\n table = str.maketrans('','',string.punctuation)\n tokens = [w.translate(table) for w in tokens]\n tokens = [word for word in tokens if word.isalpha()]\n tokens = [word.lower() for word in tokens]\n return tokens","metadata":{"colab":{"background_save":true},"id":"pAyasGbKHfVF","execution":{"iopub.status.busy":"2023-12-29T13:56:49.099933Z","iopub.execute_input":"2023-12-29T13:56:49.100424Z","iopub.status.idle":"2023-12-29T13:56:49.106762Z","shell.execute_reply.started":"2023-12-29T13:56:49.100389Z","shell.execute_reply":"2023-12-29T13:56:49.105816Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"tokens = cleantext(data)\nprint(tokens[:50])","metadata":{"colab":{"background_save":true},"id":"ScNJa-5DHfXv","outputId":"90372839-7b1f-4ce2-df98-025511a607dc","execution":{"iopub.status.busy":"2023-12-29T13:56:49.107945Z","iopub.execute_input":"2023-12-29T13:56:49.108241Z","iopub.status.idle":"2023-12-29T13:56:49.866629Z","shell.execute_reply.started":"2023-12-29T13:56:49.108218Z","shell.execute_reply":"2023-12-29T13:56:49.865722Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"['from', 'fairest', 'creatures', 'we', 'desire', 'increase', 'that', 'thereby', 'beautys', 'rose', 'might', 'never', 'die', 'but', 'as', 'the', 'riper', 'should', 'by', 'time', 'decease', 'his', 'tender', 'heir', 'might', 'bear', 'his', 'memory', 'but', 'thou', 'contracted', 'to', 'thine', 'own', 'bright', 'eyes', 'feedst', 'thy', 'lights', 'flame', 'with', 'selfsubstantial', 'fuel', 'making', 'a', 'famine', 'where', 'abundance', 'lies', 'thy']\n","output_type":"stream"}]},{"cell_type":"code","source":"len(tokens)","metadata":{"colab":{"background_save":true},"id":"UcimXXthHfZz","outputId":"b884342f-3fbb-4c5f-bc6d-a8cc1a9a777d","execution":{"iopub.status.busy":"2023-12-29T13:56:49.867872Z","iopub.execute_input":"2023-12-29T13:56:49.868172Z","iopub.status.idle":"2023-12-29T13:56:49.873930Z","shell.execute_reply.started":"2023-12-29T13:56:49.868145Z","shell.execute_reply":"2023-12-29T13:56:49.872995Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"898199"},"metadata":{}}]},{"cell_type":"code","source":"len(set(tokens)) #unique words","metadata":{"colab":{"background_save":true},"id":"aaBEiyZVHfb2","outputId":"bb9b5216-8a85-4582-f39c-8c8e11514eb5","execution":{"iopub.status.busy":"2023-12-29T13:56:49.875223Z","iopub.execute_input":"2023-12-29T13:56:49.875574Z","iopub.status.idle":"2023-12-29T13:56:49.939975Z","shell.execute_reply.started":"2023-12-29T13:56:49.875543Z","shell.execute_reply":"2023-12-29T13:56:49.938938Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"27956"},"metadata":{}}]},{"cell_type":"code","source":"length = 50 + 1\nlines = []\n\nfor i in range(length, len(tokens)):\n seq = tokens[i - length:i]\n line = ' '.join(seq)\n lines.append(line)\n if i > 200000:\n break\n\nprint(len(lines))","metadata":{"colab":{"background_save":true},"id":"JvZqBxNPJN0n","outputId":"896daabe-0a43-4c11-f601-7dfa75fe821b","execution":{"iopub.status.busy":"2023-12-29T13:56:49.944073Z","iopub.execute_input":"2023-12-29T13:56:49.944420Z","iopub.status.idle":"2023-12-29T13:56:50.445315Z","shell.execute_reply.started":"2023-12-29T13:56:49.944383Z","shell.execute_reply":"2023-12-29T13:56:50.444354Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"199951\n","output_type":"stream"}]},{"cell_type":"code","source":"lines[0]","metadata":{"colab":{"background_save":true},"id":"wLWrQZOOJN5J","outputId":"afdd7cd3-1d82-4b6c-a7a0-bcd0fa45b591","execution":{"iopub.status.busy":"2023-12-29T13:56:50.446527Z","iopub.execute_input":"2023-12-29T13:56:50.446891Z","iopub.status.idle":"2023-12-29T13:56:50.453494Z","shell.execute_reply.started":"2023-12-29T13:56:50.446857Z","shell.execute_reply":"2023-12-29T13:56:50.452418Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"'from fairest creatures we desire increase that thereby beautys rose might never die but as the riper should by time decease his tender heir might bear his memory but thou contracted to thine own bright eyes feedst thy lights flame with selfsubstantial fuel making a famine where abundance lies thy self'"},"metadata":{}}]},{"cell_type":"code","source":"tokens[50]","metadata":{"colab":{"background_save":true},"id":"CQjuiThgJN8y","outputId":"959fb156-7250-4dd5-f734-3aa0d35492fc","execution":{"iopub.status.busy":"2023-12-29T13:56:50.454720Z","iopub.execute_input":"2023-12-29T13:56:50.455005Z","iopub.status.idle":"2023-12-29T13:56:50.463159Z","shell.execute_reply.started":"2023-12-29T13:56:50.454966Z","shell.execute_reply":"2023-12-29T13:56:50.462220Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"'self'"},"metadata":{}}]},{"cell_type":"code","source":"lines[1]","metadata":{"colab":{"background_save":true},"id":"gHzXCwYvJN_F","outputId":"fa37e6c4-9e78-42ed-936e-06e30993ace7","execution":{"iopub.status.busy":"2023-12-29T13:56:50.464259Z","iopub.execute_input":"2023-12-29T13:56:50.464567Z","iopub.status.idle":"2023-12-29T13:56:50.471768Z","shell.execute_reply.started":"2023-12-29T13:56:50.464543Z","shell.execute_reply":"2023-12-29T13:56:50.470968Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"'fairest creatures we desire increase that thereby beautys rose might never die but as the riper should by time decease his tender heir might bear his memory but thou contracted to thine own bright eyes feedst thy lights flame with selfsubstantial fuel making a famine where abundance lies thy self thy'"},"metadata":{}}]},{"cell_type":"markdown","source":"## Build LSTM Model and Prepare X & Y","metadata":{"id":"xRNmMnInMY7B"}},{"cell_type":"code","source":"import numpy as np\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, LSTM, Embedding\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences","metadata":{"colab":{"background_save":true},"id":"IxbfwrZ3JOCK","execution":{"iopub.status.busy":"2023-12-29T13:56:50.472958Z","iopub.execute_input":"2023-12-29T13:56:50.473261Z","iopub.status.idle":"2023-12-29T13:56:50.588966Z","shell.execute_reply.started":"2023-12-29T13:56:50.473231Z","shell.execute_reply":"2023-12-29T13:56:50.588022Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"tokenizer = Tokenizer()\ntokenizer.fit_on_texts(lines)\nsequences = tokenizer.texts_to_sequences(lines)","metadata":{"colab":{"background_save":true},"id":"arGAw3x3JOD4","execution":{"iopub.status.busy":"2023-12-29T13:56:50.590149Z","iopub.execute_input":"2023-12-29T13:56:50.590410Z","iopub.status.idle":"2023-12-29T13:57:06.990239Z","shell.execute_reply.started":"2023-12-29T13:56:50.590388Z","shell.execute_reply":"2023-12-29T13:57:06.989229Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"sequences = np.array(sequences)\nX, y = sequences[:, :-1], sequences[:, -1]","metadata":{"colab":{"background_save":true},"id":"MZuzN3ebJOGG","execution":{"iopub.status.busy":"2023-12-29T13:57:06.991498Z","iopub.execute_input":"2023-12-29T13:57:06.991815Z","iopub.status.idle":"2023-12-29T13:57:07.995317Z","shell.execute_reply.started":"2023-12-29T13:57:06.991781Z","shell.execute_reply":"2023-12-29T13:57:07.994282Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"X[0]","metadata":{"colab":{"background_save":true},"id":"NVqG5lE_JOIP","outputId":"426a3144-89f9-46ea-83fb-22b7742daac1","execution":{"iopub.status.busy":"2023-12-29T13:57:07.996870Z","iopub.execute_input":"2023-12-29T13:57:07.997334Z","iopub.status.idle":"2023-12-29T13:57:08.007773Z","shell.execute_reply.started":"2023-12-29T13:57:07.997282Z","shell.execute_reply":"2023-12-29T13:57:08.006858Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"array([ 47, 1408, 1264, 37, 451, 1406, 9, 2766, 1158,\n 1213, 171, 132, 269, 20, 24, 1, 4782, 87,\n 30, 98, 4781, 18, 715, 1263, 171, 211, 18,\n 829, 20, 27, 3807, 4, 214, 121, 1212, 153,\n 13004, 31, 2765, 1847, 16, 13003, 13002, 754, 7,\n 3806, 99, 2430, 466, 31])"},"metadata":{}}]},{"cell_type":"code","source":"y[0]","metadata":{"colab":{"background_save":true},"id":"fYcWpjpHJOKL","outputId":"9a6df667-291a-4a0b-fd05-c28c681a83ea","execution":{"iopub.status.busy":"2023-12-29T13:57:08.008988Z","iopub.execute_input":"2023-12-29T13:57:08.009377Z","iopub.status.idle":"2023-12-29T13:57:08.017192Z","shell.execute_reply.started":"2023-12-29T13:57:08.009343Z","shell.execute_reply":"2023-12-29T13:57:08.016239Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"307"},"metadata":{}}]},{"cell_type":"code","source":"vocab_size = len(tokenizer.word_index) + 1","metadata":{"colab":{"background_save":true},"id":"ANrsMQsXPHGe","execution":{"iopub.status.busy":"2023-12-29T13:57:08.018535Z","iopub.execute_input":"2023-12-29T13:57:08.018829Z","iopub.status.idle":"2023-12-29T13:57:08.024815Z","shell.execute_reply.started":"2023-12-29T13:57:08.018805Z","shell.execute_reply":"2023-12-29T13:57:08.023851Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"y = to_categorical(y, num_classes = vocab_size)\nX.shape[1]","metadata":{"colab":{"background_save":true},"id":"JOrZqU-COuPb","outputId":"183ea328-c0dc-4c42-edf5-d2bc42c1566c","execution":{"iopub.status.busy":"2023-12-29T13:57:08.025881Z","iopub.execute_input":"2023-12-29T13:57:08.026152Z","iopub.status.idle":"2023-12-29T13:57:08.510076Z","shell.execute_reply.started":"2023-12-29T13:57:08.026118Z","shell.execute_reply":"2023-12-29T13:57:08.509084Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"50"},"metadata":{}}]},{"cell_type":"code","source":"seq_length = X.shape[1]","metadata":{"colab":{"background_save":true},"id":"Gs6-H2_kOuUb","execution":{"iopub.status.busy":"2023-12-29T13:57:08.511550Z","iopub.execute_input":"2023-12-29T13:57:08.511850Z","iopub.status.idle":"2023-12-29T13:57:08.515874Z","shell.execute_reply.started":"2023-12-29T13:57:08.511824Z","shell.execute_reply":"2023-12-29T13:57:08.514960Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":"## LSTM Model","metadata":{"id":"md-0DSqxPqTA"}},{"cell_type":"code","source":"model = Sequential()\nmodel.add(Embedding(vocab_size, 50, input_length = seq_length))\nmodel.add(LSTM(256, return_sequences = True))\nmodel.add(LSTM(256))\nmodel.add(Dense(128, activation = 'relu'))\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(vocab_size, activation = 'softmax'))","metadata":{"colab":{"background_save":true},"id":"-ONjzrlOOuZk","execution":{"iopub.status.busy":"2023-12-29T13:57:08.517355Z","iopub.execute_input":"2023-12-29T13:57:08.518033Z","iopub.status.idle":"2023-12-29T13:57:12.742524Z","shell.execute_reply.started":"2023-12-29T13:57:08.518007Z","shell.execute_reply":"2023-12-29T13:57:12.741745Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"colab":{"background_save":true},"id":"jOqo90T9Qdok","outputId":"fd51d2db-b242-4321-a772-1169690181bd","execution":{"iopub.status.busy":"2023-12-29T13:57:12.743548Z","iopub.execute_input":"2023-12-29T13:57:12.743804Z","iopub.status.idle":"2023-12-29T13:57:12.768168Z","shell.execute_reply.started":"2023-12-29T13:57:12.743781Z","shell.execute_reply":"2023-12-29T13:57:12.766834Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n embedding (Embedding) (None, 50, 50) 650450 \n \n lstm (LSTM) (None, 50, 256) 314368 \n \n lstm_1 (LSTM) (None, 256) 525312 \n \n dense (Dense) (None, 128) 32896 \n \n dense_1 (Dense) (None, 128) 16512 \n \n dense_2 (Dense) (None, 13009) 1678161 \n \n=================================================================\nTotal params: 3217699 (12.27 MB)\nTrainable params: 3217699 (12.27 MB)\nNon-trainable params: 0 (0.00 Byte)\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])","metadata":{"colab":{"background_save":true},"id":"cqrQq-wZQduv","execution":{"iopub.status.busy":"2023-12-29T13:57:12.769303Z","iopub.execute_input":"2023-12-29T13:57:12.769593Z","iopub.status.idle":"2023-12-29T13:57:12.787015Z","shell.execute_reply.started":"2023-12-29T13:57:12.769568Z","shell.execute_reply":"2023-12-29T13:57:12.786065Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"# model.fit(X, y, batch_size = 512, epochs = 10)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-x7sodiaQ5Kz","outputId":"7df24bb2-bb4f-4a1b-f9aa-e986e802da72","execution":{"iopub.status.busy":"2023-12-29T12:27:59.301514Z","iopub.execute_input":"2023-12-29T12:27:59.301787Z","iopub.status.idle":"2023-12-29T12:27:59.305646Z","shell.execute_reply.started":"2023-12-29T12:27:59.301759Z","shell.execute_reply":"2023-12-29T12:27:59.304781Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"# model.fit(X, y, batch_size = 512, epochs = 20)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T12:40:36.646987Z","iopub.execute_input":"2023-12-29T12:40:36.647682Z","iopub.status.idle":"2023-12-29T12:46:53.896796Z","shell.execute_reply.started":"2023-12-29T12:40:36.647651Z","shell.execute_reply":"2023-12-29T12:46:53.895934Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"Epoch 1/20\n391/391 [==============================] - 67s 150ms/step - loss: 6.9716 - accuracy: 0.0281\nEpoch 2/20\n391/391 [==============================] - 25s 63ms/step - loss: 6.6133 - accuracy: 0.0358\nEpoch 3/20\n391/391 [==============================] - 18s 46ms/step - loss: 6.3875 - accuracy: 0.0531\nEpoch 4/20\n391/391 [==============================] - 16s 40ms/step - loss: 6.1302 - accuracy: 0.0741\nEpoch 5/20\n391/391 [==============================] - 15s 39ms/step - loss: 5.9314 - accuracy: 0.0884\nEpoch 6/20\n391/391 [==============================] - 15s 37ms/step - loss: 5.7719 - accuracy: 0.0968\nEpoch 7/20\n391/391 [==============================] - 15s 38ms/step - loss: 5.6343 - accuracy: 0.1038\nEpoch 8/20\n391/391 [==============================] - 15s 38ms/step - loss: 5.5047 - accuracy: 0.1088\nEpoch 9/20\n391/391 [==============================] - 14s 36ms/step - loss: 5.3814 - accuracy: 0.1136\nEpoch 10/20\n391/391 [==============================] - 14s 36ms/step - loss: 5.2578 - accuracy: 0.1174\nEpoch 11/20\n391/391 [==============================] - 14s 36ms/step - loss: 5.1349 - accuracy: 0.1213\nEpoch 12/20\n391/391 [==============================] - 14s 36ms/step - loss: 5.0130 - accuracy: 0.1249\nEpoch 13/20\n391/391 [==============================] - 14s 35ms/step - loss: 4.8898 - accuracy: 0.1292\nEpoch 14/20\n391/391 [==============================] - 14s 36ms/step - loss: 4.7681 - accuracy: 0.1345\nEpoch 15/20\n391/391 [==============================] - 14s 35ms/step - loss: 4.6433 - accuracy: 0.1418\nEpoch 16/20\n391/391 [==============================] - 13s 34ms/step - loss: 4.5225 - accuracy: 0.1531\nEpoch 17/20\n391/391 [==============================] - 14s 35ms/step - loss: 4.4015 - accuracy: 0.1645\nEpoch 18/20\n391/391 [==============================] - 13s 34ms/step - loss: 4.2887 - accuracy: 0.1757\nEpoch 19/20\n391/391 [==============================] - 14s 36ms/step - loss: 4.1795 - accuracy: 0.1881\nEpoch 20/20\n391/391 [==============================] - 14s 36ms/step - loss: 4.0764 - accuracy: 0.2010\n","output_type":"stream"},{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.History at 0x7f98f7f59360>"},"metadata":{}}]},{"cell_type":"code","source":"# model.fit(X, y, batch_size = 512, epochs = 30)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T13:01:04.023989Z","iopub.execute_input":"2023-12-29T13:01:04.024370Z","iopub.status.idle":"2023-12-29T13:08:56.771629Z","shell.execute_reply.started":"2023-12-29T13:01:04.024340Z","shell.execute_reply":"2023-12-29T13:08:56.770704Z"},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"391/391 [==============================] - 65s 143ms/step - loss: 7.0002 - accuracy: 0.0286\nEpoch 2/30\n391/391 [==============================] - 25s 63ms/step - loss: 6.6495 - accuracy: 0.0351\nEpoch 3/30\n391/391 [==============================] - 18s 46ms/step - loss: 6.4912 - accuracy: 0.0438\nEpoch 4/30\n391/391 [==============================] - 15s 40ms/step - loss: 6.3627 - accuracy: 0.0497\nEpoch 5/30\n391/391 [==============================] - 13s 34ms/step - loss: 6.2490 - accuracy: 0.0586\nEpoch 6/30\n391/391 [==============================] - 13s 34ms/step - loss: 6.1308 - accuracy: 0.0674\nEpoch 7/30\n391/391 [==============================] - 13s 32ms/step - loss: 6.0226 - accuracy: 0.0772\nEpoch 8/30\n391/391 [==============================] - 13s 34ms/step - loss: 5.9310 - accuracy: 0.0852\nEpoch 9/30\n391/391 [==============================] - 13s 32ms/step - loss: 5.8482 - accuracy: 0.0904\nEpoch 10/30\n391/391 [==============================] - 12s 31ms/step - loss: 5.7721 - accuracy: 0.0954\nEpoch 11/30\n391/391 [==============================] - 13s 32ms/step - loss: 5.7005 - accuracy: 0.0998\nEpoch 12/30\n391/391 [==============================] - 12s 32ms/step - loss: 5.6321 - accuracy: 0.1040\nEpoch 13/30\n391/391 [==============================] - 12s 31ms/step - loss: 5.5635 - accuracy: 0.1076\nEpoch 14/30\n391/391 [==============================] - 13s 33ms/step - loss: 5.4987 - accuracy: 0.1100\nEpoch 15/30\n391/391 [==============================] - 12s 30ms/step - loss: 5.4345 - accuracy: 0.1143\nEpoch 16/30\n391/391 [==============================] - 13s 34ms/step - loss: 5.3701 - accuracy: 0.1172\nEpoch 17/30\n391/391 [==============================] - 12s 31ms/step - loss: 5.3069 - accuracy: 0.1204\nEpoch 18/30\n391/391 [==============================] - 12s 31ms/step - loss: 5.2460 - accuracy: 0.1232\nEpoch 19/30\n391/391 [==============================] - 12s 31ms/step - loss: 5.1847 - accuracy: 0.1254\nEpoch 20/30\n391/391 [==============================] - 13s 33ms/step - loss: 5.1271 - accuracy: 0.1276\nEpoch 21/30\n391/391 [==============================] - 13s 34ms/step - loss: 5.0692 - accuracy: 0.1299\nEpoch 22/30\n391/391 [==============================] - 12s 31ms/step - loss: 5.0126 - accuracy: 0.1327\nEpoch 23/30\n391/391 [==============================] - 12s 30ms/step - loss: 4.9579 - accuracy: 0.1352\nEpoch 24/30\n391/391 [==============================] - 12s 31ms/step - loss: 4.9052 - accuracy: 0.1390\nEpoch 25/30\n391/391 [==============================] - 12s 30ms/step - loss: 4.8509 - accuracy: 0.1423\nEpoch 26/30\n391/391 [==============================] - 12s 31ms/step - loss: 4.8031 - accuracy: 0.1459\nEpoch 27/30\n391/391 [==============================] - 12s 31ms/step - loss: 4.7521 - accuracy: 0.1497\nEpoch 28/30\n391/391 [==============================] - 12s 31ms/step - loss: 4.7064 - accuracy: 0.1528\nEpoch 29/30\n391/391 [==============================] - 12s 31ms/step - loss: 4.6608 - accuracy: 0.1569\nEpoch 30/30\n391/391 [==============================] - 12s 32ms/step - loss: 4.6175 - accuracy: 0.1611\n","output_type":"stream"},{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.History at 0x7b35b0ccc730>"},"metadata":{}}]},{"cell_type":"code","source":"# model.fit(X, y, batch_size = 256, epochs = 30)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T13:19:42.484729Z","iopub.execute_input":"2023-12-29T13:19:42.485090Z","iopub.status.idle":"2023-12-29T13:30:34.498411Z","shell.execute_reply.started":"2023-12-29T13:19:42.485053Z","shell.execute_reply":"2023-12-29T13:30:34.497411Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"Epoch 1/30\n782/782 [==============================] - 76s 83ms/step - loss: 6.8855 - accuracy: 0.0305\nEpoch 2/30\n782/782 [==============================] - 25s 33ms/step - loss: 6.5269 - accuracy: 0.0441\nEpoch 3/30\n782/782 [==============================] - 22s 28ms/step - loss: 6.3194 - accuracy: 0.0594\nEpoch 4/30\n782/782 [==============================] - 21s 27ms/step - loss: 6.1363 - accuracy: 0.0737\nEpoch 5/30\n782/782 [==============================] - 20s 25ms/step - loss: 5.9864 - accuracy: 0.0841\nEpoch 6/30\n782/782 [==============================] - 19s 24ms/step - loss: 5.8584 - accuracy: 0.0931\nEpoch 7/30\n782/782 [==============================] - 19s 25ms/step - loss: 5.7439 - accuracy: 0.1016\nEpoch 8/30\n782/782 [==============================] - 20s 25ms/step - loss: 5.6409 - accuracy: 0.1069\nEpoch 9/30\n782/782 [==============================] - 18s 24ms/step - loss: 5.5460 - accuracy: 0.1116\nEpoch 10/30\n782/782 [==============================] - 19s 25ms/step - loss: 5.4542 - accuracy: 0.1154\nEpoch 11/30\n782/782 [==============================] - 19s 25ms/step - loss: 5.3665 - accuracy: 0.1186\nEpoch 12/30\n782/782 [==============================] - 19s 24ms/step - loss: 5.2822 - accuracy: 0.1213\nEpoch 13/30\n782/782 [==============================] - 18s 24ms/step - loss: 5.2011 - accuracy: 0.1241\nEpoch 14/30\n782/782 [==============================] - 19s 25ms/step - loss: 5.1200 - accuracy: 0.1264\nEpoch 15/30\n782/782 [==============================] - 18s 23ms/step - loss: 5.0415 - accuracy: 0.1294\nEpoch 16/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.9638 - accuracy: 0.1318\nEpoch 17/30\n782/782 [==============================] - 19s 24ms/step - loss: 4.8896 - accuracy: 0.1350\nEpoch 18/30\n782/782 [==============================] - 18s 24ms/step - loss: 4.8164 - accuracy: 0.1405\nEpoch 19/30\n782/782 [==============================] - 19s 24ms/step - loss: 4.7487 - accuracy: 0.1456\nEpoch 20/30\n782/782 [==============================] - 18s 24ms/step - loss: 4.6839 - accuracy: 0.1516\nEpoch 21/30\n782/782 [==============================] - 19s 24ms/step - loss: 4.6234 - accuracy: 0.1574\nEpoch 22/30\n782/782 [==============================] - 19s 24ms/step - loss: 4.5664 - accuracy: 0.1629\nEpoch 23/30\n782/782 [==============================] - 19s 24ms/step - loss: 4.5137 - accuracy: 0.1684\nEpoch 24/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.4661 - accuracy: 0.1730\nEpoch 25/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.4183 - accuracy: 0.1778\nEpoch 26/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.3772 - accuracy: 0.1821\nEpoch 27/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.3369 - accuracy: 0.1861\nEpoch 28/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.2966 - accuracy: 0.1907\nEpoch 29/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.2604 - accuracy: 0.1948\nEpoch 30/30\n782/782 [==============================] - 18s 23ms/step - loss: 4.2243 - accuracy: 0.1994\n","output_type":"stream"},{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.History at 0x7af2375c98d0>"},"metadata":{}}]},{"cell_type":"code","source":"# model.fit(X, y, batch_size = 32, epochs = 10)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T13:35:18.155479Z","iopub.execute_input":"2023-12-29T13:35:18.155863Z","iopub.status.idle":"2023-12-29T13:53:09.367440Z","shell.execute_reply.started":"2023-12-29T13:35:18.155832Z","shell.execute_reply":"2023-12-29T13:53:09.366410Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"Epoch 1/10\n6249/6249 [==============================] - 139s 20ms/step - loss: 6.7749 - accuracy: 0.0337\nEpoch 2/10\n6249/6249 [==============================] - 98s 16ms/step - loss: 6.4526 - accuracy: 0.0480\nEpoch 3/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 6.2278 - accuracy: 0.0668\nEpoch 4/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 6.0589 - accuracy: 0.0814\nEpoch 5/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 5.9362 - accuracy: 0.0888\nEpoch 6/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 5.8801 - accuracy: 0.0939\nEpoch 7/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 5.7753 - accuracy: 0.0990\nEpoch 8/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 5.6742 - accuracy: 0.1037\nEpoch 9/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 5.5803 - accuracy: 0.1080\nEpoch 10/10\n6249/6249 [==============================] - 96s 15ms/step - loss: 5.4890 - accuracy: 0.1111\n","output_type":"stream"},{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.History at 0x78b35c8ab4c0>"},"metadata":{}}]},{"cell_type":"code","source":"model.fit(X, y, batch_size = 200, epochs = 50)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T13:57:33.192289Z","iopub.execute_input":"2023-12-29T13:57:33.192676Z","iopub.status.idle":"2023-12-29T14:28:14.553076Z","shell.execute_reply.started":"2023-12-29T13:57:33.192646Z","shell.execute_reply":"2023-12-29T14:28:14.552157Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"Epoch 1/50\n1000/1000 [==============================] - 86s 78ms/step - loss: 6.8116 - accuracy: 0.0336\nEpoch 2/50\n1000/1000 [==============================] - 41s 41ms/step - loss: 6.4579 - accuracy: 0.0472\nEpoch 3/50\n1000/1000 [==============================] - 39s 39ms/step - loss: 6.1781 - accuracy: 0.0711\nEpoch 4/50\n1000/1000 [==============================] - 37s 37ms/step - loss: 5.9738 - accuracy: 0.0896\nEpoch 5/50\n1000/1000 [==============================] - 36s 36ms/step - loss: 5.8402 - accuracy: 0.0969\nEpoch 6/50\n1000/1000 [==============================] - 36s 36ms/step - loss: 5.7250 - accuracy: 0.1023\nEpoch 7/50\n1000/1000 [==============================] - 36s 36ms/step - loss: 5.6204 - accuracy: 0.1062\nEpoch 8/50\n1000/1000 [==============================] - 36s 36ms/step - loss: 5.5223 - accuracy: 0.1091\nEpoch 9/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 5.4319 - accuracy: 0.1127\nEpoch 10/50\n1000/1000 [==============================] - 36s 36ms/step - loss: 5.3424 - accuracy: 0.1153\nEpoch 11/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 5.2541 - accuracy: 0.1181\nEpoch 12/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 5.1669 - accuracy: 0.1201\nEpoch 13/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 5.0801 - accuracy: 0.1225\nEpoch 14/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.9960 - accuracy: 0.1243\nEpoch 15/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.9130 - accuracy: 0.1268\nEpoch 16/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.8334 - accuracy: 0.1297\nEpoch 17/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.7552 - accuracy: 0.1332\nEpoch 18/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.6792 - accuracy: 0.1375\nEpoch 19/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.6070 - accuracy: 0.1420\nEpoch 20/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.5382 - accuracy: 0.1464\nEpoch 21/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.4734 - accuracy: 0.1524\nEpoch 22/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.4114 - accuracy: 0.1568\nEpoch 23/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.3484 - accuracy: 0.1634\nEpoch 24/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.2920 - accuracy: 0.1683\nEpoch 25/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.2350 - accuracy: 0.1739\nEpoch 26/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.1787 - accuracy: 0.1798\nEpoch 27/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.1249 - accuracy: 0.1865\nEpoch 28/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.0741 - accuracy: 0.1920\nEpoch 29/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 4.0235 - accuracy: 0.1977\nEpoch 30/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.9729 - accuracy: 0.2036\nEpoch 31/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.9252 - accuracy: 0.2100\nEpoch 32/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.8798 - accuracy: 0.2152\nEpoch 33/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.8323 - accuracy: 0.2213\nEpoch 34/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.7874 - accuracy: 0.2265\nEpoch 35/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.7432 - accuracy: 0.2330\nEpoch 36/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.7000 - accuracy: 0.2384\nEpoch 37/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.6589 - accuracy: 0.2444\nEpoch 38/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.6148 - accuracy: 0.2504\nEpoch 39/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.5715 - accuracy: 0.2563\nEpoch 40/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.5324 - accuracy: 0.2616\nEpoch 41/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.4935 - accuracy: 0.2668\nEpoch 42/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.4588 - accuracy: 0.2725\nEpoch 43/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.4164 - accuracy: 0.2782\nEpoch 44/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.3732 - accuracy: 0.2848\nEpoch 45/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.3382 - accuracy: 0.2898\nEpoch 46/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.3009 - accuracy: 0.2952\nEpoch 47/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.2633 - accuracy: 0.3007\nEpoch 48/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.2275 - accuracy: 0.3071\nEpoch 49/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.1900 - accuracy: 0.3112\nEpoch 50/50\n1000/1000 [==============================] - 35s 35ms/step - loss: 3.1522 - accuracy: 0.3176\n","output_type":"stream"},{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.History at 0x785ed8adb640>"},"metadata":{}}]},{"cell_type":"code","source":"seed_text=\"From fairest creatures we desire increase\"\nseed_text","metadata":{"id":"MJgAh-OtQ5PC","execution":{"iopub.status.busy":"2023-12-29T14:29:37.954259Z","iopub.execute_input":"2023-12-29T14:29:37.954674Z","iopub.status.idle":"2023-12-29T14:29:37.960890Z","shell.execute_reply.started":"2023-12-29T14:29:37.954644Z","shell.execute_reply":"2023-12-29T14:29:37.959886Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"'From fairest creatures we desire increase'"},"metadata":{}}]},{"cell_type":"code","source":"def generate_text_seq(model, tokenizer, text_seq_length, seed_text, n_words):\n for _ in range(n_words):\n encoded = tokenizer.texts_to_sequences([seed_text])[0]\n encoded = pad_sequences([encoded], maxlen=text_seq_length, truncating='pre')\n \n # Use predict instead of predict_classes\n y_predict = model.predict(encoded)\n \n predicted_word_index = np.argmax(y_predict)\n predicted_word = \"\"\n \n for word, index in tokenizer.word_index.items():\n if index == predicted_word_index:\n predicted_word = word\n break\n\n seed_text += \" \" + predicted_word\n\n print(seed_text)\n","metadata":{"id":"59_Zpuv9hWL3","execution":{"iopub.status.busy":"2023-12-29T14:29:40.563907Z","iopub.execute_input":"2023-12-29T14:29:40.564817Z","iopub.status.idle":"2023-12-29T14:29:40.571282Z","shell.execute_reply.started":"2023-12-29T14:29:40.564783Z","shell.execute_reply":"2023-12-29T14:29:40.570368Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"generate_text_seq(model, tokenizer, seq_length, seed_text, 100)","metadata":{"id":"_2scWjB4hWPS","execution":{"iopub.status.busy":"2023-12-29T14:29:46.743900Z","iopub.execute_input":"2023-12-29T14:29:46.744274Z","iopub.status.idle":"2023-12-29T14:29:53.216058Z","shell.execute_reply.started":"2023-12-29T14:29:46.744244Z","shell.execute_reply":"2023-12-29T14:29:53.215148Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 1s 695ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 23ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 19ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 19ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 23ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 23ms/step\n1/1 [==============================] - 0s 23ms/step\n1/1 [==============================] - 0s 24ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 23ms/step\nFrom fairest creatures we desire increase into the world kings known with horrors it is reported it is the breathing plague of base nature le beau proculeius staid you captain first lord we have deservd caesar royal instrument i th cold ground when we have assaild them into dotage enter a messenger hastily messenger and of rome menenius i am sorry to you my lord ham i will not till answering down to thee and shrive you to my faith i have engagd the approved means to blow the king and let the ports of many simples that are silenced the rushes and you markd me\n","output_type":"stream"}]}]} |