5cleaned_Heaviest_Project.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6199,"status":"ok","timestamp":1681900502128,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"Ug8vfVBeUrQw","outputId":"0bcfe5be-c301-411d-a891-1cedfaba2d29"},"outputs":[{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n","[nltk_data] Downloading package wordnet to /root/nltk_data...\n","[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"]},{"data":{"text/plain":["True"]},"execution_count":1,"metadata":{},"output_type":"execute_result"}],"source":["from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Conv1D, Conv1DTranspose, LeakyReLU, Dropout, Embedding, Concatenate\n","from tensorflow.keras.preprocessing import text, sequence\n","from tensorflow.keras.utils import pad_sequences, to_categorical\n","\n","import pickle as pkl\n","from matplotlib import pyplot as plt\n","\n","import re \n","import pandas as pd \n","import numpy as np \n","from numpy import zeros\n","from numpy import ones\n","from numpy import asarray\n","from numpy.random import randn\n","from numpy.random import randint\n","from numpy.linalg import norm\n","\n","import nltk\n","from nltk.stem import WordNetLemmatizer\n","from nltk.tokenize import line_tokenize\n","from nltk.tokenize import word_tokenize\n","\n","nltk.download('punkt')\n","nltk.download('stopwords')\n","nltk.download('wordnet')\n","nltk.download('omw-1.4')"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28308,"status":"ok","timestamp":1681900530431,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"VeVpF6SVFETd"},"outputs":[{"ename":"MessageError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m\u003cipython-input-2-d5df0069828e\u003e\u001b[0m in \u001b[0;36m\u003ccell line: 2\u003e\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----\u003e 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\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/python3.9/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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    102\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--\u003e 103\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    104\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    130\u001b[0m   )\n\u001b[1;32m    131\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--\u003e 132\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    133\u001b[0m         \u001b[0;34m'request_auth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'authType'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'dfs_ephemeral'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     )\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    174\u001b[0m       \u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpect_reply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m   )\n\u001b[0;32m--\u003e 176\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\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/python3.9/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    101\u001b[0m     ):\n\u001b[1;32m    102\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--\u003e 103\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\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    104\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":1447,"status":"ok","timestamp":1681900531875,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"UHqTUhzzd0sk"},"outputs":[],"source":["df = pd.read_csv('drive/MyDrive/ColabNotebooks/train_split_data_balance.csv')"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":354},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1681900532555,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"ytSBYkDoy0Pk","outputId":"c5c0ea5b-386b-4a2b-8d3e-fc60bbf2ab80"},"outputs":[{"data":{"text/html":["\n","  \u003cdiv id=\"df-9d7d810f-abf3-4298-8bde-2c2a75eab45e\"\u003e\n","    \u003cdiv class=\"colab-df-container\"\u003e\n","      \u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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Visit the ' +\n","            '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      \u003c/script\u003e\n","    \u003c/div\u003e\n","  \u003c/div\u003e\n","  "],"text/plain":["                    id                                          tweet_url  \\\n","0  1107720189762785281  https://twitter.com/user/status/11077201897627...   \n","1  1064319619199102976  https://twitter.com/user/status/10643196191991...   \n","2  1107125500126916608  https://twitter.com/user/status/11071255001269...   \n","3  1062682630846627840  https://twitter.com/user/status/10626826308466...   \n","4  1045031269204201473  https://twitter.com/user/status/10450312692042...   \n","\n","                                             img_url     labels  labels_value  \\\n","0  http://pbs.twimg.com/tweet_video_thumb/D19pot-...  [0, 0, 0]             0   \n","1     http://pbs.twimg.com/media/DsU5C8sX4AEncr2.jpg  [0, 5, 5]             5   \n","2     http://pbs.twimg.com/media/D11MxN6W0AAl1HU.jpg  [1, 1, 1]             1   \n","3     http://pbs.twimg.com/media/Dr9oOEqUcAEuLLU.jpg  [5, 3, 3]             3   \n","4     http://pbs.twimg.com/media/DoCyVOUXsAATbxh.jpg  [0, 1, 1]             1   \n","\n","                                          tweet_text  \\\n","0  Nigga been jammin have east like im from New Y...   \n","1  Everyone say hello to my retarded son https://...   \n","2  He killed his babymother then killed their dau...   \n","3                     Faggot https://t.co/icwuxgx8Tq   \n","4  When the desert snake startles  the female red...   \n","\n","                                labels_str  \n","0        ['NotHate', 'NotHate', 'NotHate']  \n","1    ['NotHate', 'OtherHate', 'OtherHate']  \n","2           ['Racist', 'Racist', 'Racist']  \n","3  ['OtherHate', 'Homophobe', 'Homophobe']  \n","4          ['NotHate', 'Racist', 'Racist']  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1681900544643,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"hGqdurBzyqhO"},"outputs":[],"source":["kaam_ka_df = df[['tweet_text', 'labels_value']]"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1681900544644,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"ZEqhcoxm2LHB"},"outputs":[],"source":["# what we don't need\n","\n","# from sklearn.feature_extraction.text import CountVectorizer\n","# from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","# cv = CountVectorizer()\n","# stemer = nltk.stem.PorterStemmer()\n","# tfidf = TfidfVectorizer()"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":519,"status":"ok","timestamp":1681900545801,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"rgFWTsMo7-vj"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1681900546346,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"hn244X77DEPB","outputId":"3cba8099-a750-4b0e-8797-d7d182eb8ddc"},"outputs":[{"data":{"text/plain":["\u003cbound method NDFrame.describe of                                               tweet_text  labels_value\n","0      Nigga been jammin have east like im from New Y...             0\n","1      Everyone say hello to my retarded son https://...             5\n","2      He killed his babymother then killed their dau...             1\n","3                         Faggot https://t.co/icwuxgx8Tq             3\n","4      When the desert snake startles  the female red...             1\n","...                                                  ...           ...\n","32792  Someone take this cunt's phone. https://t.co/i...             2\n","32793  I smell crime ! Nigga's gon relate  🤣🦅 #machgo...             1\n","32794  lmao son this nigga is going to be bbq chicken...             1\n","32795  @moneymari97 Yeah nigga you done put the endin...             0\n","32796  All ISIS-held territory '100 per cent eliminat...             0\n","\n","[32797 rows x 2 columns]\u003e"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["kaam_ka_df.describe"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":774,"status":"ok","timestamp":1681900559722,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"lfyfFe0MzVIw"},"outputs":[],"source":["x = kaam_ka_df['tweet_text']\n","y = kaam_ka_df['labels_value']"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1681900559723,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"k0ZF2PQEVanT","outputId":"03163ea3-0d08-4b8f-b163-022d90eb2962"},"outputs":[{"data":{"text/plain":["0        0\n","1        5\n","2        1\n","3        3\n","4        1\n","        ..\n","32792    2\n","32793    1\n","32794    1\n","32795    0\n","32796    0\n","Name: labels_value, Length: 32797, dtype: int64"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["kaam_ka_df['labels_value']"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":12957,"status":"ok","timestamp":1681900574389,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"fnsCE2KV8neV"},"outputs":[],"source":["words = dict()\n","\n","def add_to_dict(d, filename):\n","  with open(filename, 'r') as f:\n","    for line in f.readlines():\n","      line = line.split(' ')\n","\n","      try:\n","        d[line[0]] = np.array(line[1:], dtype=float)\n","      except:\n","        continue\n","\n","add_to_dict(words, 'drive/MyDrive/ColabNotebooks/glove.6B.50d.txt')\n"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":2370,"status":"ok","timestamp":1681900576757,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"Y4etex4bWrbe"},"outputs":[],"source":["pkl.dump(words, open('words.pkl', 'wb'))"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":752,"status":"ok","timestamp":1681900577506,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"p9LHJWU3_-uE","outputId":"2e3a2782-8faf-4a62-e82b-1a18e1830c9e"},"outputs":[{"data":{"text/plain":["400000"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["listofwords =[]\n","for key in words:\n","  listofwords.append([key, words[key]])\n","len(listofwords)"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1681900579441,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"bx1pQN6VQPtV"},"outputs":[],"source":["yy = np.array(y).reshape(-1,)\n","y = np.array(y)\n","y = to_categorical(y, num_classes = 6, dtype = 'int')"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1681900581969,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"Si5BPn_beJDs","outputId":"e578f024-32f7-40c0-f352-d41520c1f275"},"outputs":[{"data":{"text/plain":["(array([0, 5, 1, ..., 1, 0, 0]),\n"," array([[1, 0, 0, 0, 0, 0],\n","        [0, 0, 0, 0, 0, 1],\n","        [0, 1, 0, 0, 0, 0],\n","        ...,\n","        [0, 1, 0, 0, 0, 0],\n","        [1, 0, 0, 0, 0, 0],\n","        [1, 0, 0, 0, 0, 0]]))"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["yy,y "]},{"cell_type":"markdown","metadata":{"id":"0sxTYYKrKJBW"},"source":["size of the data set is 2782\n"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2288,"status":"ok","timestamp":1681900589082,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"h4_AFrXPChno","outputId":"528df591-05c0-4e06-f2ca-abd99aaedbbe"},"outputs":[{"data":{"text/plain":["['nigga',\n"," 'been',\n"," 'jammin',\n"," 'have',\n"," 'east',\n"," 'like',\n"," 'im',\n"," 'from',\n"," 'new',\n"," 'york',\n"," ':']"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["lemmatizer = WordNetLemmatizer()\n","\n","def message_to_token_list(s):\n","  tokens = word_tokenize(s)\n","  lowercased_tokens = [t.lower() for t in tokens]\n","  lemmatized_tokens = [lemmatizer.lemmatize(t) for t in lowercased_tokens]\n","  for j in range(0, len(lemmatized_tokens)):\n","    if(lemmatized_tokens[j].__contains__('@') or lemmatized_tokens[j].__contains__('http')):\n","      lemmatized_tokens[j] = ''\n","  useful_tokens = [t for t in lemmatized_tokens if t in words]\n","  return useful_tokens\n","\n","message_to_token_list(x[0])"]},{"cell_type":"code","execution_count":18,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1681900589082,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"gAx3XBHvDg8S"},"outputs":[],"source":["vocabulary = []\n","\n","def message_to_word_vectors(message, word_dict=words):\n","  vectors = []\n","\n","  for token in message:\n","    if token not in word_dict:\n","      continue\n","    \n","    token_vector = word_dict[token]\n","    vocabulary.append([token, words[token]])\n","    vectors.append(token_vector)\n","  \n","  return np.array(vectors, dtype=float)"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21256,"status":"ok","timestamp":1681900610883,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"W5TrH4oIDkbE","outputId":"8280a7df-3cdf-401d-8394-7233c74f5e5e"},"outputs":[{"name":"stderr","output_type":"stream","text":["\u003cipython-input-19-af4dcd5db64e\u003e:3: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  x[counter]=message_to_word_vectors(message_to_token_list(curr))\n"]}],"source":["counter=0;\n","for curr in x:\n","  x[counter]=message_to_word_vectors(message_to_token_list(curr))\n","  counter=counter+1\n"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1681900610883,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"_IhXpH0A9Koc","outputId":"080d9bc4-205a-4618-d1e0-1d390c6ec707"},"outputs":[{"data":{"text/plain":["393718"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["len(vocabulary)"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1681900610884,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"1JBDVXoD5lty","outputId":"e2ab7e9e-f1ae-4784-cdcc-4fba8842143b"},"outputs":[{"data":{"text/plain":["(11, 50)"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["x[0].shape"]},{"cell_type":"code","execution_count":22,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1681900610884,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"rxsUOoWTESti"},"outputs":[],"source":["xx = []\n","\n","for i in x:\n","  xx.append(i)\n"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":752,"status":"ok","timestamp":1681900611632,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"NvkijLFngZtO"},"outputs":[],"source":["xxx = pad_sequences(xx, maxlen = 25, padding = 'pre', value = 0.000, dtype = 'float')"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1681900611632,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"01zKEI_9gzQZ","outputId":"69d06897-4812-4b1b-db70-2fd039f0d09a"},"outputs":[{"data":{"text/plain":["(32797, 25, 50)"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["xxx.shape"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1681900611633,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"e_X_OjtAg8aO","outputId":"c9bdf598-303b-4c35-ace0-2fdc2d8025a8"},"outputs":[{"data":{"text/plain":["(array([[1, 0, 0, 0, 0, 0],\n","        [0, 0, 0, 0, 0, 1],\n","        [0, 1, 0, 0, 0, 0],\n","        ...,\n","        [0, 1, 0, 0, 0, 0],\n","        [1, 0, 0, 0, 0, 0],\n","        [1, 0, 0, 0, 0, 0]]),\n"," array([0, 5, 1, ..., 1, 0, 0]))"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["y,yy"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1681900611633,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"Fi5RDT77g9DR","outputId":"3b6839ed-535d-4062-c0b4-2f889c9701cd"},"outputs":[{"data":{"text/plain":["((32797, 25, 50), (32797, 6), (32797,))"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["xxx.shape, y.shape, yy.shape"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1681900613509,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"qRt02HSg70vk"},"outputs":[],"source":["vector_length = xxx.shape[1]"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1681900614074,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"0OzE0ng8UoZA","outputId":"506d70c5-f503-4f65-86c1-6b31fad18e53"},"outputs":[{"data":{"text/plain":["25"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["vector_length"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1681900615543,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"6OT7y_6NnUaf","outputId":"c2f8c25c-1714-4f32-a3a8-c490915eb6ab"},"outputs":[{"data":{"text/plain":["array([ 1.9511e-01,  5.0739e-01,  1.4709e-03,  4.1914e-02, -1.6759e-01,\n","        3.7517e-02, -1.3970e+00, -9.2398e-01, -2.4296e-01, -1.5171e-01,\n","       -4.7829e-01,  5.4612e-02, -2.4986e-01,  3.8398e-01,  1.6182e-02,\n","        3.4938e-01, -2.2627e-01,  8.6618e-02, -4.1001e-01, -1.8139e-01,\n","        7.5607e-01, -2.6200e-02, -6.9557e-01,  1.0874e-01, -4.7539e-01,\n","       -1.8095e+00, -1.6940e-01, -5.9863e-02, -1.6806e-01, -9.4546e-02,\n","        3.6610e+00,  4.1462e-02, -2.9161e-01, -6.9772e-01,  3.0805e-01,\n","       -2.8457e-01,  1.3217e-01, -7.6430e-03, -9.2390e-02, -4.9237e-01,\n","       -2.7055e-01,  6.0425e-02,  9.5107e-02, -2.3679e-01, -8.6108e-02,\n","        1.0243e+00, -2.2779e-01,  3.0488e-02, -1.4272e-01,  4.5411e-01])"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["xxx[0][22]"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":5313,"status":"ok","timestamp":1681900724879,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"fCDfniSoU7x5","outputId":"3fc20603-e607-45da-cf3b-2361857f5cfa"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"]},{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 1)]          0           []                               \n","                                                                                                  \n"," embedding (Embedding)          (None, 1, 50)        300         ['input_1[0][0]']                \n","                                                                                                  \n"," dense (Dense)                  (None, 1, 1250)      63750       ['embedding[0][0]']              \n","                                                                                                  \n"," input_2 (InputLayer)           [(None, 25, 50)]     0           []                               \n","                                                                                                  \n"," reshape (Reshape)              (None, 25, 50)       0           ['dense[0][0]']                  \n","                                                                                                  \n"," concatenate (Concatenate)      (None, 25, 100)      0           ['input_2[0][0]',                \n","                                                                  'reshape[0][0]']                \n","                                                                                                  \n"," conv1d (Conv1D)                (None, 13, 128)      38528       ['concatenate[0][0]']            \n","                                                                                                  \n"," leaky_re_lu (LeakyReLU)        (None, 13, 128)      0           ['conv1d[0][0]']                 \n","                                                                                                  \n"," conv1d_1 (Conv1D)              (None, 7, 128)       49280       ['leaky_re_lu[0][0]']            \n","                                                                                                  \n"," leaky_re_lu_1 (LeakyReLU)      (None, 7, 128)       0           ['conv1d_1[0][0]']               \n","                                                                                                  \n"," flatten (Flatten)              (None, 896)          0           ['leaky_re_lu_1[0][0]']          \n","                                                                                                  \n"," dropout (Dropout)              (None, 896)          0           ['flatten[0][0]']                \n","                                                                                                  \n"," dense_1 (Dense)                (None, 1)            897         ['dropout[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 152,755\n","Trainable params: 152,755\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n","None\n"]},{"data":{"image/png":"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\n","text/plain":["\u003cIPython.core.display.Image object\u003e"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["def define_discriminator(in_shape=(25,50), n_classes=6):\n","\tin_label = Input(shape=(1,))\n","\tli = Embedding(n_classes, 50)(in_label)\n"," \n","\tn_nodes = in_shape[0] * in_shape[1] \n","\tli = Dense(n_nodes)(li)  \n","\tli = Reshape((in_shape[0], 50))(li)  \n"," \n","\tin_image = Input(shape=in_shape) \n"," \n","\t# in_image = Dense(n_nodes)(in_image)\n","\t# in_image = Reshape((in_shape[0], 50))(in_image)\n","\n","\tmerge = Concatenate()([in_image, li]) \n","    \n","\tfe = Conv1D(128, 3, strides=2, padding='same')(merge) \n","\tfe = LeakyReLU(alpha=0.2)(fe)\n","\t# downsample\n","\tfe = Conv1D(128, 3, strides=2, padding='same')(fe) #8x8x128\n","\tfe = LeakyReLU(alpha=0.2)(fe)\n","\tfe = Flatten()(fe)  #8192  (8*8*128=8192)\n","\t# dropout\n","\tfe = Dropout(0.4)(fe)\n","\t# output\n","\tout_layer = Dense(1, activation='sigmoid')(fe)  #Shape=1\n","    \n","\tmodel = Model([in_image, in_label], out_layer)\n","\topt = Adam(lr=0.0002, beta_1=0.5)\n","\tmodel.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n","\treturn model\n","\n","\n","\n","\n","test_discr = define_discriminator()\n","print(test_discr.summary())\n","from tensorflow.keras.utils import plot_model\n","plot_model(test_discr, to_file='discr.png', show_shapes=True)\n"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2068,"status":"ok","timestamp":1681900741747,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"ViA-P6b4WSi2","outputId":"0497d610-a6c1-4ab4-e1c4-ba303cf4f6c7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_2\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_6 (InputLayer)           [(None, 100)]        0           []                               \n","                                                                                                  \n"," input_5 (InputLayer)           [(None, 1)]          0           []                               \n","                                                                                                  \n"," dense_5 (Dense)                (None, 1000)         101000      ['input_6[0][0]']                \n","                                                                                                  \n"," embedding_2 (Embedding)        (None, 1, 50)        150         ['input_5[0][0]']                \n","                                                                                                  \n"," leaky_re_lu_7 (LeakyReLU)      (None, 1000)         0           ['dense_5[0][0]']                \n","                                                                                                  \n"," dense_4 (Dense)                (None, 1, 100)       5100        ['embedding_2[0][0]']            \n","                                                                                                  \n"," reshape_4 (Reshape)            (None, 100, 10)      0           ['leaky_re_lu_7[0][0]']          \n","                                                                                                  \n"," reshape_3 (Reshape)            (None, 100, 1)       0           ['dense_4[0][0]']                \n","                                                                                                  \n"," concatenate_2 (Concatenate)    (None, 100, 11)      0           ['reshape_4[0][0]',              \n","                                                                  'reshape_3[0][0]']              \n","                                                                                                  \n"," conv1d_transpose_4 (Conv1DTran  (None, 200, 10)     450         ['concatenate_2[0][0]']          \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_8 (LeakyReLU)      (None, 200, 10)      0           ['conv1d_transpose_4[0][0]']     \n","                                                                                                  \n"," conv1d_transpose_5 (Conv1DTran  (None, 400, 10)     410         ['leaky_re_lu_8[0][0]']          \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_9 (LeakyReLU)      (None, 400, 10)      0           ['conv1d_transpose_5[0][0]']     \n","                                                                                                  \n"," conv1d_transpose_6 (Conv1DTran  (None, 800, 10)     410         ['leaky_re_lu_9[0][0]']          \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_10 (LeakyReLU)     (None, 800, 10)      0           ['conv1d_transpose_6[0][0]']     \n","                                                                                                  \n"," conv1d_transpose_7 (Conv1DTran  (None, 1600, 25)    1025        ['leaky_re_lu_10[0][0]']         \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_11 (LeakyReLU)     (None, 1600, 25)     0           ['conv1d_transpose_7[0][0]']     \n","                                                                                                  \n"," conv1d_5 (Conv1D)              (None, 800, 50)      5050        ['leaky_re_lu_11[0][0]']         \n","                                                                                                  \n"," conv1d_6 (Conv1D)              (None, 100, 50)      10050       ['conv1d_5[0][0]']               \n","                                                                                                  \n"," conv1d_7 (Conv1D)              (None, 25, 50)       10050       ['conv1d_6[0][0]']               \n","                                                                                                  \n","==================================================================================================\n","Total params: 133,695\n","Trainable params: 133,695\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n","None\n"]},{"data":{"image/png":"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\n","text/plain":["\u003cIPython.core.display.Image object\u003e"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":[" def define_generator(latent_dim, n_classes=6):\n","\tin_label = Input(shape=(1,))  \n","\t# embedding for categorical input\n","\tli = Embedding(n_classes, 50)(in_label) #Shape 1,50\n","    \n","\t# linear multiplication\n","\tn_nodes = 100  # To match the dimensions for concatenation later in this step.  \n","\tli = Dense(n_nodes)(li) \n","\tli = Reshape((100, 1))(li)\n"," \n","\n","\n","\t# image generator input\n","\tin_lat = Input(shape=(latent_dim,))  \n","\tn_nodes = 1000\n","\tgen = Dense(n_nodes)(in_lat)  \n","\tgen = LeakyReLU(alpha=0.2)(gen)\n","\tgen = Reshape((100, 10))(gen) \n"," \n","\n","\tmerge = Concatenate()([gen, li])  \n"," \n","\n","\tgen = Conv1DTranspose(10, 4, strides=2, padding='same')(merge) \n","\tgen = LeakyReLU(alpha=0.2)(gen)\n","\tgen = Conv1DTranspose(10, 4, strides=2, padding='same')(gen) \n","\tgen = LeakyReLU(alpha=0.2)(gen)\n","\tgen = Conv1DTranspose(10, 4, strides=2, padding='same')(gen) \n","\tgen = LeakyReLU(alpha=0.2)(gen)\n","\tgen = Conv1DTranspose(25, 4, strides=2, padding='same')(gen) \n","\tgen = LeakyReLU(alpha=0.2)(gen)\n"," \n","\tgen = Conv1D(50,4, strides = 2 , padding = 'same')(gen)\n","\tgen = Conv1D(50,4, strides = 8 , padding = 'same')(gen)\n","\tgen = Conv1D(50,4, strides = 4 , padding = 'same')(gen)\n","\t\n","\tout_layer = (gen) \n","\n","\tmodel = Model([in_lat, in_label], out_layer)\n","\treturn model   \n","\n","test_gen = define_generator(100, n_classes=3)\n","print(test_gen.summary())\n","plot_model(test_gen, to_file='gen.png', show_shapes=True)"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1677,"status":"ok","timestamp":1681900749309,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"1TQClf9YWao9","outputId":"2702a94e-b8b1-495c-888a-f16a46debefb"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"]},{"name":"stdout","output_type":"stream","text":["Model: \"model_3\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_6 (InputLayer)           [(None, 100)]        0           []                               \n","                                                                                                  \n"," input_5 (InputLayer)           [(None, 1)]          0           []                               \n","                                                                                                  \n"," dense_5 (Dense)                (None, 1000)         101000      ['input_6[0][0]']                \n","                                                                                                  \n"," embedding_2 (Embedding)        (None, 1, 50)        150         ['input_5[0][0]']                \n","                                                                                                  \n"," leaky_re_lu_7 (LeakyReLU)      (None, 1000)         0           ['dense_5[0][0]']                \n","                                                                                                  \n"," dense_4 (Dense)                (None, 1, 100)       5100        ['embedding_2[0][0]']            \n","                                                                                                  \n"," reshape_4 (Reshape)            (None, 100, 10)      0           ['leaky_re_lu_7[0][0]']          \n","                                                                                                  \n"," reshape_3 (Reshape)            (None, 100, 1)       0           ['dense_4[0][0]']                \n","                                                                                                  \n"," concatenate_2 (Concatenate)    (None, 100, 11)      0           ['reshape_4[0][0]',              \n","                                                                  'reshape_3[0][0]']              \n","                                                                                                  \n"," conv1d_transpose_4 (Conv1DTran  (None, 200, 10)     450         ['concatenate_2[0][0]']          \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_8 (LeakyReLU)      (None, 200, 10)      0           ['conv1d_transpose_4[0][0]']     \n","                                                                                                  \n"," conv1d_transpose_5 (Conv1DTran  (None, 400, 10)     410         ['leaky_re_lu_8[0][0]']          \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_9 (LeakyReLU)      (None, 400, 10)      0           ['conv1d_transpose_5[0][0]']     \n","                                                                                                  \n"," conv1d_transpose_6 (Conv1DTran  (None, 800, 10)     410         ['leaky_re_lu_9[0][0]']          \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_10 (LeakyReLU)     (None, 800, 10)      0           ['conv1d_transpose_6[0][0]']     \n","                                                                                                  \n"," conv1d_transpose_7 (Conv1DTran  (None, 1600, 25)    1025        ['leaky_re_lu_10[0][0]']         \n"," spose)                                                                                           \n","                                                                                                  \n"," leaky_re_lu_11 (LeakyReLU)     (None, 1600, 25)     0           ['conv1d_transpose_7[0][0]']     \n","                                                                                                  \n"," conv1d_5 (Conv1D)              (None, 800, 50)      5050        ['leaky_re_lu_11[0][0]']         \n","                                                                                                  \n"," conv1d_6 (Conv1D)              (None, 100, 50)      10050       ['conv1d_5[0][0]']               \n","                                                                                                  \n"," conv1d_7 (Conv1D)              (None, 25, 50)       10050       ['conv1d_6[0][0]']               \n","                                                                                                  \n"," model (Functional)             (None, 1)            152755      ['conv1d_7[0][0]',               \n","                                                                  'input_5[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 286,450\n","Trainable params: 133,695\n","Non-trainable params: 152,755\n","__________________________________________________________________________________________________\n"]},{"data":{"image/png":"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\n","text/plain":["\u003cIPython.core.display.Image object\u003e"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["def define_gan(g_model, d_model):\n","\td_model.trainable = False  \n","\t#Discriminator is trained separately. So set to not trainable.\n","\tgen_noise, gen_label = g_model.input  \n","\tgen_output = g_model.output  \n","\tgan_output = d_model([gen_output, gen_label])\n","\tmodel = Model([gen_noise, gen_label], gan_output)\n","\topt = Adam(lr=0.0002, beta_1=0.5)\n","\tmodel.compile(loss='binary_crossentropy', optimizer=opt)\n","\treturn model\n","\n","test_gan = define_gan(test_gen, test_discr)\n","test_gan.summary()\n","plot_model(test_gan, to_file='gAn.png', show_shapes=True)\n"]},{"cell_type":"code","execution_count":34,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1681900749309,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"BFI70n32CxSf"},"outputs":[],"source":["def load_real_samples():\n","\treturn [xxx, yy]\n"]},{"cell_type":"code","execution_count":35,"metadata":{"executionInfo":{"elapsed":1579,"status":"ok","timestamp":1681900750885,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"BtNr3e3aWhSS"},"outputs":[],"source":["def generate_real_samples(vectors, labels, n_samples):\n","\tvectors, labels = vectors, labels  \n","\tix = randint(0, vectors.shape[0], n_samples)\n","\tX, labels = vectors[ix], labels[ix]\n","\ty = ones((n_samples, 1)) \n","\treturn [X, labels], y\n","\n","# check_generate_real_samples = generate_real_samples(x, y, 1)\n","# check_generate_real_samples"]},{"cell_type":"code","execution_count":36,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1681900752282,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"1Z8un3iyWjpb"},"outputs":[],"source":["def generate_latent_points(latent_dim, n_samples, n_classes=3):\n","\tx_input = randn(latent_dim * n_samples)\n","\tz_input = x_input.reshape(n_samples, latent_dim)\n","\tlabels = randint(0, n_classes, n_samples)\n","\treturn [z_input, labels]\n","\n","# check_generate_latent_points = generate_latent_points(100, 1, 3)\n","# check_generate_latent_points[0],check_generate_latent_points[1] "]},{"cell_type":"code","execution_count":37,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1681900753093,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"R4OgkCPwWmZe"},"outputs":[],"source":["def generate_fake_samples(generator, latent_dim, n_samples):\n","\tz_input, labels_input = generate_latent_points(latent_dim, n_samples)\n","\timages = generator.predict([z_input, labels_input])\n","\ty = zeros((n_samples, 1))  \n","\treturn [images, labels_input], y\n","\n","# check_generate_fake_samples = generate_fake_samples(test_gen, 100, 1)\n","# check_generate_fake_samples"]},{"cell_type":"code","execution_count":38,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1681900755020,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"NVnbjrF5WooC"},"outputs":[],"source":["def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=100, n_batch=10):\n","\tbat_per_epo = int(dataset[0].shape[0] / n_batch)\n","\thalf_batch = int(n_batch / 2)  #the discriminator model is updated for a half batch of real samples and a half batch of fake samples, combined a single batch. \n","\t# manually enumerate epochs\n","\tfor i in range(n_epochs):\n","\t\t# enumerate batches over the training set\n","\t\tfor j in range(bat_per_epo):\n","      \t# Train the discriminator on real and fake images, separately (half batch each)\n","      \t# Research showed that separate training is more effective. \n","\t\t\t\t# get randomly selected 'real' samples\n","      \t# get randomly selected 'real' samples\n","\t\t\t[X_real, labels_real], y_real = generate_real_samples(xxx, yy, half_batch)\n","            # update discriminator model weights\n","            # train_on_batch allows you to update weights based on a collection of samples you provide\n","\t\t\td_loss_real, _ = d_model.train_on_batch([X_real, labels_real], y_real)\n","\t\t\t\t# generate 'fake' examples\n","\t\t\t[X_fake, labels], y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n","\t\t\t\t# update discriminator model weights\n","\t\t\td_loss_fake, _ = d_model.train_on_batch([X_fake, labels], y_fake)\n","        # d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) #Average loss if you want to report single..\n","\t\t\t\t# prepare points in latent space as input for the generator\n","\t\t\t[z_input, labels_input] = generate_latent_points(latent_dim, n_batch)\n","        # The generator wants the discriminator to label the generated samples as valid (ones)\n","        # This is where the generator is trying to trick discriminator into believing\n","        # the generated image is true (hence value of 1 for y)\t\n","\t\t\t\t# create inverted labels for the fake samples\n","\t\t\ty_gan = ones((n_batch, 1))\n","        # Generator is part of combined model where it got directly linked with the discriminator\n","        # Train the generator with latent_dim as x and 1 as y. \n","        # Again, 1 as the output as it is adversarial and if generator did a great\n","        # job of folling the discriminator then the output would be 1 (true)\n","\t\t\t\t# update the generator via the discriminator's error\n","\t\t\tg_loss = gan_model.train_on_batch([z_input, labels_input], y_gan)\n","\t\t\t\t# Print losses on this batch\n","\t\t\tprint('Epoch\u003e%d, Batch%d/%d, d1=%.3f, d2=%.3f g=%.3f' %(i+1, j+1, bat_per_epo, d_loss_real, d_loss_fake, g_loss))\n","\t\t# save the generator model\n","\tg_model.save('Generator.h5')\n","\td_model.save('Discriminator.h5')\n"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":825,"status":"ok","timestamp":1681900757459,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"V6dkg9aEGyKn","outputId":"431bf7f6-bc49-4892-b605-2df0725053f2"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n","WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"]}],"source":["# size of the latent space\n","latent_dim = 100\n","# create the discriminator\n","d_model = define_discriminator()\n","# create the generator\n","g_model = define_generator(latent_dim)\n","# create the gan\n","gan_model = define_gan(g_model, d_model)\n","dataset = load_real_samples()"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":154882,"status":"error","timestamp":1681900914822,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"GSyiBg-_W2hQ","outputId":"6d54f20c-3797-41bf-b364-129544a65235"},"outputs":[{"name":"stdout","output_type":"stream","text":["2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch1/256, d1=0.650, d2=0.713 g=0.684\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch2/256, d1=0.414, d2=0.720 g=0.682\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch3/256, d1=0.296, d2=0.728 g=0.680\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch4/256, d1=0.174, d2=0.740 g=0.683\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch5/256, d1=0.125, d2=0.739 g=0.710\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch6/256, d1=0.085, d2=0.715 g=0.766\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch7/256, d1=0.101, d2=0.668 g=0.864\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch8/256, d1=0.108, d2=0.608 g=0.972\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch9/256, d1=0.098, d2=0.564 g=1.046\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch10/256, d1=0.090, d2=0.520 g=1.143\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch11/256, d1=0.094, d2=0.458 g=1.291\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch12/256, d1=0.065, d2=0.380 g=1.450\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch13/256, d1=0.101, d2=0.312 g=1.615\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch14/256, d1=0.032, d2=0.255 g=1.851\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch15/256, d1=0.030, d2=0.200 g=2.148\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch16/256, d1=0.049, d2=0.144 g=2.420\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch17/256, d1=0.044, d2=0.117 g=2.657\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch18/256, d1=0.015, d2=0.087 g=2.976\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch19/256, d1=0.028, d2=0.077 g=3.311\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch20/256, d1=0.080, d2=0.077 g=3.425\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch21/256, d1=0.041, d2=0.067 g=3.840\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch22/256, d1=0.010, d2=0.035 g=4.296\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch23/256, d1=0.016, d2=0.035 g=4.829\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch24/256, d1=0.028, d2=0.031 g=5.159\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch25/256, d1=0.019, d2=0.031 g=5.874\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch26/256, d1=0.025, d2=0.011 g=5.765\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch27/256, d1=0.027, d2=0.095 g=7.733\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch28/256, d1=0.069, d2=0.003 g=6.734\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch29/256, d1=0.060, d2=0.090 g=6.670\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch30/256, d1=0.064, d2=0.021 g=6.024\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch31/256, d1=0.060, d2=0.135 g=7.551\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch32/256, d1=0.129, d2=0.004 g=6.806\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch33/256, d1=0.054, d2=0.029 g=4.807\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch34/256, d1=0.024, d2=1.138 g=9.051\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch35/256, d1=0.205, d2=0.000 g=9.471\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch36/256, d1=0.472, d2=0.022 g=4.222\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch37/256, d1=0.009, d2=4.084 g=6.773\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch38/256, d1=0.147, d2=0.014 g=7.771\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch39/256, d1=0.628, d2=1.926 g=9.819\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch40/256, d1=1.094, d2=0.168 g=8.774\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch41/256, d1=0.609, d2=1.102 g=10.804\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch42/256, d1=0.558, d2=0.049 g=8.838\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch43/256, d1=0.529, d2=2.891 g=10.813\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch44/256, d1=1.378, d2=0.289 g=7.784\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch45/256, d1=0.830, d2=0.610 g=4.055\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch46/256, d1=0.796, d2=1.044 g=3.621\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch47/256, d1=0.563, d2=0.737 g=3.464\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch48/256, d1=0.304, d2=0.283 g=3.490\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch49/256, d1=0.300, d2=0.256 g=3.535\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch50/256, d1=0.159, d2=0.224 g=3.397\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch51/256, d1=0.343, d2=0.176 g=3.024\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch52/256, d1=0.184, d2=0.397 g=2.991\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch53/256, d1=0.116, d2=0.339 g=3.542\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch54/256, d1=0.198, d2=0.242 g=3.373\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch55/256, d1=0.179, d2=0.273 g=3.497\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch56/256, d1=0.191, d2=0.497 g=3.963\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch57/256, d1=0.313, d2=0.200 g=3.948\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch58/256, d1=0.363, d2=0.645 g=3.413\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch59/256, d1=0.492, d2=0.604 g=3.517\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch60/256, d1=0.453, d2=0.720 g=3.425\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch61/256, d1=0.456, d2=0.681 g=3.601\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e1, Batch62/256, d1=0.980, d2=1.229 g=3.722\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch63/256, d1=1.333, d2=1.366 g=4.897\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch64/256, d1=1.588, d2=0.540 g=4.780\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch65/256, d1=1.225, d2=0.681 g=5.490\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch66/256, d1=1.241, d2=0.237 g=4.478\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch67/256, d1=0.832, d2=0.843 g=5.633\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch68/256, d1=0.956, d2=0.172 g=4.943\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch69/256, d1=0.890, d2=0.930 g=4.060\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch70/256, d1=0.958, d2=0.421 g=3.146\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch71/256, d1=1.108, d2=0.609 g=2.234\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch72/256, d1=1.039, d2=0.693 g=1.918\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch73/256, d1=0.800, d2=0.422 g=2.013\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch74/256, d1=0.689, d2=0.378 g=1.942\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch75/256, d1=0.614, d2=0.400 g=1.995\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch76/256, d1=0.568, d2=0.337 g=2.091\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch77/256, d1=0.632, d2=0.303 g=2.181\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch78/256, d1=0.524, d2=0.293 g=2.180\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch79/256, d1=0.437, d2=0.271 g=2.169\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch80/256, d1=0.383, d2=0.260 g=2.198\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch81/256, d1=0.307, d2=0.266 g=2.354\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch82/256, d1=0.308, d2=0.322 g=2.491\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch83/256, d1=0.298, d2=0.245 g=2.563\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch84/256, d1=0.377, d2=0.287 g=2.332\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch85/256, d1=0.370, d2=0.286 g=2.148\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch86/256, d1=0.430, d2=0.404 g=1.968\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch87/256, d1=0.431, d2=0.490 g=2.239\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch88/256, d1=0.419, d2=0.500 g=2.196\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch89/256, d1=0.654, d2=0.421 g=1.994\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch90/256, d1=0.571, d2=0.451 g=1.611\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch91/256, d1=0.539, d2=0.459 g=1.448\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch92/256, d1=0.473, d2=0.534 g=1.648\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch93/256, d1=0.415, d2=0.389 g=1.789\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch94/256, d1=0.597, d2=0.302 g=1.618\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch95/256, d1=0.391, d2=0.369 g=1.718\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch96/256, d1=0.464, d2=0.362 g=1.796\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch97/256, d1=0.320, d2=0.288 g=1.856\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch98/256, d1=0.395, d2=0.382 g=1.878\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch99/256, d1=0.317, d2=0.291 g=1.929\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch100/256, d1=0.352, d2=0.311 g=1.743\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch101/256, d1=0.374, d2=0.369 g=1.661\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch102/256, d1=0.380, d2=0.384 g=1.653\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch103/256, d1=0.363, d2=0.431 g=1.909\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch104/256, d1=0.347, d2=0.344 g=2.107\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch105/256, d1=0.357, d2=0.366 g=2.200\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch106/256, d1=0.576, d2=0.341 g=1.923\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch107/256, d1=0.385, d2=0.498 g=1.961\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch108/256, d1=0.431, d2=0.382 g=2.042\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch109/256, d1=0.687, d2=0.406 g=1.750\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch110/256, d1=0.494, d2=0.566 g=1.707\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch111/256, d1=0.465, d2=0.467 g=1.847\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch112/256, d1=0.577, d2=0.460 g=1.775\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch113/256, d1=0.602, d2=0.474 g=1.808\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch114/256, d1=0.479, d2=0.426 g=1.855\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch115/256, d1=0.474, d2=0.436 g=1.910\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch116/256, d1=0.650, d2=0.455 g=1.922\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch117/256, d1=0.438, d2=0.381 g=1.819\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch118/256, d1=0.475, d2=0.375 g=1.832\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch119/256, d1=0.393, d2=0.357 g=1.953\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch120/256, d1=0.486, d2=0.362 g=1.930\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch121/256, d1=0.456, d2=0.353 g=1.812\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch122/256, d1=0.381, d2=0.330 g=1.874\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch123/256, d1=0.504, d2=0.372 g=1.928\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch124/256, d1=0.472, d2=0.323 g=1.908\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch125/256, d1=0.443, d2=0.372 g=1.837\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch126/256, d1=0.428, d2=0.425 g=1.918\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch127/256, d1=0.378, d2=0.468 g=1.949\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch128/256, d1=0.363, d2=0.280 g=2.009\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch129/256, d1=0.377, d2=0.341 g=1.794\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch130/256, d1=0.428, d2=0.386 g=1.672\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch131/256, d1=0.372, d2=0.381 g=1.531\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch132/256, d1=0.316, d2=0.375 g=1.644\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch133/256, d1=0.378, d2=0.371 g=1.697\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch134/256, d1=0.331, d2=0.400 g=1.821\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch135/256, d1=0.414, d2=0.319 g=1.789\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch136/256, d1=0.314, d2=0.387 g=1.847\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch137/256, d1=0.288, d2=0.506 g=2.143\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch138/256, d1=0.392, d2=0.277 g=2.153\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch139/256, d1=0.354, d2=0.379 g=2.005\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch140/256, d1=0.464, d2=0.348 g=2.126\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch141/256, d1=0.467, d2=0.351 g=1.945\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch142/256, d1=0.431, d2=0.335 g=1.951\n","2/2 [==============================] - 0s 20ms/step\n","Epoch\u003e1, Batch143/256, d1=0.361, d2=0.557 g=2.284\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch144/256, d1=0.620, d2=0.272 g=2.187\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch145/256, d1=0.379, d2=0.356 g=2.103\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch146/256, d1=0.412, d2=0.478 g=2.487\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch147/256, d1=0.416, d2=0.220 g=2.279\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e1, Batch148/256, d1=0.483, d2=0.242 g=2.085\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch149/256, d1=0.367, d2=0.336 g=1.913\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e1, Batch150/256, d1=0.430, d2=0.446 g=1.987\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch151/256, d1=0.380, d2=0.337 g=2.109\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch152/256, d1=0.526, d2=0.364 g=1.829\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch153/256, d1=0.415, d2=0.473 g=1.862\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch154/256, d1=0.430, d2=0.381 g=1.811\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch155/256, d1=0.469, d2=0.494 g=1.858\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch156/256, d1=0.382, d2=0.394 g=2.048\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch157/256, d1=0.483, d2=0.371 g=1.916\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch158/256, d1=0.463, d2=0.498 g=1.625\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch159/256, d1=0.412, d2=0.581 g=1.953\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch160/256, d1=0.550, d2=0.519 g=1.889\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch161/256, d1=0.527, d2=0.512 g=2.050\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e1, Batch162/256, d1=0.767, d2=0.542 g=1.768\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch163/256, d1=0.512, d2=0.418 g=1.769\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch164/256, d1=0.534, d2=0.463 g=1.892\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch165/256, d1=0.457, d2=0.414 g=1.927\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch166/256, d1=0.548, d2=0.405 g=1.921\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch167/256, d1=0.444, d2=0.412 g=1.675\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch168/256, d1=0.407, d2=0.515 g=1.799\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch169/256, d1=0.431, d2=0.300 g=1.625\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch170/256, d1=0.440, d2=0.454 g=1.568\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch171/256, d1=0.504, d2=0.480 g=1.748\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch172/256, d1=0.404, d2=0.378 g=1.744\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch173/256, d1=0.431, d2=0.497 g=1.811\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch174/256, d1=0.472, d2=0.432 g=1.749\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch175/256, d1=0.460, d2=0.338 g=1.755\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch176/256, d1=0.315, d2=0.449 g=1.819\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e1, Batch177/256, d1=0.403, d2=0.308 g=1.863\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch178/256, d1=0.409, d2=0.418 g=1.868\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e1, Batch179/256, d1=0.430, d2=0.606 g=1.794\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e1, Batch180/256, d1=0.370, d2=0.321 g=1.939\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch181/256, d1=0.504, d2=0.400 g=1.899\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch182/256, d1=0.345, d2=0.453 g=1.875\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch183/256, d1=0.460, d2=0.344 g=1.861\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch184/256, d1=0.405, d2=0.471 g=1.856\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch185/256, d1=0.408, d2=0.381 g=1.951\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch186/256, d1=0.498, d2=0.376 g=1.907\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch187/256, d1=0.480, d2=0.430 g=1.952\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch188/256, d1=0.411, d2=0.414 g=2.070\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch189/256, d1=0.509, d2=0.521 g=2.177\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch190/256, d1=0.624, d2=0.435 g=2.206\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch191/256, d1=0.772, d2=0.432 g=1.853\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch192/256, d1=0.462, d2=0.403 g=2.097\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch193/256, d1=0.557, d2=0.420 g=1.956\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch194/256, d1=0.591, d2=0.434 g=1.954\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch195/256, d1=0.551, d2=0.481 g=1.945\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch196/256, d1=0.537, d2=0.402 g=1.773\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch197/256, d1=0.520, d2=0.440 g=1.751\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch198/256, d1=0.515, d2=0.433 g=1.729\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch199/256, d1=0.505, d2=0.406 g=1.666\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch200/256, d1=0.380, d2=0.389 g=1.869\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch201/256, d1=0.452, d2=0.370 g=1.822\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch202/256, d1=0.493, d2=0.414 g=1.713\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch203/256, d1=0.419, d2=0.402 g=1.857\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch204/256, d1=0.444, d2=0.394 g=2.153\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e1, Batch205/256, d1=0.446, d2=0.285 g=1.837\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch206/256, d1=0.422, d2=0.424 g=1.768\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch207/256, d1=0.292, d2=0.375 g=2.005\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch208/256, d1=0.513, d2=0.339 g=1.763\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch209/256, d1=0.367, d2=0.398 g=1.741\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch210/256, d1=0.411, d2=0.415 g=1.768\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch211/256, d1=0.542, d2=0.444 g=1.748\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch212/256, d1=0.467, d2=0.502 g=1.829\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch213/256, d1=0.551, d2=0.362 g=1.945\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch214/256, d1=0.518, d2=0.425 g=1.749\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch215/256, d1=0.400, d2=0.467 g=1.760\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch216/256, d1=0.499, d2=0.454 g=1.779\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch217/256, d1=0.446, d2=0.415 g=1.863\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch218/256, d1=0.444, d2=0.473 g=2.040\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch219/256, d1=0.516, d2=0.287 g=1.932\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch220/256, d1=0.453, d2=0.488 g=2.010\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch221/256, d1=0.386, d2=0.360 g=2.207\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch222/256, d1=0.523, d2=0.419 g=2.066\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch223/256, d1=0.436, d2=0.280 g=1.935\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch224/256, d1=0.457, d2=0.456 g=1.867\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch225/256, d1=0.431, d2=0.379 g=1.904\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch226/256, d1=0.506, d2=0.411 g=1.875\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch227/256, d1=0.419, d2=0.414 g=2.086\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch228/256, d1=0.360, d2=0.351 g=2.346\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch229/256, d1=0.517, d2=0.384 g=2.142\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch230/256, d1=0.498, d2=0.303 g=1.803\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch231/256, d1=0.506, d2=0.538 g=1.880\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch232/256, d1=0.456, d2=0.446 g=1.925\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch233/256, d1=0.447, d2=0.376 g=1.894\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch234/256, d1=0.367, d2=0.365 g=1.910\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e1, Batch235/256, d1=0.384, d2=0.386 g=1.936\n","2/2 [==============================] - 0s 15ms/step\n","Epoch\u003e1, Batch236/256, d1=0.466, d2=0.390 g=1.974\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch237/256, d1=0.405, d2=0.431 g=2.049\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e1, Batch238/256, d1=0.477, d2=0.396 g=2.053\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch239/256, d1=0.504, d2=0.430 g=2.129\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch240/256, d1=0.364, d2=0.315 g=2.048\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e1, Batch241/256, d1=0.547, d2=0.336 g=1.922\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e1, Batch242/256, d1=0.395, d2=0.447 g=2.040\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch243/256, d1=0.477, d2=0.364 g=2.188\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch244/256, d1=0.456, d2=0.320 g=1.831\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch245/256, d1=0.472, d2=0.449 g=1.909\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e1, Batch246/256, d1=0.439, d2=0.456 g=1.888\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch247/256, d1=0.411, d2=0.399 g=2.009\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e1, Batch248/256, d1=0.411, d2=0.429 g=1.855\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e1, Batch249/256, d1=0.454, d2=0.451 g=1.758\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch250/256, d1=0.460, d2=0.500 g=1.862\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e1, Batch251/256, d1=0.429, d2=0.480 g=2.124\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch252/256, d1=0.443, d2=0.400 g=2.000\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch253/256, d1=0.613, d2=0.551 g=1.906\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch254/256, d1=0.389, d2=0.423 g=2.304\n","2/2 [==============================] - 0s 15ms/step\n","Epoch\u003e1, Batch255/256, d1=0.535, d2=0.397 g=2.284\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e1, Batch256/256, d1=0.521, d2=0.457 g=2.414\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch1/256, d1=0.625, d2=0.343 g=2.276\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch2/256, d1=0.565, d2=0.446 g=2.619\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch3/256, d1=0.482, d2=0.323 g=2.348\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch4/256, d1=0.445, d2=0.330 g=2.262\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch5/256, d1=0.454, d2=0.384 g=1.937\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch6/256, d1=0.523, d2=0.438 g=2.019\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch7/256, d1=0.479, d2=0.338 g=1.898\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch8/256, d1=0.403, d2=0.408 g=1.925\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch9/256, d1=0.409, d2=0.381 g=1.859\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch10/256, d1=0.430, d2=0.344 g=1.824\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch11/256, d1=0.360, d2=0.338 g=1.828\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch12/256, d1=0.374, d2=0.344 g=2.029\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch13/256, d1=0.417, d2=0.339 g=2.051\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e2, Batch14/256, d1=0.366, d2=0.352 g=1.842\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e2, Batch15/256, d1=0.384, d2=0.377 g=1.786\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch16/256, d1=0.282, d2=0.304 g=1.979\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch17/256, d1=0.426, d2=0.269 g=1.741\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch18/256, d1=0.307, d2=0.393 g=1.941\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch19/256, d1=0.335, d2=0.382 g=1.860\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch20/256, d1=0.335, d2=0.398 g=2.140\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch21/256, d1=0.513, d2=0.403 g=1.820\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch22/256, d1=0.426, d2=0.386 g=1.682\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch23/256, d1=0.343, d2=0.389 g=1.774\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch24/256, d1=0.407, d2=0.391 g=1.977\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch25/256, d1=0.403, d2=0.447 g=1.943\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch26/256, d1=0.440, d2=0.404 g=1.925\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch27/256, d1=0.580, d2=0.425 g=1.711\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch28/256, d1=0.466, d2=0.430 g=1.872\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch29/256, d1=0.471, d2=0.390 g=2.010\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch30/256, d1=0.547, d2=0.329 g=1.748\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch31/256, d1=0.325, d2=0.381 g=1.970\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch32/256, d1=0.434, d2=0.347 g=1.894\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch33/256, d1=0.310, d2=0.367 g=1.981\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch34/256, d1=0.330, d2=0.255 g=2.169\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e2, Batch35/256, d1=0.441, d2=0.296 g=2.074\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch36/256, d1=0.252, d2=0.246 g=2.130\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch37/256, d1=0.329, d2=0.310 g=1.853\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e2, Batch38/256, d1=0.257, d2=0.300 g=1.944\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch39/256, d1=0.318, d2=0.364 g=2.105\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch40/256, d1=0.379, d2=0.334 g=2.013\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch41/256, d1=0.284, d2=0.287 g=2.256\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch42/256, d1=0.392, d2=0.367 g=2.062\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch43/256, d1=0.332, d2=0.353 g=2.066\n","2/2 [==============================] - 0s 18ms/step\n","Epoch\u003e2, Batch44/256, d1=0.345, d2=0.346 g=1.885\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch45/256, d1=0.366, d2=0.412 g=2.087\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch46/256, d1=0.548, d2=0.494 g=2.271\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch47/256, d1=0.541, d2=0.422 g=2.642\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch48/256, d1=0.682, d2=0.403 g=2.334\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch49/256, d1=0.472, d2=0.344 g=2.179\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch50/256, d1=0.370, d2=0.409 g=2.749\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch51/256, d1=0.382, d2=0.317 g=2.540\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch52/256, d1=0.358, d2=0.319 g=2.489\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch53/256, d1=0.396, d2=0.297 g=2.477\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch54/256, d1=0.451, d2=0.354 g=2.268\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch55/256, d1=0.450, d2=0.305 g=2.014\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch56/256, d1=0.329, d2=0.361 g=2.262\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch57/256, d1=0.369, d2=0.347 g=2.290\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch58/256, d1=0.367, d2=0.374 g=2.220\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch59/256, d1=0.490, d2=0.377 g=1.900\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch60/256, d1=0.385, d2=0.439 g=1.818\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch61/256, d1=0.285, d2=0.438 g=2.214\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch62/256, d1=0.448, d2=0.396 g=2.179\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch63/256, d1=0.484, d2=0.416 g=2.101\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch64/256, d1=0.562, d2=0.741 g=2.300\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch65/256, d1=0.667, d2=0.400 g=2.126\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch66/256, d1=0.571, d2=0.344 g=1.895\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch67/256, d1=0.397, d2=0.380 g=2.011\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch68/256, d1=0.471, d2=0.417 g=2.394\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch69/256, d1=0.657, d2=0.407 g=2.209\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch70/256, d1=0.385, d2=0.409 g=2.742\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch71/256, d1=0.598, d2=0.321 g=2.412\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e2, Batch72/256, d1=0.494, d2=0.284 g=2.149\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch73/256, d1=0.516, d2=0.442 g=1.785\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch74/256, d1=0.459, d2=0.444 g=1.968\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch75/256, d1=0.587, d2=0.325 g=1.849\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch76/256, d1=0.448, d2=0.497 g=1.940\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e2, Batch77/256, d1=0.355, d2=0.380 g=2.262\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch78/256, d1=0.531, d2=0.356 g=2.156\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch79/256, d1=0.508, d2=0.337 g=1.965\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch80/256, d1=0.355, d2=0.385 g=2.040\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch81/256, d1=0.467, d2=0.415 g=2.024\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch82/256, d1=0.445, d2=0.271 g=1.972\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch83/256, d1=0.408, d2=0.384 g=1.907\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch84/256, d1=0.256, d2=0.332 g=2.170\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch85/256, d1=0.411, d2=0.279 g=2.201\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch86/256, d1=0.299, d2=0.284 g=2.088\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e2, Batch87/256, d1=0.374, d2=0.354 g=1.887\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch88/256, d1=0.425, d2=0.393 g=1.973\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch89/256, d1=0.384, d2=0.308 g=2.073\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch90/256, d1=0.385, d2=0.310 g=1.909\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch91/256, d1=0.321, d2=0.348 g=2.001\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch92/256, d1=0.252, d2=0.301 g=2.227\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e2, Batch93/256, d1=0.308, d2=0.259 g=2.216\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch94/256, d1=0.314, d2=0.489 g=2.245\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch95/256, d1=0.407, d2=0.364 g=2.649\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch96/256, d1=0.459, d2=0.319 g=2.226\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch97/256, d1=0.503, d2=0.485 g=2.053\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch98/256, d1=0.430, d2=0.457 g=2.348\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch99/256, d1=0.390, d2=0.425 g=2.482\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch100/256, d1=0.492, d2=0.356 g=2.510\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch101/256, d1=0.430, d2=0.461 g=2.841\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch102/256, d1=0.326, d2=0.222 g=3.088\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch103/256, d1=0.473, d2=0.451 g=2.835\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch104/256, d1=0.365, d2=0.291 g=2.715\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch105/256, d1=0.276, d2=0.251 g=2.468\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch106/256, d1=0.476, d2=0.529 g=2.541\n","2/2 [==============================] - 0s 19ms/step\n","Epoch\u003e2, Batch107/256, d1=0.354, d2=0.264 g=2.644\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e2, Batch108/256, d1=0.460, d2=0.331 g=2.300\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch109/256, d1=0.303, d2=0.456 g=2.561\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch110/256, d1=0.435, d2=0.272 g=2.507\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch111/256, d1=0.424, d2=0.407 g=2.274\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch112/256, d1=0.316, d2=0.496 g=2.562\n","2/2 [==============================] - 0s 18ms/step\n","Epoch\u003e2, Batch113/256, d1=0.654, d2=0.380 g=2.241\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch114/256, d1=0.573, d2=0.490 g=2.061\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch115/256, d1=0.539, d2=0.401 g=2.041\n","2/2 [==============================] - 0s 16ms/step\n","Epoch\u003e2, Batch116/256, d1=0.578, d2=0.461 g=1.864\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch117/256, d1=0.495, d2=0.433 g=2.133\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch118/256, d1=0.430, d2=0.419 g=2.029\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch119/256, d1=0.373, d2=0.349 g=2.323\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch120/256, d1=0.410, d2=0.343 g=1.993\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch121/256, d1=0.301, d2=0.398 g=1.991\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch122/256, d1=0.369, d2=0.348 g=2.120\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e2, Batch123/256, d1=0.449, d2=0.349 g=1.936\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch124/256, d1=0.392, d2=0.531 g=2.110\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch125/256, d1=0.484, d2=0.412 g=2.330\n","2/2 [==============================] - 0s 16ms/step\n","Epoch\u003e2, Batch126/256, d1=0.445, d2=0.367 g=2.226\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch127/256, d1=0.483, d2=0.326 g=1.993\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch128/256, d1=0.424, d2=0.410 g=1.908\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch129/256, d1=0.370, d2=0.385 g=1.998\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e2, Batch130/256, d1=0.433, d2=0.399 g=2.171\n","2/2 [==============================] - 0s 20ms/step\n","Epoch\u003e2, Batch131/256, d1=0.449, d2=0.364 g=2.249\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch132/256, d1=0.498, d2=0.430 g=1.953\n","2/2 [==============================] - 0s 16ms/step\n","Epoch\u003e2, Batch133/256, d1=0.276, d2=0.393 g=2.280\n","2/2 [==============================] - 0s 17ms/step\n","Epoch\u003e2, Batch134/256, d1=0.338, d2=0.334 g=2.435\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch135/256, d1=0.368, d2=0.331 g=2.179\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch136/256, d1=0.292, d2=0.350 g=2.307\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch137/256, d1=0.321, d2=0.389 g=2.298\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch138/256, d1=0.327, d2=0.444 g=2.385\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch139/256, d1=0.471, d2=0.411 g=2.641\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e2, Batch140/256, d1=0.481, d2=0.377 g=2.258\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e2, Batch141/256, d1=0.347, d2=0.699 g=2.745\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch142/256, d1=0.453, d2=0.384 g=2.735\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch143/256, d1=0.636, d2=0.372 g=2.331\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch144/256, d1=0.531, d2=0.890 g=3.243\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e2, Batch145/256, d1=0.554, d2=0.327 g=3.974\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch146/256, d1=0.612, d2=0.276 g=3.193\n","2/2 [==============================] - 0s 20ms/step\n","Epoch\u003e2, Batch147/256, d1=0.519, d2=0.360 g=2.460\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch148/256, d1=0.254, d2=0.836 g=2.550\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch149/256, d1=0.503, d2=0.249 g=2.769\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch150/256, d1=0.699, d2=0.358 g=2.320\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch151/256, d1=0.414, d2=0.437 g=2.830\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch152/256, d1=0.467, d2=0.232 g=2.293\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch153/256, d1=0.662, d2=0.559 g=1.846\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch154/256, d1=0.669, d2=0.624 g=1.738\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch155/256, d1=0.483, d2=0.524 g=1.893\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch156/256, d1=0.438, d2=0.386 g=1.810\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch157/256, d1=0.385, d2=0.359 g=1.866\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch158/256, d1=0.380, d2=0.463 g=2.084\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch159/256, d1=0.482, d2=0.449 g=2.130\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch160/256, d1=0.432, d2=0.302 g=2.099\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch161/256, d1=0.498, d2=0.391 g=1.985\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch162/256, d1=0.423, d2=0.429 g=1.946\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch163/256, d1=0.533, d2=0.364 g=1.865\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch164/256, d1=0.343, d2=0.335 g=1.978\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch165/256, d1=0.346, d2=0.324 g=1.876\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch166/256, d1=0.419, d2=0.392 g=1.952\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch167/256, d1=0.375, d2=0.332 g=1.964\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch168/256, d1=0.382, d2=0.322 g=1.898\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch169/256, d1=0.457, d2=0.446 g=1.741\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch170/256, d1=0.423, d2=0.417 g=1.748\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch171/256, d1=0.307, d2=0.332 g=2.096\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch172/256, d1=0.410, d2=0.338 g=1.878\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch173/256, d1=0.321, d2=0.298 g=1.935\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch174/256, d1=0.510, d2=0.487 g=2.022\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch175/256, d1=0.460, d2=0.348 g=1.831\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch176/256, d1=0.495, d2=0.429 g=1.747\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch177/256, d1=0.310, d2=0.378 g=1.866\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch178/256, d1=0.404, d2=0.379 g=1.951\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch179/256, d1=0.453, d2=0.348 g=1.884\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch180/256, d1=0.331, d2=0.318 g=1.832\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch181/256, d1=0.431, d2=0.391 g=1.770\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch182/256, d1=0.289, d2=0.351 g=1.997\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch183/256, d1=0.332, d2=0.446 g=2.102\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch184/256, d1=0.347, d2=0.277 g=2.157\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch185/256, d1=0.483, d2=0.349 g=1.999\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch186/256, d1=0.336, d2=0.432 g=1.775\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch187/256, d1=0.292, d2=0.417 g=2.244\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch188/256, d1=0.412, d2=0.543 g=2.155\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch189/256, d1=0.539, d2=0.341 g=2.040\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch190/256, d1=0.529, d2=0.421 g=1.817\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e2, Batch191/256, d1=0.446, d2=0.550 g=1.957\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch192/256, d1=0.453, d2=0.383 g=2.227\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch193/256, d1=0.560, d2=0.539 g=2.365\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch194/256, d1=0.479, d2=0.292 g=2.151\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch195/256, d1=0.468, d2=0.457 g=2.255\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch196/256, d1=0.431, d2=0.347 g=2.459\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch197/256, d1=0.479, d2=0.303 g=2.028\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch198/256, d1=0.412, d2=0.485 g=2.172\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch199/256, d1=0.448, d2=0.384 g=2.290\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch200/256, d1=0.449, d2=0.321 g=2.225\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch201/256, d1=0.530, d2=0.432 g=2.104\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch202/256, d1=0.275, d2=0.345 g=2.244\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch203/256, d1=0.389, d2=0.315 g=2.038\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch204/256, d1=0.367, d2=0.320 g=1.946\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch205/256, d1=0.341, d2=0.468 g=2.153\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch206/256, d1=0.377, d2=0.366 g=2.296\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch207/256, d1=0.339, d2=0.337 g=2.473\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch208/256, d1=0.371, d2=0.306 g=2.361\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch209/256, d1=0.544, d2=0.413 g=2.005\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch210/256, d1=0.382, d2=0.640 g=2.154\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch211/256, d1=0.451, d2=0.404 g=2.630\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch212/256, d1=0.686, d2=0.353 g=1.950\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch213/256, d1=0.443, d2=0.583 g=2.037\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch214/256, d1=0.475, d2=0.501 g=2.341\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch215/256, d1=0.648, d2=0.361 g=2.104\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch216/256, d1=0.556, d2=0.523 g=1.754\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch217/256, d1=0.481, d2=0.553 g=2.026\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch218/256, d1=0.641, d2=0.446 g=2.204\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch219/256, d1=0.519, d2=0.503 g=2.056\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch220/256, d1=0.533, d2=0.490 g=2.043\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch221/256, d1=0.508, d2=0.414 g=2.094\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch222/256, d1=0.561, d2=0.467 g=2.049\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch223/256, d1=0.591, d2=0.476 g=1.882\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch224/256, d1=0.416, d2=0.452 g=1.822\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch225/256, d1=0.341, d2=0.356 g=2.189\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch226/256, d1=0.502, d2=0.311 g=1.886\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch227/256, d1=0.461, d2=0.453 g=1.974\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch228/256, d1=0.521, d2=0.419 g=1.785\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch229/256, d1=0.307, d2=0.352 g=1.935\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch230/256, d1=0.403, d2=0.374 g=1.906\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e2, Batch231/256, d1=0.411, d2=0.436 g=2.040\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch232/256, d1=0.380, d2=0.310 g=2.052\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch233/256, d1=0.371, d2=0.302 g=2.175\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch234/256, d1=0.325, d2=0.280 g=2.027\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch235/256, d1=0.350, d2=0.375 g=2.094\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch236/256, d1=0.312, d2=0.350 g=2.312\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch237/256, d1=0.505, d2=0.384 g=2.127\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e2, Batch238/256, d1=0.331, d2=0.299 g=2.158\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch239/256, d1=0.292, d2=0.319 g=2.134\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch240/256, d1=0.358, d2=0.344 g=2.093\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch241/256, d1=0.308, d2=0.451 g=2.152\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch242/256, d1=0.398, d2=0.357 g=2.319\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e2, Batch243/256, d1=0.355, d2=0.383 g=2.355\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch244/256, d1=0.605, d2=0.529 g=2.286\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch245/256, d1=0.666, d2=0.445 g=2.029\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch246/256, d1=0.369, d2=0.590 g=2.388\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch247/256, d1=0.383, d2=0.337 g=2.635\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e2, Batch248/256, d1=0.645, d2=0.420 g=2.457\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch249/256, d1=0.654, d2=0.498 g=2.162\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e2, Batch250/256, d1=0.546, d2=0.525 g=2.295\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e2, Batch251/256, d1=0.470, d2=0.298 g=2.493\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch252/256, d1=0.502, d2=0.325 g=2.192\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e2, Batch253/256, d1=0.427, d2=0.387 g=2.252\n","2/2 [==============================] - 0s 17ms/step\n","Epoch\u003e2, Batch254/256, d1=0.606, d2=0.486 g=1.856\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e2, Batch255/256, d1=0.392, d2=0.407 g=2.150\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e2, Batch256/256, d1=0.599, d2=0.430 g=1.942\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch1/256, d1=0.376, d2=0.404 g=1.936\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch2/256, d1=0.453, d2=0.413 g=1.996\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch3/256, d1=0.438, d2=0.402 g=2.239\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch4/256, d1=0.436, d2=0.348 g=2.274\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch5/256, d1=0.500, d2=0.421 g=2.205\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch6/256, d1=0.456, d2=0.417 g=1.844\n","2/2 [==============================] - 0s 15ms/step\n","Epoch\u003e3, Batch7/256, d1=0.473, d2=0.527 g=1.939\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch8/256, d1=0.428, d2=0.351 g=2.024\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch9/256, d1=0.555, d2=0.554 g=2.112\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch10/256, d1=0.458, d2=0.433 g=2.343\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch11/256, d1=0.497, d2=0.293 g=2.024\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch12/256, d1=0.429, d2=0.472 g=1.927\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch13/256, d1=0.403, d2=0.444 g=1.877\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch14/256, d1=0.435, d2=0.327 g=1.927\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch15/256, d1=0.448, d2=0.444 g=1.873\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch16/256, d1=0.344, d2=0.461 g=2.074\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch17/256, d1=0.396, d2=0.336 g=2.342\n","2/2 [==============================] - 0s 19ms/step\n","Epoch\u003e3, Batch18/256, d1=0.556, d2=0.438 g=2.085\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch19/256, d1=0.482, d2=0.379 g=1.842\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch20/256, d1=0.417, d2=0.383 g=2.142\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch21/256, d1=0.521, d2=0.426 g=1.895\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch22/256, d1=0.337, d2=0.537 g=2.277\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch23/256, d1=0.554, d2=0.357 g=2.070\n","2/2 [==============================] - 0s 22ms/step\n","Epoch\u003e3, Batch24/256, d1=0.475, d2=0.440 g=1.920\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch25/256, d1=0.516, d2=0.415 g=1.915\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e3, Batch26/256, d1=0.420, d2=0.428 g=2.141\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch27/256, d1=0.508, d2=0.433 g=2.126\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch28/256, d1=0.465, d2=0.326 g=1.877\n","2/2 [==============================] - 0s 16ms/step\n","Epoch\u003e3, Batch29/256, d1=0.300, d2=0.357 g=1.966\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch30/256, d1=0.437, d2=0.346 g=1.862\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch31/256, d1=0.329, d2=0.391 g=2.067\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch32/256, d1=0.390, d2=0.366 g=2.249\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch33/256, d1=0.449, d2=0.447 g=2.379\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch34/256, d1=0.549, d2=0.435 g=2.100\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch35/256, d1=0.430, d2=0.355 g=2.140\n","2/2 [==============================] - 0s 15ms/step\n","Epoch\u003e3, Batch36/256, d1=0.455, d2=0.335 g=1.813\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch37/256, d1=0.362, d2=0.413 g=2.078\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch38/256, d1=0.427, d2=0.464 g=2.157\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch39/256, d1=0.360, d2=0.371 g=2.520\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch40/256, d1=0.450, d2=0.294 g=2.378\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch41/256, d1=0.488, d2=0.282 g=2.090\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch42/256, d1=0.362, d2=0.374 g=2.107\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch43/256, d1=0.298, d2=0.423 g=2.127\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch44/256, d1=0.449, d2=0.301 g=2.320\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch45/256, d1=0.390, d2=0.385 g=2.119\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch46/256, d1=0.458, d2=0.327 g=2.313\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch47/256, d1=0.364, d2=0.381 g=2.562\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch48/256, d1=0.496, d2=0.372 g=2.227\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch49/256, d1=0.493, d2=0.332 g=2.350\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch50/256, d1=0.338, d2=0.403 g=2.244\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch51/256, d1=0.344, d2=0.294 g=2.362\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch52/256, d1=0.491, d2=0.377 g=2.178\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch53/256, d1=0.402, d2=0.331 g=2.144\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch54/256, d1=0.391, d2=0.328 g=2.020\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch55/256, d1=0.322, d2=0.390 g=2.318\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch56/256, d1=0.360, d2=0.376 g=2.174\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch57/256, d1=0.353, d2=0.302 g=1.947\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch58/256, d1=0.326, d2=0.327 g=2.156\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch59/256, d1=0.291, d2=0.326 g=2.271\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch60/256, d1=0.360, d2=0.318 g=2.116\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch61/256, d1=0.306, d2=0.377 g=2.407\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch62/256, d1=0.417, d2=0.258 g=2.061\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch63/256, d1=0.495, d2=0.357 g=1.959\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch64/256, d1=0.293, d2=0.396 g=2.067\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch65/256, d1=0.284, d2=0.357 g=2.267\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch66/256, d1=0.417, d2=0.336 g=2.163\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch67/256, d1=0.413, d2=0.426 g=1.937\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch68/256, d1=0.323, d2=0.367 g=2.343\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch69/256, d1=0.343, d2=0.372 g=2.462\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch70/256, d1=0.506, d2=0.264 g=2.160\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch71/256, d1=0.465, d2=0.399 g=1.872\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch72/256, d1=0.357, d2=0.351 g=2.263\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch73/256, d1=0.420, d2=0.409 g=2.171\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch74/256, d1=0.430, d2=0.406 g=2.230\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch75/256, d1=0.458, d2=0.378 g=2.184\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch76/256, d1=0.304, d2=0.536 g=2.461\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch77/256, d1=0.428, d2=0.218 g=2.295\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch78/256, d1=0.518, d2=0.415 g=2.072\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch79/256, d1=0.467, d2=0.420 g=1.970\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch80/256, d1=0.271, d2=0.371 g=2.311\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch81/256, d1=0.418, d2=0.285 g=2.358\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch82/256, d1=0.384, d2=0.384 g=2.287\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch83/256, d1=0.346, d2=0.378 g=2.304\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch84/256, d1=0.391, d2=0.404 g=2.104\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch85/256, d1=0.412, d2=0.491 g=2.419\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch86/256, d1=0.408, d2=0.343 g=2.512\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch87/256, d1=0.485, d2=0.447 g=2.404\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch88/256, d1=0.421, d2=0.431 g=2.310\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch89/256, d1=0.327, d2=0.415 g=2.518\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch90/256, d1=0.480, d2=0.470 g=2.934\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch91/256, d1=0.646, d2=0.377 g=2.159\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch92/256, d1=0.511, d2=0.402 g=1.982\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch93/256, d1=0.364, d2=0.386 g=2.051\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch94/256, d1=0.320, d2=0.511 g=2.348\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch95/256, d1=0.474, d2=0.376 g=2.429\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch96/256, d1=0.573, d2=0.301 g=2.179\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch97/256, d1=0.427, d2=0.401 g=1.751\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch98/256, d1=0.355, d2=0.426 g=2.119\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch99/256, d1=0.455, d2=0.511 g=2.168\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch100/256, d1=0.458, d2=0.362 g=2.265\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch101/256, d1=0.580, d2=0.546 g=2.210\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch102/256, d1=0.415, d2=0.240 g=2.038\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch103/256, d1=0.401, d2=0.341 g=2.013\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch104/256, d1=0.489, d2=0.431 g=2.166\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch105/256, d1=0.300, d2=0.332 g=2.369\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch106/256, d1=0.393, d2=0.312 g=2.190\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch107/256, d1=0.365, d2=0.400 g=2.404\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch108/256, d1=0.392, d2=0.315 g=2.253\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch109/256, d1=0.312, d2=0.425 g=2.319\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch110/256, d1=0.552, d2=0.367 g=2.018\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch111/256, d1=0.420, d2=0.536 g=2.228\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch112/256, d1=0.443, d2=0.360 g=2.225\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch113/256, d1=0.468, d2=0.449 g=2.118\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch114/256, d1=0.312, d2=0.356 g=2.249\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch115/256, d1=0.473, d2=0.446 g=2.407\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch116/256, d1=0.533, d2=0.435 g=2.255\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch117/256, d1=0.400, d2=0.385 g=2.336\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch118/256, d1=0.543, d2=0.389 g=2.115\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch119/256, d1=0.444, d2=0.509 g=2.071\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch120/256, d1=0.350, d2=0.453 g=2.194\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch121/256, d1=0.397, d2=0.338 g=2.364\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch122/256, d1=0.504, d2=0.381 g=1.959\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch123/256, d1=0.469, d2=0.369 g=1.908\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch124/256, d1=0.411, d2=0.513 g=2.144\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch125/256, d1=0.399, d2=0.363 g=2.133\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch126/256, d1=0.430, d2=0.378 g=2.230\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch127/256, d1=0.527, d2=0.445 g=1.899\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch128/256, d1=0.446, d2=0.394 g=1.938\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch129/256, d1=0.384, d2=0.481 g=2.059\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch130/256, d1=0.545, d2=0.476 g=2.160\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch131/256, d1=0.426, d2=0.312 g=2.105\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch132/256, d1=0.411, d2=0.407 g=2.069\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch133/256, d1=0.574, d2=0.451 g=1.967\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch134/256, d1=0.379, d2=0.390 g=2.317\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch135/256, d1=0.539, d2=0.393 g=2.074\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch136/256, d1=0.379, d2=0.447 g=2.223\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch137/256, d1=0.426, d2=0.326 g=2.246\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch138/256, d1=0.440, d2=0.376 g=2.133\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch139/256, d1=0.405, d2=0.382 g=2.083\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch140/256, d1=0.453, d2=0.258 g=2.050\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch141/256, d1=0.455, d2=0.379 g=1.844\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch142/256, d1=0.374, d2=0.566 g=2.201\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch143/256, d1=0.443, d2=0.416 g=2.390\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch144/256, d1=0.580, d2=0.441 g=2.034\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch145/256, d1=0.388, d2=0.472 g=2.229\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch146/256, d1=0.462, d2=0.490 g=2.319\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch147/256, d1=0.581, d2=0.382 g=1.972\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch148/256, d1=0.399, d2=0.386 g=1.879\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch149/256, d1=0.451, d2=0.448 g=2.150\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch150/256, d1=0.455, d2=0.426 g=2.012\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch151/256, d1=0.506, d2=0.482 g=2.276\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch152/256, d1=0.466, d2=0.419 g=2.123\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch153/256, d1=0.428, d2=0.376 g=1.989\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch154/256, d1=0.407, d2=0.403 g=2.114\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch155/256, d1=0.384, d2=0.347 g=2.145\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch156/256, d1=0.385, d2=0.362 g=2.139\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch157/256, d1=0.330, d2=0.289 g=2.312\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch158/256, d1=0.468, d2=0.371 g=2.079\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch159/256, d1=0.412, d2=0.492 g=2.321\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch160/256, d1=0.387, d2=0.367 g=2.419\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch161/256, d1=0.409, d2=0.320 g=1.729\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch162/256, d1=0.313, d2=0.414 g=2.179\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch163/256, d1=0.394, d2=0.386 g=2.346\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch164/256, d1=0.391, d2=0.342 g=2.346\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch165/256, d1=0.620, d2=0.415 g=1.768\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch166/256, d1=0.311, d2=0.443 g=1.881\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch167/256, d1=0.427, d2=0.504 g=2.247\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch168/256, d1=0.584, d2=0.498 g=2.239\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch169/256, d1=0.458, d2=0.333 g=1.985\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch170/256, d1=0.457, d2=0.486 g=1.904\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch171/256, d1=0.348, d2=0.433 g=2.199\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch172/256, d1=0.548, d2=0.383 g=2.403\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch173/256, d1=0.527, d2=0.364 g=2.051\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch174/256, d1=0.521, d2=0.392 g=1.970\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch175/256, d1=0.377, d2=0.505 g=2.200\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch176/256, d1=0.491, d2=0.344 g=2.198\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch177/256, d1=0.425, d2=0.415 g=2.359\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch178/256, d1=0.421, d2=0.293 g=2.372\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch179/256, d1=0.567, d2=0.422 g=1.919\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch180/256, d1=0.363, d2=0.486 g=2.083\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch181/256, d1=0.457, d2=0.283 g=2.226\n","2/2 [==============================] - 0s 16ms/step\n","Epoch\u003e3, Batch182/256, d1=0.358, d2=0.391 g=1.977\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch183/256, d1=0.415, d2=0.418 g=2.003\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch184/256, d1=0.281, d2=0.296 g=2.258\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch185/256, d1=0.350, d2=0.332 g=2.234\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch186/256, d1=0.358, d2=0.468 g=2.092\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch187/256, d1=0.530, d2=0.440 g=2.037\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch188/256, d1=0.426, d2=0.443 g=1.946\n","2/2 [==============================] - 0s 15ms/step\n","Epoch\u003e3, Batch189/256, d1=0.420, d2=0.367 g=1.916\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch190/256, d1=0.320, d2=0.438 g=1.816\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch191/256, d1=0.405, d2=0.431 g=1.908\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch192/256, d1=0.442, d2=0.391 g=1.840\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch193/256, d1=0.409, d2=0.479 g=1.907\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch194/256, d1=0.457, d2=0.424 g=1.991\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch195/256, d1=0.389, d2=0.450 g=2.023\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch196/256, d1=0.434, d2=0.513 g=2.368\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch197/256, d1=0.449, d2=0.274 g=2.451\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch198/256, d1=0.471, d2=0.334 g=2.090\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch199/256, d1=0.334, d2=0.609 g=2.460\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch200/256, d1=0.391, d2=0.251 g=2.433\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch201/256, d1=0.296, d2=0.365 g=2.476\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch202/256, d1=0.562, d2=0.352 g=2.154\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch203/256, d1=0.372, d2=0.354 g=2.112\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch204/256, d1=0.390, d2=0.434 g=2.305\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch205/256, d1=0.482, d2=0.349 g=2.099\n","2/2 [==============================] - 0s 16ms/step\n","Epoch\u003e3, Batch206/256, d1=0.374, d2=0.420 g=2.586\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch207/256, d1=0.394, d2=0.274 g=2.412\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch208/256, d1=0.527, d2=0.431 g=2.107\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch209/256, d1=0.444, d2=0.371 g=1.951\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch210/256, d1=0.362, d2=0.470 g=2.028\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch211/256, d1=0.374, d2=0.494 g=2.301\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch212/256, d1=0.393, d2=0.318 g=2.294\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch213/256, d1=0.569, d2=0.394 g=2.195\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch214/256, d1=0.442, d2=0.488 g=1.804\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch215/256, d1=0.316, d2=0.442 g=2.307\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch216/256, d1=0.465, d2=0.387 g=2.659\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch217/256, d1=0.361, d2=0.312 g=2.195\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch218/256, d1=0.401, d2=0.256 g=1.989\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch219/256, d1=0.343, d2=0.457 g=2.065\n","2/2 [==============================] - 0s 18ms/step\n","Epoch\u003e3, Batch220/256, d1=0.335, d2=0.436 g=2.478\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch221/256, d1=0.465, d2=0.359 g=2.519\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch222/256, d1=0.554, d2=0.421 g=2.242\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e3, Batch223/256, d1=0.419, d2=0.378 g=2.178\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch224/256, d1=0.373, d2=0.431 g=2.502\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch225/256, d1=0.524, d2=0.328 g=2.345\n","2/2 [==============================] - 0s 14ms/step\n","Epoch\u003e3, Batch226/256, d1=0.499, d2=0.356 g=2.268\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch227/256, d1=0.439, d2=0.492 g=2.285\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch228/256, d1=0.491, d2=0.418 g=2.258\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch229/256, d1=0.534, d2=0.488 g=2.259\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch230/256, d1=0.477, d2=0.372 g=2.234\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch231/256, d1=0.507, d2=0.402 g=2.060\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch232/256, d1=0.350, d2=0.442 g=2.205\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch233/256, d1=0.463, d2=0.437 g=2.097\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch234/256, d1=0.383, d2=0.354 g=2.298\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch235/256, d1=0.406, d2=0.434 g=2.590\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch236/256, d1=0.462, d2=0.391 g=2.644\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch237/256, d1=0.476, d2=0.327 g=2.530\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e3, Batch238/256, d1=0.347, d2=0.295 g=2.112\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e3, Batch239/256, d1=0.266, d2=0.357 g=2.085\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch240/256, d1=0.363, d2=0.385 g=2.273\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch241/256, d1=0.306, d2=0.207 g=2.146\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch242/256, d1=0.386, d2=0.357 g=1.958\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch243/256, d1=0.261, d2=0.488 g=2.467\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch244/256, d1=0.593, d2=0.273 g=1.999\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch245/256, d1=0.483, d2=0.523 g=2.089\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch246/256, d1=0.342, d2=0.452 g=2.394\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e3, Batch247/256, d1=0.310, d2=0.316 g=2.582\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e3, Batch248/256, d1=0.524, d2=0.363 g=2.001\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch249/256, d1=0.376, d2=0.406 g=2.111\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch250/256, d1=0.361, d2=0.375 g=2.191\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch251/256, d1=0.410, d2=0.546 g=2.522\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e3, Batch252/256, d1=0.499, d2=0.309 g=2.294\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e3, Batch253/256, d1=0.467, d2=0.354 g=2.360\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e3, Batch254/256, d1=0.362, d2=0.441 g=2.662\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e3, Batch255/256, d1=0.432, d2=0.252 g=2.179\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e3, Batch256/256, d1=0.358, d2=0.510 g=2.269\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch1/256, d1=0.373, d2=0.356 g=2.689\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch2/256, d1=0.541, d2=0.440 g=2.350\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch3/256, d1=0.486, d2=0.397 g=2.453\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch4/256, d1=0.378, d2=0.301 g=2.822\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch5/256, d1=0.497, d2=0.391 g=2.320\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch6/256, d1=0.552, d2=0.312 g=2.303\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch7/256, d1=0.294, d2=0.422 g=2.161\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e4, Batch8/256, d1=0.410, d2=0.365 g=2.361\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch9/256, d1=0.468, d2=0.363 g=2.676\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch10/256, d1=0.442, d2=0.366 g=2.551\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch11/256, d1=0.452, d2=0.318 g=2.244\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch12/256, d1=0.295, d2=0.366 g=2.494\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch13/256, d1=0.419, d2=0.252 g=2.710\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch14/256, d1=0.401, d2=0.401 g=2.232\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch15/256, d1=0.385, d2=0.330 g=2.225\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch16/256, d1=0.479, d2=0.574 g=2.225\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch17/256, d1=0.431, d2=0.381 g=2.313\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch18/256, d1=0.493, d2=0.275 g=2.073\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch19/256, d1=0.465, d2=0.454 g=1.827\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e4, Batch20/256, d1=0.302, d2=0.526 g=2.347\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch21/256, d1=0.487, d2=0.342 g=2.663\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e4, Batch22/256, d1=0.276, d2=0.370 g=2.367\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch23/256, d1=0.422, d2=0.259 g=2.249\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e4, Batch24/256, d1=0.466, d2=0.357 g=2.125\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch25/256, d1=0.390, d2=0.424 g=2.319\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch26/256, d1=0.363, d2=0.358 g=2.048\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch27/256, d1=0.338, d2=0.282 g=2.294\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch28/256, d1=0.248, d2=0.356 g=2.315\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch29/256, d1=0.514, d2=0.326 g=2.318\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch30/256, d1=0.320, d2=0.425 g=2.415\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch31/256, d1=0.483, d2=0.356 g=2.104\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch32/256, d1=0.368, d2=0.347 g=2.276\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch33/256, d1=0.477, d2=0.439 g=2.500\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e4, Batch34/256, d1=0.564, d2=0.321 g=2.425\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch35/256, d1=0.353, d2=0.312 g=2.210\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch36/256, d1=0.357, d2=0.449 g=2.452\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch37/256, d1=0.406, d2=0.356 g=2.546\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch38/256, d1=0.308, d2=0.219 g=2.605\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch39/256, d1=0.380, d2=0.345 g=2.316\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch40/256, d1=0.422, d2=0.296 g=2.192\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch41/256, d1=0.362, d2=0.357 g=2.021\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch42/256, d1=0.240, d2=0.314 g=2.333\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch43/256, d1=0.386, d2=0.311 g=2.405\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch44/256, d1=0.421, d2=0.510 g=2.350\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch45/256, d1=0.384, d2=0.425 g=2.304\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch46/256, d1=0.366, d2=0.372 g=2.159\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch47/256, d1=0.413, d2=0.406 g=2.108\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch48/256, d1=0.553, d2=0.322 g=1.985\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch49/256, d1=0.349, d2=0.512 g=2.238\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch50/256, d1=0.305, d2=0.334 g=2.715\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch51/256, d1=0.571, d2=0.379 g=2.212\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch52/256, d1=0.374, d2=0.373 g=2.084\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch53/256, d1=0.408, d2=0.410 g=2.346\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch54/256, d1=0.500, d2=0.320 g=2.024\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch55/256, d1=0.416, d2=0.551 g=2.440\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e4, Batch56/256, d1=0.402, d2=0.341 g=2.422\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch57/256, d1=0.328, d2=0.262 g=2.265\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch58/256, d1=0.293, d2=0.341 g=1.929\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch59/256, d1=0.432, d2=0.452 g=2.194\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch60/256, d1=0.440, d2=0.492 g=2.392\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch61/256, d1=0.476, d2=0.308 g=2.467\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch62/256, d1=0.488, d2=0.509 g=2.218\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch63/256, d1=0.336, d2=0.263 g=2.127\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch64/256, d1=0.480, d2=0.332 g=2.028\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch65/256, d1=0.463, d2=0.557 g=1.989\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch66/256, d1=0.442, d2=0.432 g=2.492\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch67/256, d1=0.646, d2=0.284 g=1.962\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch68/256, d1=0.446, d2=0.514 g=1.920\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch69/256, d1=0.295, d2=0.371 g=2.628\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch70/256, d1=0.534, d2=0.302 g=2.092\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch71/256, d1=0.425, d2=0.561 g=2.130\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch72/256, d1=0.456, d2=0.369 g=2.209\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch73/256, d1=0.510, d2=0.458 g=2.248\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch74/256, d1=0.384, d2=0.261 g=2.290\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch75/256, d1=0.515, d2=0.418 g=2.360\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch76/256, d1=0.387, d2=0.401 g=2.514\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch77/256, d1=0.439, d2=0.302 g=2.157\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch78/256, d1=0.312, d2=0.480 g=2.059\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch79/256, d1=0.376, d2=0.523 g=2.075\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch80/256, d1=0.462, d2=0.353 g=2.487\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch81/256, d1=0.407, d2=0.280 g=2.172\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch82/256, d1=0.393, d2=0.429 g=2.000\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch83/256, d1=0.259, d2=0.355 g=2.525\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch84/256, d1=0.394, d2=0.254 g=2.211\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch85/256, d1=0.474, d2=0.407 g=1.996\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch86/256, d1=0.386, d2=0.428 g=2.500\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch87/256, d1=0.457, d2=0.250 g=2.658\n","2/2 [==============================] - 0s 12ms/step\n","Epoch\u003e4, Batch88/256, d1=0.442, d2=0.412 g=2.376\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch89/256, d1=0.322, d2=0.246 g=2.689\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch90/256, d1=0.532, d2=0.309 g=2.212\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch91/256, d1=0.310, d2=0.451 g=2.166\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch92/256, d1=0.386, d2=0.519 g=2.511\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch93/256, d1=0.395, d2=0.367 g=2.403\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch94/256, d1=0.514, d2=0.379 g=2.023\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch95/256, d1=0.409, d2=0.543 g=2.216\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch96/256, d1=0.311, d2=0.360 g=2.535\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch97/256, d1=0.505, d2=0.348 g=2.021\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch98/256, d1=0.502, d2=0.443 g=2.171\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch99/256, d1=0.336, d2=0.487 g=2.543\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch100/256, d1=0.410, d2=0.327 g=2.608\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch101/256, d1=0.540, d2=0.389 g=2.240\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch102/256, d1=0.301, d2=0.336 g=2.497\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch103/256, d1=0.426, d2=0.290 g=2.160\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch104/256, d1=0.372, d2=0.448 g=1.998\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch105/256, d1=0.453, d2=0.464 g=2.389\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch106/256, d1=0.476, d2=0.362 g=2.054\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch107/256, d1=0.387, d2=0.425 g=2.479\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch108/256, d1=0.360, d2=0.318 g=2.088\n","2/2 [==============================] - 0s 7ms/step\n","Epoch\u003e4, Batch109/256, d1=0.304, d2=0.402 g=2.374\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch110/256, d1=0.477, d2=0.402 g=2.368\n","2/2 [==============================] - 0s 16ms/step\n","Epoch\u003e4, Batch111/256, d1=0.320, d2=0.415 g=2.393\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch112/256, d1=0.627, d2=0.348 g=2.533\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch113/256, d1=0.486, d2=0.247 g=2.297\n","2/2 [==============================] - 0s 13ms/step\n","Epoch\u003e4, Batch114/256, d1=0.275, d2=0.496 g=2.229\n","2/2 [==============================] - 0s 8ms/step\n","Epoch\u003e4, Batch115/256, d1=0.521, d2=0.331 g=3.010\n","2/2 [==============================] - 0s 9ms/step\n","Epoch\u003e4, Batch116/256, d1=0.386, d2=0.236 g=2.886\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch117/256, d1=0.429, d2=0.318 g=2.125\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch118/256, d1=0.391, d2=0.516 g=2.440\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch119/256, d1=0.522, d2=0.313 g=2.284\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch120/256, d1=0.419, d2=0.447 g=2.113\n","2/2 [==============================] - 0s 4ms/step\n","Epoch\u003e4, Batch121/256, d1=0.377, d2=0.419 g=2.389\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch122/256, d1=0.455, d2=0.362 g=2.332\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch123/256, d1=0.467, d2=0.300 g=2.233\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch124/256, d1=0.320, d2=0.357 g=2.190\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch125/256, d1=0.376, d2=0.335 g=2.451\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch126/256, d1=0.426, d2=0.443 g=2.228\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch127/256, d1=0.356, d2=0.430 g=2.494\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch128/256, d1=0.475, d2=0.352 g=2.394\n","2/2 [==============================] - 0s 10ms/step\n","Epoch\u003e4, Batch129/256, d1=0.353, d2=0.450 g=2.186\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch130/256, d1=0.413, d2=0.479 g=2.289\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch131/256, d1=0.465, d2=0.395 g=2.229\n","2/2 [==============================] - 0s 11ms/step\n","Epoch\u003e4, Batch132/256, d1=0.408, d2=0.410 g=2.439\n","2/2 [==============================] - 0s 6ms/step\n","Epoch\u003e4, Batch133/256, d1=0.462, d2=0.383 g=2.351\n","2/2 [==============================] - 0s 5ms/step\n","Epoch\u003e4, Batch134/256, d1=0.474, d2=0.379 g=2.275\n"]},{"ename":"KeyboardInterrupt","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m\u003cipython-input-40-e647ee3780a3\u003e\u001b[0m in \u001b[0;36m\u003ccell line: 1\u003e\u001b[0;34m()\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgan_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatent_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_batch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\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\u003cipython-input-38-73b609819e43\u003e\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(g_model, d_model, gan_model, dataset, latent_dim, n_epochs, n_batch)\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0;31m# update discriminator model weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;31m# train_on_batch allows you to update weights based on a collection of samples you provide\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---\u003e 15\u001b[0;31m                         \u001b[0md_loss_real\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_real\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_real\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_real\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     16\u001b[0m                                 \u001b[0;31m# generate 'fake' examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                         \u001b[0;34m[\u001b[0m\u001b[0mX_fake\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_fake\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_fake_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatent_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhalf_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics, return_dict)\u001b[0m\n\u001b[1;32m   2504\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2505\u001b[0m         ):\n\u001b[0;32m-\u003e 2506\u001b[0;31m             iterator = data_adapter.single_batch_iterator(\n\u001b[0m\u001b[1;32m   2507\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribute_strategy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2508\u001b[0m             )\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36msingle_batch_iterator\u001b[0;34m(strategy, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1831\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1832\u001b[0m     \u001b[0m_check_data_cardinality\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-\u003e 1833\u001b[0;31m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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   1834\u001b[0m     \u001b[0;32mif\u001b[0m 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name)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_from_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=unused-private-name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---\u003e 23\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_TensorDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/data/ops/from_tensors_op.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, element, name)\u001b[0m\n\u001b[1;32m     35\u001b[0m     variant_tensor = gen_dataset_ops.tensor_dataset(\n\u001b[1;32m     36\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---\u003e 37\u001b[0;31m         \u001b[0moutput_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_flat_tensor_shapes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structure\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     38\u001b[0m         metadata=self._metadata.SerializeToString())\n\u001b[1;32m     39\u001b[0m     \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariant_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/data/util/structure.py\u001b[0m in \u001b[0;36mget_flat_tensor_shapes\u001b[0;34m(element_spec)\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[0mA\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorShapes\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0ms\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0melement\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0mrepresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m   \"\"\"\n\u001b[0;32m--\u003e 305\u001b[0;31m   \u001b[0;32mreturn\u001b[0m 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  292\u001b[0m           spec._flat_tensor_specs for spec in nest.flatten(element_spec)))\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/data/util/structure.py\u001b[0m in \u001b[0;36m\u003cgenexpr\u003e\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    289\u001b[0m   \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m   return list(\n\u001b[0;32m--\u003e 291\u001b[0;31m       itertools.chain.from_iterable(\n\u001b[0m\u001b[1;32m    292\u001b[0m           spec._flat_tensor_specs for spec in nest.flatten(element_spec)))\n\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=10, n_batch=128)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1681899756477,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"fnxQH640fahr","outputId":"6c97e8ff-ef7c-4e89-e9c2-c63487b0208d"},"outputs":[{"data":{"text/plain":["(365, 883, 1534)"]},"execution_count":92,"metadata":{},"output_type":"execute_result"}],"source":["countzero, countone, counttwo, countthree, countfour, countfive = 0,0,0,0,0\n","for i in range(0, len(yy)):\n","  if(yy[i]==0):\n","    countzero+=1\n","  elif(yy[i]==1):\n","    countone+=1\n","  elif(yy[i]==2):\n","    counttwo+=1\n","  elif(yy[i]==3):\n","    counttwo+=1\n","  elif(yy[i]==4):\n","    counttwo+=1\n","  elif(yy[i]==5):\n","    counttwo+=1\n","countzero, countone, counttwo, countthree, countfour, countfive"]},{"cell_type":"markdown","metadata":{"id":"roy3zTs__d_6"},"source":["no of ones to generate is 651 and number of zeroes to generate is 1169\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1681899756478,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"Z9NeDzpZxl8o","outputId":"3cf528f9-1f75-4278-f490-457435cb3630"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"]}],"source":["Generator = load_model('Generator.h5')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kflvHufwq9UJ"},"outputs":[],"source":["def cosine_similarity(a,b):\n","  return np.dot(a,b)/(norm(a)*norm(b))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aOD_8IC0oKCI"},"outputs":[],"source":["#generate different value data \n","\n","def generatesamples(label, noofsample):\n","  latent_points, labels = generate_latent_points(100,noofsample)\n","  labels = np.arange(noofsample)\n","  for i in range(0, len(labels)):\n","    labels[i] = label\n","  labelVAL = Generator.predict([latent_points, labels])\n","  return labelVAL\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yyW0kqTUGnPP"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ySXozP12CWeu"},"outputs":[],"source":["predzero = counttwo-countzero\n","predone = counttwo-countone"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":970,"status":"ok","timestamp":1681899757444,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"79OISYL2qrAd","outputId":"cb64339d-10da-4bf7-84df-9bfd0439b672"},"outputs":[{"name":"stdout","output_type":"stream","text":["37/37 [==============================] - 1s 5ms/step\n","21/21 [==============================] - 0s 3ms/step\n","1/1 [==============================] - 0s 42ms/step\n"]}],"source":["generateZerolabel = generatesamples(0, predzero)\n","generateOnelabel = generatesamples(1, predone)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X2_jAo8NtqiE"},"outputs":[],"source":["def generateSentences(samplesVAL, vocabulary):\n","  sentences = []\n","  count = 0\n","  for j in generateZerolabel:\n","    count+=1\n","    words_zero = ''\n","    for i in j:\n","      maxi = 0\n","      word = ''\n","      for t in range(len(vocabulary)):\n","        cos_sim = cosine_similarity(np.array(vocabulary[t][1]), i)\n","        if(maxi\u003c cos_sim):\n","          maxi = cos_sim\n","          word = vocabulary[t][0]\n","      if(maxi\u003e0.5):\n","        words_zero+=word\n","        words_zero+=' '\n","    sentences.append(words_zero)\n","    print(count, words_zero)\n","  return sentences\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":311},"executionInfo":{"elapsed":67985,"status":"error","timestamp":1681899825427,"user":{"displayName":"MUKUND KUMAR","userId":"08396811209321913567"},"user_tz":-330},"id":"Wi3h8_q--1ii","outputId":"d73d2e56-bb37-4a5e-a8b5-a0403cc2be34"},"outputs":[{"name":"stdout","output_type":"stream","text":["1 time , with ) been double new well nah only chest ... true aww arrogant : swede sister one which one as http : \n","2 hmn hand mama only fella one having stinky . his but but or which : : http : \n","3 note first current gawd : only having gawd making being greasy being being aww and one with too one as http : \n"]},{"ename":"KeyboardInterrupt","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m\u003cipython-input-98-fec345c64614\u003e\u001b[0m in \u001b[0;36m\u003ccell line: 1\u003e\u001b[0;34m()\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0mzero_sent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerateSentences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerateZerolabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocabulary\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[0mone_sent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerateSentences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerateOnelabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocabulary\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[0mtwo_sent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerateSentences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerateTwolabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocabulary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m\u003cipython-input-97-21e9ade6c52c\u003e\u001b[0m in \u001b[0;36mgenerateSentences\u001b[0;34m(samplesVAL, vocabulary)\u001b[0m\n\u001b[1;32m      9\u001b[0m       \u001b[0mword\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     10\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m 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\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2515\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-\u003e 2516\u001b[0;31m                 \u001b[0msqnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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   2517\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqnorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2518\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["zero_sent = generateSentences(generateZerolabel, vocabulary)\n","one_sent = generateSentences(generateOnelabel, vocabulary)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZcIv-jqPCva1"},"outputs":[],"source":["pkl.dump(zero_sent, open('Zerosdataset.pkl', 'wb'))\n","pkl.dump(one_sent, open('Onesdataset.pkl', 'wb'))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2NM6i1iazz2M"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"name":"","version":""},"gpuClass":"standard","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":"3.10.9"},"vscode":{"interpreter":{"hash":"91d4e2271cf209bade5eacc82405e130e48ddbdbe9e6210b60ee5c51482ed339"}}},"nbformat":4,"nbformat_minor":0}