{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":87793,"databundleVersionId":12024591,"sourceType":"competition"}],"dockerImageVersionId":31012,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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**load the dataset**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport torch\n\ntrain_seq=pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding/train_sequences.v2.csv\")\ntrain_labels=pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding/train_labels.v2.csv\")\ntrain_seq.head(5)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:21:16.162563Z","iopub.execute_input":"2025-04-28T16:21:16.163216Z","iopub.status.idle":"2025-04-28T16:21:21.696905Z","shell.execute_reply.started":"2025-04-28T16:21:16.163173Z","shell.execute_reply":"2025-04-28T16:21:21.695794Z"}},"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":" target_id sequence \\\n0 7TAX_M CUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC... \n1 4WF1_CA AAUUGAAGAGUUUGAUCAUGGCUCAGAUUGAACGCUGGCGGCAGGC... \n2 8PVA_b UGCCUGGCGGCCGUAGCGCGGUGGUCCCACCUGACCCCAUGCCGAA... \n3 8OVE_BB CAACUGCAGACCGUACUCAUCACCGCAUCAGGUCCCCAAGCAUCGA... \n4 8JDL_w UACCUGGUUGAUCCUGCCAGUAGCAUUGCUUGCCAAAGAUUAAGCC... \n\n temporal_cutoff description \\\n0 2022-09-21 Cryo-EM structure of the Csy-AcrIF24-promoter ... \n1 2014-11-05 Crystal structure of the E. coli ribosome boun... \n2 2023-11-29 Structure of bacterial ribosome determined by ... \n3 2023-11-29 CRYO-EM STRUCTURE OF TRYPANOSOMA BRUCEI PROCYC... \n4 2023-12-06 Structure of the Human cytoplasmic Ribosome wi... \n\n all_sequences \n0 >7TAX_1|Chain A|CRISPR-associated protein Csy1... \n1 >4WF1_1|Chains A[auth AA], BB[auth CA]|16S rRN... \n2 >8PVA_1|Chain A|16S rRNA|Escherichia coli (562... \n3 >8OVE_1|Chain A[auth AA]|SSU rRNA|Trypanosoma ... \n4 >8JDL_1|Chain A|mRNA|Homo sapiens (9606)\\nUUAU... ","text/html":"
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target_idsequencetemporal_cutoffdescriptionall_sequences
07TAX_MCUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC...2022-09-21Cryo-EM structure of the Csy-AcrIF24-promoter ...>7TAX_1|Chain A|CRISPR-associated protein Csy1...
14WF1_CAAAUUGAAGAGUUUGAUCAUGGCUCAGAUUGAACGCUGGCGGCAGGC...2014-11-05Crystal structure of the E. coli ribosome boun...>4WF1_1|Chains A[auth AA], BB[auth CA]|16S rRN...
28PVA_bUGCCUGGCGGCCGUAGCGCGGUGGUCCCACCUGACCCCAUGCCGAA...2023-11-29Structure of bacterial ribosome determined by ...>8PVA_1|Chain A|16S rRNA|Escherichia coli (562...
38OVE_BBCAACUGCAGACCGUACUCAUCACCGCAUCAGGUCCCCAAGCAUCGA...2023-11-29CRYO-EM STRUCTURE OF TRYPANOSOMA BRUCEI PROCYC...>8OVE_1|Chain A[auth AA]|SSU rRNA|Trypanosoma ...
48JDL_wUACCUGGUUGAUCCUGCCAGUAGCAUUGCUUGCCAAAGAUUAAGCC...2023-12-06Structure of the Human cytoplasmic Ribosome wi...>8JDL_1|Chain A|mRNA|Homo sapiens (9606)\\nUUAU...
\n
"},"metadata":{}}],"execution_count":74},{"cell_type":"code","source":"train_labels.head(5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:21:21.697842Z","iopub.execute_input":"2025-04-28T16:21:21.698175Z","iopub.status.idle":"2025-04-28T16:21:21.710606Z","shell.execute_reply.started":"2025-04-28T16:21:21.69815Z","shell.execute_reply":"2025-04-28T16:21:21.709818Z"}},"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":" ID resname resid x_1 y_1 z_1\n0 7TAX_M_1 C 1 187.126007 148.246002 210.417999\n1 7TAX_M_2 U 2 185.255997 152.968002 204.617996\n2 7TAX_M_3 A 3 189.360992 161.802002 205.214996\n3 7TAX_M_4 A 4 186.000000 156.595993 209.951996\n4 7TAX_M_5 G 5 181.947998 158.186996 213.610992","text/html":"
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IDresnameresidx_1y_1z_1
07TAX_M_1C1187.126007148.246002210.417999
17TAX_M_2U2185.255997152.968002204.617996
27TAX_M_3A3189.360992161.802002205.214996
37TAX_M_4A4186.000000156.595993209.951996
47TAX_M_5G5181.947998158.186996213.610992
\n
"},"metadata":{}}],"execution_count":75},{"cell_type":"code","source":"min_len=1000\nmax_len=-1\nfor i in train_seq[\"sequence\"]:\n min_len=min(min_len,len(i))\n max_len=max(max_len,len(i))\nprint(f\"the min length {min_len} and max length {max_len}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:21:21.712109Z","iopub.execute_input":"2025-04-28T16:21:21.712388Z","iopub.status.idle":"2025-04-28T16:21:21.738476Z","shell.execute_reply.started":"2025-04-28T16:21:21.712366Z","shell.execute_reply":"2025-04-28T16:21:21.737417Z"}},"outputs":[{"name":"stdout","text":"the min length 10 and max length 4417\n","output_type":"stream"}],"execution_count":76},{"cell_type":"markdown","source":"# **create the seq and merge the data according to their target_id**","metadata":{}},{"cell_type":"code","source":"\ndef create_seq(train_seq):\n for base in [\"A\",\"G\",\"C\",\"U\"]:\n train_seq[f\"{base.lower()}_count\"]=train_seq[\"sequence\"].str.count(base)\n \n train_seq[\"sequence_len\"]=[ len(seq) for seq in train_seq[\"sequence\"]]\n \n return train_seq\n\ndef merger(train_seq,train_labels):\n train_seq=create_seq(train_seq)\n train_labels[\"target_id\"]=train_labels[\"ID\"].apply(lambda x:\"_\".join(x.split(\"_\")[:2]))\n df_merged=pd.merge(\n train_labels,\n train_seq,\n on=\"target_id\",\n how=\"left\"\n )\n return df_merged","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:23:03.100409Z","iopub.execute_input":"2025-04-28T16:23:03.100798Z","iopub.status.idle":"2025-04-28T16:23:03.107809Z","shell.execute_reply.started":"2025-04-28T16:23:03.10077Z","shell.execute_reply":"2025-04-28T16:23:03.106693Z"}},"outputs":[],"execution_count":77},{"cell_type":"code","source":"\ndf_merge=merger(train_seq,train_labels)\n\ndf_merge.head(5)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:23:08.432031Z","iopub.execute_input":"2025-04-28T16:23:08.432369Z","iopub.status.idle":"2025-04-28T16:23:12.628892Z","shell.execute_reply.started":"2025-04-28T16:23:08.432346Z","shell.execute_reply":"2025-04-28T16:23:12.627924Z"}},"outputs":[{"execution_count":78,"output_type":"execute_result","data":{"text/plain":" ID resname resid x_1 y_1 z_1 target_id \\\n0 7TAX_M_1 C 1 187.126007 148.246002 210.417999 7TAX_M \n1 7TAX_M_2 U 2 185.255997 152.968002 204.617996 7TAX_M \n2 7TAX_M_3 A 3 189.360992 161.802002 205.214996 7TAX_M \n3 7TAX_M_4 A 4 186.000000 156.595993 209.951996 7TAX_M \n4 7TAX_M_5 G 5 181.947998 158.186996 213.610992 7TAX_M \n\n sequence temporal_cutoff \\\n0 CUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC... 2022-09-21 \n1 CUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC... 2022-09-21 \n2 CUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC... 2022-09-21 \n3 CUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC... 2022-09-21 \n4 CUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC... 2022-09-21 \n\n description \\\n0 Cryo-EM structure of the Csy-AcrIF24-promoter ... \n1 Cryo-EM structure of the Csy-AcrIF24-promoter ... \n2 Cryo-EM structure of the Csy-AcrIF24-promoter ... \n3 Cryo-EM structure of the Csy-AcrIF24-promoter ... \n4 Cryo-EM structure of the Csy-AcrIF24-promoter ... \n\n all_sequences a_count g_count \\\n0 >7TAX_1|Chain A|CRISPR-associated protein Csy1... 13 16 \n1 >7TAX_1|Chain A|CRISPR-associated protein Csy1... 13 16 \n2 >7TAX_1|Chain A|CRISPR-associated protein Csy1... 13 16 \n3 >7TAX_1|Chain A|CRISPR-associated protein Csy1... 13 16 \n4 >7TAX_1|Chain A|CRISPR-associated protein Csy1... 13 16 \n\n c_count u_count sequence_len \n0 17 15 61 \n1 17 15 61 \n2 17 15 61 \n3 17 15 61 \n4 17 15 61 ","text/html":"
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IDresnameresidx_1y_1z_1target_idsequencetemporal_cutoffdescriptionall_sequencesa_countg_countc_countu_countsequence_len
07TAX_M_1C1187.126007148.246002210.4179997TAX_MCUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC...2022-09-21Cryo-EM structure of the Csy-AcrIF24-promoter ...>7TAX_1|Chain A|CRISPR-associated protein Csy1...1316171561
17TAX_M_2U2185.255997152.968002204.6179967TAX_MCUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC...2022-09-21Cryo-EM structure of the Csy-AcrIF24-promoter ...>7TAX_1|Chain A|CRISPR-associated protein Csy1...1316171561
27TAX_M_3A3189.360992161.802002205.2149967TAX_MCUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC...2022-09-21Cryo-EM structure of the Csy-AcrIF24-promoter ...>7TAX_1|Chain A|CRISPR-associated protein Csy1...1316171561
37TAX_M_4A4186.000000156.595993209.9519967TAX_MCUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC...2022-09-21Cryo-EM structure of the Csy-AcrIF24-promoter ...>7TAX_1|Chain A|CRISPR-associated protein Csy1...1316171561
47TAX_M_5G5181.947998158.186996213.6109927TAX_MCUAAGAAAUUCACGGCGGGCUUGAUGUCCGCGUCUACCUGAUUCAC...2022-09-21Cryo-EM structure of the Csy-AcrIF24-promoter ...>7TAX_1|Chain A|CRISPR-associated protein Csy1...1316171561
\n
"},"metadata":{}}],"execution_count":78},{"cell_type":"markdown","source":"# **merge data information**","metadata":{}},{"cell_type":"code","source":"df_merge.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:24:28.994548Z","iopub.execute_input":"2025-04-28T16:24:28.994956Z","iopub.status.idle":"2025-04-28T16:24:29.013074Z","shell.execute_reply.started":"2025-04-28T16:24:28.99493Z","shell.execute_reply":"2025-04-28T16:24:29.012138Z"}},"outputs":[{"name":"stdout","text":"\nRangeIndex: 3677095 entries, 0 to 3677094\nData columns (total 16 columns):\n # Column Dtype \n--- ------ ----- \n 0 ID object \n 1 resname object \n 2 resid int64 \n 3 x_1 float64\n 4 y_1 float64\n 5 z_1 float64\n 6 target_id object \n 7 sequence object \n 8 temporal_cutoff object \n 9 description object \n 10 all_sequences object \n 11 a_count int64 \n 12 g_count int64 \n 13 c_count int64 \n 14 u_count int64 \n 15 sequence_len int64 \ndtypes: float64(3), int64(6), object(7)\nmemory usage: 448.9+ MB\n","output_type":"stream"}],"execution_count":79},{"cell_type":"code","source":"df_merge.isnull().values.any()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:24:37.360541Z","iopub.execute_input":"2025-04-28T16:24:37.360868Z","iopub.status.idle":"2025-04-28T16:24:38.666048Z","shell.execute_reply.started":"2025-04-28T16:24:37.360847Z","shell.execute_reply":"2025-04-28T16:24:38.664963Z"}},"outputs":[{"execution_count":80,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":80},{"cell_type":"markdown","source":"# **remove unuse columns and encode categorical coloumn**","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nle=LabelEncoder()\n\ndef remove_trash(df):\n df.drop(columns=[\"description\",\"temporal_cutoff\",\"target_id\",\"ID\",\"resname\",\"all_sequences\",\"sequence\"],inplace=True)\n return df\ndef encode_categorical(df):\n df[\"resname_en\"]=le.fit_transform(df[\"resname\"])\n return df\n \n \n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:02.832359Z","iopub.execute_input":"2025-04-28T16:25:02.832758Z","iopub.status.idle":"2025-04-28T16:25:02.838637Z","shell.execute_reply.started":"2025-04-28T16:25:02.832731Z","shell.execute_reply":"2025-04-28T16:25:02.837565Z"}},"outputs":[],"execution_count":83},{"cell_type":"code","source":"\ndf_merge=encode_categorical(df_merge)\ndf_merge=remove_trash(df_merge)\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:05.354291Z","iopub.execute_input":"2025-04-28T16:25:05.354684Z","iopub.status.idle":"2025-04-28T16:25:06.076791Z","shell.execute_reply.started":"2025-04-28T16:25:05.354647Z","shell.execute_reply":"2025-04-28T16:25:06.075723Z"}},"outputs":[],"execution_count":84},{"cell_type":"code","source":"df_merge.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:09.771728Z","iopub.execute_input":"2025-04-28T16:25:09.772055Z","iopub.status.idle":"2025-04-28T16:25:09.784271Z","shell.execute_reply.started":"2025-04-28T16:25:09.772031Z","shell.execute_reply":"2025-04-28T16:25:09.78306Z"}},"outputs":[{"name":"stdout","text":"\nRangeIndex: 3677095 entries, 0 to 3677094\nData columns (total 10 columns):\n # Column Dtype \n--- ------ ----- \n 0 resid int64 \n 1 x_1 float64\n 2 y_1 float64\n 3 z_1 float64\n 4 a_count int64 \n 5 g_count int64 \n 6 c_count int64 \n 7 u_count int64 \n 8 sequence_len int64 \n 9 resname_en int64 \ndtypes: float64(3), int64(7)\nmemory usage: 280.5 MB\n","output_type":"stream"}],"execution_count":85},{"cell_type":"markdown","source":"# **split the data into train_input and train_output**","metadata":{}},{"cell_type":"code","source":"# X: All input features\ntrain_input = df_merge[['sequence_len', 'a_count', 'g_count', 'c_count', 'u_count']]\n\n# y: The target labels you want to predict\ntrain_output = df_merge[['x_1', 'y_1', 'z_1','resid','resname_en']]\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:21.766782Z","iopub.execute_input":"2025-04-28T16:25:21.767137Z","iopub.status.idle":"2025-04-28T16:25:21.986654Z","shell.execute_reply.started":"2025-04-28T16:25:21.767111Z","shell.execute_reply":"2025-04-28T16:25:21.985446Z"}},"outputs":[],"execution_count":86},{"cell_type":"code","source":"train_input.head(5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:25.15836Z","iopub.execute_input":"2025-04-28T16:25:25.158712Z","iopub.status.idle":"2025-04-28T16:25:25.169145Z","shell.execute_reply.started":"2025-04-28T16:25:25.158687Z","shell.execute_reply":"2025-04-28T16:25:25.167805Z"}},"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":" sequence_len a_count g_count c_count u_count\n0 61 13 16 17 15\n1 61 13 16 17 15\n2 61 13 16 17 15\n3 61 13 16 17 15\n4 61 13 16 17 15","text/html":"
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sequence_lena_countg_countc_countu_count
06113161715
16113161715
26113161715
36113161715
46113161715
\n
"},"metadata":{}}],"execution_count":87},{"cell_type":"code","source":"train_output.head(1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:28.123593Z","iopub.execute_input":"2025-04-28T16:25:28.123979Z","iopub.status.idle":"2025-04-28T16:25:28.134321Z","shell.execute_reply.started":"2025-04-28T16:25:28.123954Z","shell.execute_reply":"2025-04-28T16:25:28.13339Z"}},"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":" x_1 y_1 z_1 resid resname_en\n0 187.126007 148.246002 210.417999 1 2","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
x_1y_1z_1residresname_en
0187.126007148.246002210.41799912
\n
"},"metadata":{}}],"execution_count":88},{"cell_type":"markdown","source":"# **split the data for train and test**","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\n\nx_train,x_test,y_train,y_test=train_test_split(train_input,train_output,test_size=0.2,random_state=42)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:31.342851Z","iopub.execute_input":"2025-04-28T16:25:31.343138Z","iopub.status.idle":"2025-04-28T16:25:32.632301Z","shell.execute_reply.started":"2025-04-28T16:25:31.343118Z","shell.execute_reply":"2025-04-28T16:25:32.63138Z"}},"outputs":[],"execution_count":89},{"cell_type":"code","source":"import numpy as np\nimport torch\nfrom torch.nn.utils.rnn import pad_sequence\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:35.497314Z","iopub.execute_input":"2025-04-28T16:25:35.497726Z","iopub.status.idle":"2025-04-28T16:25:35.50343Z","shell.execute_reply.started":"2025-04-28T16:25:35.497698Z","shell.execute_reply":"2025-04-28T16:25:35.50236Z"}},"outputs":[],"execution_count":90},{"cell_type":"code","source":"x_train.head(1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:39.559248Z","iopub.execute_input":"2025-04-28T16:25:39.559565Z","iopub.status.idle":"2025-04-28T16:25:39.569558Z","shell.execute_reply.started":"2025-04-28T16:25:39.559543Z","shell.execute_reply":"2025-04-28T16:25:39.568683Z"}},"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" sequence_len a_count g_count c_count u_count\n2064828 2749 714 843 688 504","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sequence_lena_countg_countc_countu_count
20648282749714843688504
\n
"},"metadata":{}}],"execution_count":91},{"cell_type":"markdown","source":"# **model defination**","metadata":{}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\n\n\nclass RNAFoldingModel(nn.Module):\n def __init__(self, input_dim, hidden_dim=128):\n super(RNAFoldingModel, self).__init__()\n\n self.model = nn.Sequential(\n nn.Linear(input_dim, hidden_dim),\n nn.ReLU(),\n nn.Dropout(0.3),\n nn.Linear(hidden_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, 5) # Predict (x_1, y_1, z_1,resid,resname)\n )\n\n def forward(self, x):\n return self.model(x)\n\nmodel=RNAFoldingModel(5)\ncriterion=nn.MSELoss() # Mean Squared Error for regression\noptimizer=torch.optim.Adam(model.parameters(),lr=0.01) # Adam optimizer\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:42.311202Z","iopub.execute_input":"2025-04-28T16:25:42.311537Z","iopub.status.idle":"2025-04-28T16:25:42.324509Z","shell.execute_reply.started":"2025-04-28T16:25:42.311513Z","shell.execute_reply":"2025-04-28T16:25:42.323559Z"}},"outputs":[],"execution_count":92},{"cell_type":"code","source":"# Before training loop\nx_train_tensor = torch.tensor(x_train.values, dtype=torch.float32)\ny_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:25:45.507513Z","iopub.execute_input":"2025-04-28T16:25:45.508252Z","iopub.status.idle":"2025-04-28T16:25:45.593561Z","shell.execute_reply.started":"2025-04-28T16:25:45.508222Z","shell.execute_reply":"2025-04-28T16:25:45.592655Z"}},"outputs":[],"execution_count":93},{"cell_type":"markdown","source":"# **traning loop**","metadata":{}},{"cell_type":"code","source":"num_epoch=200\nfor epoch in range(num_epoch):\n model.train()\n optimizer.zero_grad()\n outputs=model(x_train_tensor)\n loss=criterion(outputs,y_train_tensor)\n loss.backward()\n optimizer.step()\n if epoch%10==0:\n print(f\"Epoch {epoch:03d} — Loss: {loss.item():.4f}\")\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-28T16:30:43.22567Z","iopub.execute_input":"2025-04-28T16:30:43.226Z"}},"outputs":[{"name":"stdout","text":"Epoch 000 — Loss: nan\nEpoch 010 — Loss: nan\nEpoch 020 — Loss: nan\nEpoch 030 — Loss: nan\nEpoch 040 — Loss: nan\nEpoch 050 — Loss: nan\nEpoch 060 — Loss: nan\nEpoch 070 — Loss: nan\nEpoch 080 — Loss: nan\nEpoch 090 — Loss: nan\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}