10_dvc_without_jenkins
This commit is contained in:
Wojciech Lidwin
parent
b003a1d5ad
commit
7dd2475f24
5
.dvc/config
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5
.dvc/config
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@ -0,0 +1,5 @@
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[core]
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no_scm = True
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remote = ium_ssh_remote
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['remote "ium_ssh_remote"']
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url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl
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2
.dvc/config.local
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2
.dvc/config.local
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@ -0,0 +1,2 @@
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['remote "ium_ssh_remote"']
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password = IUM@2021
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1
.dvc/tmp/lock
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1
.dvc/tmp/lock
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@ -0,0 +1 @@
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27860
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3
.dvcignore
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3
.dvcignore
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# Add patterns of files dvc should ignore, which could improve
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# the performance. Learn more at
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# https://dvc.org/doc/user-guide/dvcignore
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@ -12,7 +12,6 @@ RUN pip3 install seaborn
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RUN pip3 install numpy
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RUN pip3 install keras
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RUN pip3 install tensorflow
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RUN pip3 install scikit-learn
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RUN pip3 install argparse
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RUN pip3 install matplotlib
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RUN pip3 install sacred
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61201
baltimore_dev.csv
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baltimore_dev.csv
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Load Diff
28313
baltimore_test.csv
28313
baltimore_test.csv
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Load Diff
185603
baltimore_train.csv
185603
baltimore_train.csv
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Load Diff
17
dvc.yml
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17
dvc.yml
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stages:
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prepare:
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cmd: data.py
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deps:
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- data.py
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- BPD_Part_1_Victim_Based_Crime_Data.csv
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outs:
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- baltimore_test.csv
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- baltimore_train.csv
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- baltimore_dev.csv
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train:
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cmd: ium_train.py
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deps:
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- ium_train.py
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- baltimore_train.csv
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outs:
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- baltimore.zip
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27
ium_train.py
27
ium_train.py
@ -11,10 +11,6 @@ import numpy as np
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from sklearn.preprocessing import LabelEncoder
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import argparse
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import shutil
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from sacred.observers import FileStorageObserver, MongoObserver
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from sacred import Experiment
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ex = Experiment('s487197-train', save_git_info=False)
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def get_x_y(data):
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@ -30,31 +26,14 @@ def get_x_y(data):
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return data, x, y
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@ex.config
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def my_config():
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def train_model():
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parser = argparse.ArgumentParser(description='Train')
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parser.add_argument('-epochs', type=int, default=20)
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parser.add_argument('-lr', type=float, default=0.01)
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parser.add_argument('-validation_split', type=float, default=0.2)
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args = parser.parse_args()
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epochs=args.epochs
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lr=args.lr
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validation_split=args.validation_split
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return args
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@ex.automain
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def train_model(config):
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# parser = argparse.ArgumentParser(description='Train')
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# parser.add_argument('-epochs', type=int, default=20)
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# parser.add_argument('-lr', type=float, default=0.01)
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#parser.add_argument('-validation_split', type=float, default=0.2)
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ex.observers.append(FileStorageObserver('s487197'))
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
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args = config
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train = pd.read_csv('baltimore_train.csv')
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@ -81,5 +60,5 @@ def train_model(config):
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shutil.make_archive('baltimore', 'zip', 'baltimore_model')
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train_model(my_config())
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train_model()
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263
sacred_results/1/config.json
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263
sacred_results/1/config.json
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{
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"args": {
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"epochs": 20,
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"lr": 0.01,
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"py/object": "argparse.Namespace",
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"validation_split": 0.2
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},
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"epochs": 20,
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"lr": 0.01,
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"parser": {
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"_action_groups": [
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{
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"_action_groups": [],
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"_actions": {
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"py/id": 5
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},
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"_defaults": {
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"py/id": 18
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},
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"_group_actions": [],
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"_has_negative_number_optionals": {
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"py/id": 20
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},
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"_mutually_exclusive_groups": {
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"py/id": 17
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},
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"_negative_number_matcher": {
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"py/id": 19
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},
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"_option_string_actions": {
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"py/id": 9
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},
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"_registries": {
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"py/id": 2
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},
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"argument_default": null,
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"conflict_handler": "error",
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"description": null,
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"prefix_chars": "-",
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"py/object": "argparse._ArgumentGroup",
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"title": "positional arguments"
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},
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{
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"py/id": 8
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}
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],
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"_actions": [
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{
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"choices": null,
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"const": null,
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"container": {
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"_action_groups": [],
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"_actions": {
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"py/id": 5
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},
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"_defaults": {},
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"_group_actions": [
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{
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"py/id": 6
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},
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{
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"py/id": 10
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},
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{
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"py/id": 12
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},
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{
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"py/id": 14
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}
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],
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"_has_negative_number_optionals": [],
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"_mutually_exclusive_groups": [],
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"_negative_number_matcher": {
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"pattern": "^-\\d+$|^-\\d*\\.\\d+$",
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"py/object": "re.Pattern"
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},
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"_option_string_actions": {
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"--help": {
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"py/id": 6
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},
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"-epochs": {
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"choices": null,
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"const": null,
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"container": {
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"py/id": 8
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},
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"default": 20,
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"dest": "epochs",
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"help": null,
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"metavar": null,
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"nargs": null,
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"option_strings": [
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"-epochs"
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],
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"py/object": "argparse._StoreAction",
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"required": false,
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"type": {
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"py/type": "builtins.int"
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}
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},
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"-h": {
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"py/id": 6
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},
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"-lr": {
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"choices": null,
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"const": null,
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"container": {
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"py/id": 8
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},
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"default": 0.01,
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"dest": "lr",
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"help": null,
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"metavar": null,
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"nargs": null,
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"option_strings": [
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"-lr"
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],
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"py/object": "argparse._StoreAction",
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"required": false,
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"type": {
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"py/type": "builtins.float"
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}
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},
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"-validation_split": {
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"choices": null,
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"const": null,
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"container": {
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"py/id": 8
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},
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"default": 0.2,
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"dest": "validation_split",
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"help": null,
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"metavar": null,
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"nargs": null,
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"option_strings": [
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"-validation_split"
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],
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"py/object": "argparse._StoreAction",
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"required": false,
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"type": {
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"py/type": "builtins.float"
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}
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}
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},
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"_registries": {
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"py/id": 2
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},
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"argument_default": null,
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"conflict_handler": "error",
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"description": null,
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"prefix_chars": "-",
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"py/object": "argparse._ArgumentGroup",
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"title": "options"
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},
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"default": "==SUPPRESS==",
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"dest": "help",
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"help": "show this help message and exit",
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"metavar": null,
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"nargs": 0,
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"option_strings": [
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"-h",
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"--help"
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],
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"py/object": "argparse._HelpAction",
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"required": false,
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"type": null
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},
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{
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"py/id": 10
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},
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{
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"py/id": 12
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},
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{
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"py/id": 14
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}
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],
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"_defaults": {
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"py/id": 18
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},
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"_has_negative_number_optionals": {
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"py/id": 20
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},
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"_mutually_exclusive_groups": {
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"py/id": 17
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},
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"_negative_number_matcher": {
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"py/id": 19
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},
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"_option_string_actions": {
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"py/id": 9
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},
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"_optionals": {
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"py/id": 8
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},
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"_positionals": {
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"py/id": 23
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},
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"_registries": {
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"action": {
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"append": {
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"py/type": "argparse._AppendAction"
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},
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"append_const": {
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"py/type": "argparse._AppendConstAction"
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},
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"count": {
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"py/type": "argparse._CountAction"
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},
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"extend": {
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"py/type": "argparse._ExtendAction"
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},
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"help": {
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"py/type": "argparse._HelpAction"
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},
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"json://null": {
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"py/type": "argparse._StoreAction"
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},
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"parsers": {
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"py/type": "argparse._SubParsersAction"
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},
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"store": {
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"py/type": "argparse._StoreAction"
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},
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"store_const": {
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"py/type": "argparse._StoreConstAction"
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},
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"store_false": {
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"py/type": "argparse._StoreFalseAction"
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},
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"store_true": {
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"py/type": "argparse._StoreTrueAction"
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},
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"version": {
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"py/type": "argparse._VersionAction"
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}
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},
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"type": {
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"json://null": {
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"py/function": "argparse.ArgumentParser.__init__.<locals>.identity"
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}
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}
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},
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"_subparsers": null,
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"add_help": true,
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"allow_abbrev": true,
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"argument_default": null,
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"conflict_handler": "error",
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"description": "Train",
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"epilog": null,
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"exit_on_error": true,
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"formatter_class": {
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"py/type": "argparse.HelpFormatter"
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},
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"fromfile_prefix_chars": null,
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"prefix_chars": "-",
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"prog": "ium_sacred.py",
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"py/object": "argparse.ArgumentParser",
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"usage": null
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},
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"seed": 743485559,
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"validation_split": 0.2
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}
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83
sacred_results/1/cout.txt
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83
sacred_results/1/cout.txt
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Model: "sequential"
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_________________________________________________________________
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Layer (type) Output Shape Param #
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=================================================================
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normalization (Normalizatio (None, 4) 9
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n)
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dense (Dense) (None, 64) 320
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dense_1 (Dense) (None, 10) 650
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dense_2 (Dense) (None, 10) 110
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dense_3 (Dense) (None, 10) 110
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dense_4 (Dense) (None, 5) 55
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=================================================================
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Total params: 1,254
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Trainable params: 1,245
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Non-trainable params: 9
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_________________________________________________________________
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Epoch 1/20
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1/200 [..............................] - ETA: 1:17 - loss: 1.5801 - accuracy: 0.5312
70/200 [=========>....................] - ETA: 0s - loss: 0.8091 - accuracy: 0.6902
142/200 [====================>.........] - ETA: 0s - loss: 0.5649 - accuracy: 0.7795
200/200 [==============================] - 1s 2ms/step - loss: 0.4757 - accuracy: 0.8151 - val_loss: 0.2506 - val_accuracy: 0.8931
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Epoch 2/20
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1/200 [..............................] - ETA: 0s - loss: 0.1264 - accuracy: 0.9375
68/200 [=========>....................] - ETA: 0s - loss: 0.2639 - accuracy: 0.8902
132/200 [==================>...........] - ETA: 0s - loss: 0.2225 - accuracy: 0.9105
198/200 [============================>.] - ETA: 0s - loss: 0.2061 - accuracy: 0.9192
200/200 [==============================] - 0s 1ms/step - loss: 0.2053 - accuracy: 0.9198 - val_loss: 0.2247 - val_accuracy: 0.8956
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Epoch 3/20
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1/200 [..............................] - ETA: 0s - loss: 0.4937 - accuracy: 0.8125
65/200 [========>.....................] - ETA: 0s - loss: 0.2858 - accuracy: 0.8990
138/200 [===================>..........] - ETA: 0s - loss: 0.2326 - accuracy: 0.9187
193/200 [===========================>..] - ETA: 0s - loss: 0.2153 - accuracy: 0.9229
200/200 [==============================] - 0s 1ms/step - loss: 0.2130 - accuracy: 0.9239 - val_loss: 0.1631 - val_accuracy: 0.9287
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Epoch 4/20
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1/200 [..............................] - ETA: 0s - loss: 0.1936 - accuracy: 0.9375
70/200 [=========>....................] - ETA: 0s - loss: 0.1466 - accuracy: 0.9415
141/200 [====================>.........] - ETA: 0s - loss: 0.1494 - accuracy: 0.9395
200/200 [==============================] - 0s 971us/step - loss: 0.1454 - accuracy: 0.9395 - val_loss: 0.1593 - val_accuracy: 0.9275
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Epoch 5/20
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1/200 [..............................] - ETA: 0s - loss: 0.1037 - accuracy: 0.9375
71/200 [=========>....................] - ETA: 0s - loss: 0.1510 - accuracy: 0.9375
142/200 [====================>.........] - ETA: 0s - loss: 0.1478 - accuracy: 0.9401
200/200 [==============================] - 0s 978us/step - loss: 0.1421 - accuracy: 0.9425 - val_loss: 0.1440 - val_accuracy: 0.9269
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Epoch 6/20
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1/200 [..............................] - ETA: 0s - loss: 0.1472 - accuracy: 0.9375
68/200 [=========>....................] - ETA: 0s - loss: 0.1387 - accuracy: 0.9481
141/200 [====================>.........] - ETA: 0s - loss: 0.1389 - accuracy: 0.9473
200/200 [==============================] - 0s 968us/step - loss: 0.1373 - accuracy: 0.9481 - val_loss: 0.1467 - val_accuracy: 0.9275
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Epoch 7/20
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1/200 [..............................] - ETA: 0s - loss: 0.1939 - accuracy: 0.9062
72/200 [=========>....................] - ETA: 0s - loss: 0.1466 - accuracy: 0.9405
139/200 [===================>..........] - ETA: 0s - loss: 0.1615 - accuracy: 0.9442
200/200 [==============================] - 0s 961us/step - loss: 0.2264 - accuracy: 0.9217 - val_loss: 0.2458 - val_accuracy: 0.9069
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Epoch 8/20
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1/200 [..............................] - ETA: 0s - loss: 0.3132 - accuracy: 0.8750
71/200 [=========>....................] - ETA: 0s - loss: 0.1977 - accuracy: 0.9291
140/200 [====================>.........] - ETA: 0s - loss: 0.1824 - accuracy: 0.9350
200/200 [==============================] - 0s 977us/step - loss: 0.1754 - accuracy: 0.9376 - val_loss: 0.1665 - val_accuracy: 0.9237
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Epoch 9/20
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1/200 [..............................] - ETA: 0s - loss: 0.3530 - accuracy: 0.8438
69/200 [=========>....................] - ETA: 0s - loss: 0.1742 - accuracy: 0.9330
142/200 [====================>.........] - ETA: 0s - loss: 0.1587 - accuracy: 0.9430
200/200 [==============================] - 0s 963us/step - loss: 0.1532 - accuracy: 0.9458 - val_loss: 0.1558 - val_accuracy: 0.9337
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Epoch 10/20
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1/200 [..............................] - ETA: 0s - loss: 0.0859 - accuracy: 0.9688
68/200 [=========>....................] - ETA: 0s - loss: 0.1445 - accuracy: 0.9563
137/200 [===================>..........] - ETA: 0s - loss: 0.1357 - accuracy: 0.9557
200/200 [==============================] - 0s 986us/step - loss: 0.1363 - accuracy: 0.9523 - val_loss: 0.1488 - val_accuracy: 0.9519
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Epoch 11/20
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1/200 [..............................] - ETA: 0s - loss: 0.1136 - accuracy: 0.9688
71/200 [=========>....................] - ETA: 0s - loss: 0.1323 - accuracy: 0.9582
138/200 [===================>..........] - ETA: 0s - loss: 0.1300 - accuracy: 0.9577
200/200 [==============================] - 0s 965us/step - loss: 0.1360 - accuracy: 0.9533 - val_loss: 0.1379 - val_accuracy: 0.9594
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Epoch 12/20
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1/200 [..............................] - ETA: 0s - loss: 0.1272 - accuracy: 0.9688
70/200 [=========>....................] - ETA: 0s - loss: 0.1273 - accuracy: 0.9558
141/200 [====================>.........] - ETA: 0s - loss: 0.1340 - accuracy: 0.9535
200/200 [==============================] - 0s 974us/step - loss: 0.1399 - accuracy: 0.9500 - val_loss: 0.1596 - val_accuracy: 0.9331
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Epoch 13/20
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1/200 [..............................] - ETA: 0s - loss: 0.3119 - accuracy: 0.8750
69/200 [=========>....................] - ETA: 0s - loss: 0.2623 - accuracy: 0.9094
137/200 [===================>..........] - ETA: 0s - loss: 0.2135 - accuracy: 0.9281
200/200 [==============================] - 0s 969us/step - loss: 0.1915 - accuracy: 0.9369 - val_loss: 0.1526 - val_accuracy: 0.9294
|
||||
Epoch 14/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1455 - accuracy: 0.9375
67/200 [=========>....................] - ETA: 0s - loss: 0.1483 - accuracy: 0.9468
136/200 [===================>..........] - ETA: 0s - loss: 0.1708 - accuracy: 0.9403
200/200 [==============================] - 0s 967us/step - loss: 0.1559 - accuracy: 0.9458 - val_loss: 0.1417 - val_accuracy: 0.9575
|
||||
Epoch 15/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1071 - accuracy: 0.9375
72/200 [=========>....................] - ETA: 0s - loss: 0.1138 - accuracy: 0.9618
139/200 [===================>..........] - ETA: 0s - loss: 0.1219 - accuracy: 0.9611
200/200 [==============================] - 0s 954us/step - loss: 0.1286 - accuracy: 0.9580 - val_loss: 0.1347 - val_accuracy: 0.9581
|
||||
Epoch 16/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.0978 - accuracy: 0.9688
71/200 [=========>....................] - ETA: 0s - loss: 0.1310 - accuracy: 0.9591
142/200 [====================>.........] - ETA: 0s - loss: 0.1285 - accuracy: 0.9571
200/200 [==============================] - 0s 964us/step - loss: 0.1322 - accuracy: 0.9559 - val_loss: 0.1416 - val_accuracy: 0.9581
|
||||
Epoch 17/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1309 - accuracy: 0.9688
73/200 [=========>....................] - ETA: 0s - loss: 0.1378 - accuracy: 0.9563
145/200 [====================>.........] - ETA: 0s - loss: 0.1389 - accuracy: 0.9534
200/200 [==============================] - 0s 941us/step - loss: 0.1344 - accuracy: 0.9551 - val_loss: 0.1389 - val_accuracy: 0.9594
|
||||
Epoch 18/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 1.5093e-04 - accuracy: 1.0000
61/200 [========>.....................] - ETA: 0s - loss: 0.1352 - accuracy: 0.9544
128/200 [==================>...........] - ETA: 0s - loss: 0.1287 - accuracy: 0.9568
200/200 [==============================] - ETA: 0s - loss: 0.1270 - accuracy: 0.9575
200/200 [==============================] - 0s 1ms/step - loss: 0.1270 - accuracy: 0.9575 - val_loss: 0.1362 - val_accuracy: 0.9594
|
||||
Epoch 19/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1404 - accuracy: 0.9688
69/200 [=========>....................] - ETA: 0s - loss: 0.1217 - accuracy: 0.9579
139/200 [===================>..........] - ETA: 0s - loss: 0.1347 - accuracy: 0.9530
200/200 [==============================] - 0s 959us/step - loss: 0.1296 - accuracy: 0.9542 - val_loss: 0.1349 - val_accuracy: 0.9594
|
||||
Epoch 20/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.3190 - accuracy: 0.8750
68/200 [=========>....................] - ETA: 0s - loss: 0.1388 - accuracy: 0.9550
133/200 [==================>...........] - ETA: 0s - loss: 0.1288 - accuracy: 0.9594
192/200 [===========================>..] - ETA: 0s - loss: 0.1266 - accuracy: 0.9585
200/200 [==============================] - 0s 1ms/step - loss: 0.1274 - accuracy: 0.9583 - val_loss: 0.1385 - val_accuracy: 0.9575
|
||||
loss accuracy val_loss val_accuracy epoch
|
||||
0 0.475650 0.815127 0.250621 0.893125 0
|
||||
1 0.205338 0.919831 0.224716 0.895625 1
|
||||
2 0.212970 0.923894 0.163060 0.928750 2
|
||||
3 0.145409 0.939522 0.159278 0.927500 3
|
||||
4 0.142135 0.942491 0.144023 0.926875 4
|
||||
5 0.137297 0.948117 0.146737 0.927500 5
|
||||
6 0.226381 0.921706 0.245775 0.906875 6
|
||||
7 0.175432 0.937647 0.166548 0.923750 7
|
||||
8 0.153183 0.945773 0.155808 0.933750 8
|
||||
9 0.136298 0.952336 0.148819 0.951875 9
|
||||
10 0.135954 0.953274 0.137862 0.959375 10
|
||||
11 0.139950 0.949992 0.159633 0.933125 11
|
||||
12 0.191454 0.936865 0.152623 0.929375 12
|
||||
13 0.155906 0.945773 0.141746 0.957500 13
|
||||
14 0.128649 0.957962 0.134679 0.958125 14
|
||||
15 0.132179 0.955931 0.141568 0.958125 15
|
||||
16 0.134409 0.955149 0.138940 0.959375 16
|
||||
17 0.127008 0.957493 0.136193 0.959375 17
|
||||
18 0.129558 0.954212 0.134924 0.959375 18
|
||||
19 0.127444 0.958275 0.138473 0.957500 19
|
||||
1
sacred_results/1/metrics.json
Normal file
1
sacred_results/1/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
76
sacred_results/1/run.json
Normal file
76
sacred_results/1/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
"sacred==0.8.4",
|
||||
"scikit-learn==1.2.2",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
263
sacred_results/10/config.json
Normal file
263
sacred_results/10/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
|
||||
"args": {
|
||||
"epochs": 20,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"parser": {
|
||||
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"_action_groups": [],
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
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||||
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||||
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||||
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||||
},
|
||||
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|
||||
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|
||||
"help": "show this help message and exit",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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0
sacred_results/10/cout.txt
Normal file
0
sacred_results/10/cout.txt
Normal file
76
sacred_results/10/run.json
Normal file
76
sacred_results/10/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
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||||
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|
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|
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|
||||
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|
||||
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|
||||
263
sacred_results/11/config.json
Normal file
263
sacred_results/11/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
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|
||||
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|
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||||
0
sacred_results/11/cout.txt
Normal file
0
sacred_results/11/cout.txt
Normal file
76
sacred_results/11/run.json
Normal file
76
sacred_results/11/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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263
sacred_results/12/config.json
Normal file
263
sacred_results/12/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
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||||
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|
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0
sacred_results/12/cout.txt
Normal file
0
sacred_results/12/cout.txt
Normal file
76
sacred_results/12/run.json
Normal file
76
sacred_results/12/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
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||||
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||||
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||||
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||||
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|
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"gpus": [
|
||||
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||||
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||||
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||||
"total_memory": 8192
|
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}
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||||
]
|
||||
},
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||||
"hostname": "AcerNitro5WL",
|
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"os": [
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"meta": {
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||||
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|
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}
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||||
},
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"resources": [],
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||||
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||||
"start_time": "2023-05-12T02:01:52.420346",
|
||||
"status": "COMPLETED",
|
||||
"stop_time": "2023-05-12T02:01:58.218217"
|
||||
}
|
||||
263
sacred_results/13/config.json
Normal file
263
sacred_results/13/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
|
||||
"args": {
|
||||
"epochs": 20,
|
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"lr": 0.01,
|
||||
"py/object": "argparse.Namespace",
|
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|
||||
},
|
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"epochs": 20,
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|
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|
||||
62
sacred_results/13/cout.txt
Normal file
62
sacred_results/13/cout.txt
Normal file
@ -0,0 +1,62 @@
|
||||
Model: "sequential"
|
||||
_________________________________________________________________
|
||||
Layer (type) Output Shape Param #
|
||||
=================================================================
|
||||
normalization (Normalizatio (None, 4) 9
|
||||
n)
|
||||
|
||||
dense (Dense) (None, 64) 320
|
||||
|
||||
dense_1 (Dense) (None, 10) 650
|
||||
|
||||
dense_2 (Dense) (None, 10) 110
|
||||
|
||||
dense_3 (Dense) (None, 10) 110
|
||||
|
||||
dense_4 (Dense) (None, 5) 55
|
||||
|
||||
=================================================================
|
||||
Total params: 1,254
|
||||
Trainable params: 1,245
|
||||
Non-trainable params: 9
|
||||
_________________________________________________________________
|
||||
Epoch 1/20
|
||||
1/200 [..............................] - ETA: 1:42 - loss: 1.6469 - accuracy: 0.0625
35/200 [====>.........................] - ETA: 0s - loss: 1.0588 - accuracy: 0.6205
71/200 [=========>....................] - ETA: 0s - loss: 0.8379 - accuracy: 0.6937
109/200 [===============>..............] - ETA: 0s - loss: 0.6690 - accuracy: 0.7491
167/200 [========================>.....] - ETA: 0s - loss: 0.5336 - accuracy: 0.7990
200/200 [==============================] - 1s 2ms/step - loss: 0.4828 - accuracy: 0.8181 - val_loss: 0.2411 - val_accuracy: 0.9162
|
||||
Epoch 2/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.2541 - accuracy: 0.9062
68/200 [=========>....................] - ETA: 0s - loss: 0.2156 - accuracy: 0.9154
135/200 [===================>..........] - ETA: 0s - loss: 0.1996 - accuracy: 0.9243
200/200 [==============================] - 0s 1ms/step - loss: 0.1881 - accuracy: 0.9284 - val_loss: 0.1701 - val_accuracy: 0.9275
|
||||
Epoch 3/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.0257 - accuracy: 1.0000
62/200 [========>.....................] - ETA: 0s - loss: 0.1566 - accuracy: 0.9375
129/200 [==================>...........] - ETA: 0s - loss: 0.1545 - accuracy: 0.9382
193/200 [===========================>..] - ETA: 0s - loss: 0.1551 - accuracy: 0.9388
200/200 [==============================] - 0s 2ms/step - loss: 0.1541 - accuracy: 0.9392 - val_loss: 0.1709 - val_accuracy: 0.9275
|
||||
Epoch 4/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.0209 - accuracy: 1.0000
68/200 [=========>....................] - ETA: 0s - loss: 0.2249 - accuracy: 0.9168
135/200 [===================>..........] - ETA: 0s - loss: 0.2761 - accuracy: 0.8988
200/200 [==============================] - 0s 998us/step - loss: 0.2380 - accuracy: 0.9139 - val_loss: 0.1782 - val_accuracy: 0.9275
|
||||
Epoch 5/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1677 - accuracy: 0.9375
70/200 [=========>....................] - ETA: 0s - loss: 0.1504 - accuracy: 0.9415
139/200 [===================>..........] - ETA: 0s - loss: 0.1517 - accuracy: 0.9402
200/200 [==============================] - 0s 1ms/step - loss: 0.1543 - accuracy: 0.9392 - val_loss: 0.1650 - val_accuracy: 0.9275
|
||||
Epoch 6/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1536 - accuracy: 0.9062
50/200 [======>.......................] - ETA: 0s - loss: 0.1602 - accuracy: 0.9362
106/200 [==============>...............] - ETA: 0s - loss: 0.1564 - accuracy: 0.9381
163/200 [=======================>......] - ETA: 0s - loss: 0.1503 - accuracy: 0.9408
200/200 [==============================] - 0s 1ms/step - loss: 0.1528 - accuracy: 0.9392 - val_loss: 0.1668 - val_accuracy: 0.9275
|
||||
Epoch 7/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.2504 - accuracy: 0.8438
58/200 [=======>......................] - ETA: 0s - loss: 0.1488 - accuracy: 0.9397
116/200 [================>.............] - ETA: 0s - loss: 0.1608 - accuracy: 0.9353
182/200 [==========================>...] - ETA: 0s - loss: 0.1533 - accuracy: 0.9394
200/200 [==============================] - 0s 1ms/step - loss: 0.1542 - accuracy: 0.9392 - val_loss: 0.1694 - val_accuracy: 0.9275
|
||||
Epoch 8/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1517 - accuracy: 0.9375
67/200 [=========>....................] - ETA: 0s - loss: 0.1519 - accuracy: 0.9403
134/200 [===================>..........] - ETA: 0s - loss: 0.1455 - accuracy: 0.9438
200/200 [==============================] - 0s 985us/step - loss: 0.1534 - accuracy: 0.9392 - val_loss: 0.1673 - val_accuracy: 0.9275
|
||||
Epoch 9/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1589 - accuracy: 0.9375
65/200 [========>.....................] - ETA: 0s - loss: 0.1525 - accuracy: 0.9409
129/200 [==================>...........] - ETA: 0s - loss: 0.1512 - accuracy: 0.9385
188/200 [===========================>..] - ETA: 0s - loss: 0.1517 - accuracy: 0.9397
200/200 [==============================] - 0s 1ms/step - loss: 0.1534 - accuracy: 0.9392 - val_loss: 0.1719 - val_accuracy: 0.9275
|
||||
Epoch 10/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.2366 - accuracy: 0.9062
59/200 [=======>......................] - ETA: 0s - loss: 0.1635 - accuracy: 0.9333
119/200 [================>.............] - ETA: 0s - loss: 0.1574 - accuracy: 0.9349
178/200 [=========================>....] - ETA: 0s - loss: 0.1538 - accuracy: 0.9389
200/200 [==============================] - 0s 1ms/step - loss: 0.1531 - accuracy: 0.9392 - val_loss: 0.1679 - val_accuracy: 0.9275
|
||||
Epoch 11/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1362 - accuracy: 0.9375
67/200 [=========>....................] - ETA: 0s - loss: 0.1433 - accuracy: 0.9403
133/200 [==================>...........] - ETA: 0s - loss: 0.2378 - accuracy: 0.9159
182/200 [==========================>...] - ETA: 0s - loss: 0.2254 - accuracy: 0.9196
200/200 [==============================] - 0s 1ms/step - loss: 0.2225 - accuracy: 0.9200 - val_loss: 0.1731 - val_accuracy: 0.9262
|
||||
Epoch 12/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.2198 - accuracy: 0.9062
49/200 [======>.......................] - ETA: 0s - loss: 0.1698 - accuracy: 0.9324
96/200 [=============>................] - ETA: 0s - loss: 0.2074 - accuracy: 0.9229
143/200 [====================>.........] - ETA: 0s - loss: 0.1951 - accuracy: 0.9268
188/200 [===========================>..] - ETA: 0s - loss: 0.1828 - accuracy: 0.9312
200/200 [==============================] - 0s 1ms/step - loss: 0.1807 - accuracy: 0.9314 - val_loss: 0.1702 - val_accuracy: 0.9275
|
||||
Epoch 13/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1454 - accuracy: 0.9688
61/200 [========>.....................] - ETA: 0s - loss: 0.1535 - accuracy: 0.9375
102/200 [==============>...............] - ETA: 0s - loss: 0.1504 - accuracy: 0.9390
139/200 [===================>..........] - ETA: 0s - loss: 0.1554 - accuracy: 0.9379
188/200 [===========================>..] - ETA: 0s - loss: 0.1534 - accuracy: 0.9393
200/200 [==============================] - 0s 1ms/step - loss: 0.1536 - accuracy: 0.9392 - val_loss: 0.1708 - val_accuracy: 0.9275
|
||||
Epoch 14/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1768 - accuracy: 0.9062
45/200 [=====>........................] - ETA: 0s - loss: 0.1386 - accuracy: 0.9458
89/200 [============>.................] - ETA: 0s - loss: 0.1537 - accuracy: 0.9393
127/200 [==================>...........] - ETA: 0s - loss: 0.1514 - accuracy: 0.9407
171/200 [========================>.....] - ETA: 0s - loss: 0.1543 - accuracy: 0.9388
200/200 [==============================] - 0s 1ms/step - loss: 0.1537 - accuracy: 0.9392 - val_loss: 0.1679 - val_accuracy: 0.9275
|
||||
Epoch 15/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.0631 - accuracy: 0.9688
63/200 [========>.....................] - ETA: 0s - loss: 0.1696 - accuracy: 0.9301
122/200 [=================>............] - ETA: 0s - loss: 0.1608 - accuracy: 0.9357
177/200 [=========================>....] - ETA: 0s - loss: 0.1523 - accuracy: 0.9400
200/200 [==============================] - 0s 1ms/step - loss: 0.1534 - accuracy: 0.9392 - val_loss: 0.1683 - val_accuracy: 0.9275
|
||||
Epoch 16/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1745 - accuracy: 0.9062
66/200 [========>.....................] - ETA: 0s - loss: 0.1553 - accuracy: 0.9361
131/200 [==================>...........] - ETA: 0s - loss: 0.1586 - accuracy: 0.9361
195/200 [============================>.] - ETA: 0s - loss: 0.1536 - accuracy: 0.9388
200/200 [==============================] - 0s 1ms/step - loss: 0.1531 - accuracy: 0.9392 - val_loss: 0.1715 - val_accuracy: 0.9275
|
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Epoch 17/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.1205 - accuracy: 0.9688
68/200 [=========>....................] - ETA: 0s - loss: 0.1562 - accuracy: 0.9375
136/200 [===================>..........] - ETA: 0s - loss: 0.1589 - accuracy: 0.9350
199/200 [============================>.] - ETA: 0s - loss: 0.1527 - accuracy: 0.9394
200/200 [==============================] - 0s 1ms/step - loss: 0.1531 - accuracy: 0.9392 - val_loss: 0.1716 - val_accuracy: 0.9275
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Epoch 18/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.2231 - accuracy: 0.9062
67/200 [=========>....................] - ETA: 0s - loss: 0.1630 - accuracy: 0.9324
132/200 [==================>...........] - ETA: 0s - loss: 0.1583 - accuracy: 0.9354
199/200 [============================>.] - ETA: 0s - loss: 0.1524 - accuracy: 0.9394
200/200 [==============================] - 0s 1ms/step - loss: 0.1529 - accuracy: 0.9392 - val_loss: 0.1697 - val_accuracy: 0.9275
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Epoch 19/20
|
||||
1/200 [..............................] - ETA: 0s - loss: 0.2123 - accuracy: 0.9062
68/200 [=========>....................] - ETA: 0s - loss: 0.1571 - accuracy: 0.9416
134/200 [===================>..........] - ETA: 0s - loss: 0.1584 - accuracy: 0.9373
200/200 [==============================] - ETA: 0s - loss: 0.1523 - accuracy: 0.9392
200/200 [==============================] - 0s 996us/step - loss: 0.1523 - accuracy: 0.9392 - val_loss: 0.1678 - val_accuracy: 0.9275
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Epoch 20/20
|
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1/200 [..............................] - ETA: 0s - loss: 0.0634 - accuracy: 1.0000
63/200 [========>.....................] - ETA: 0s - loss: 0.1386 - accuracy: 0.9449
132/200 [==================>...........] - ETA: 0s - loss: 0.1481 - accuracy: 0.9427
200/200 [==============================] - 0s 994us/step - loss: 0.1531 - accuracy: 0.9392 - val_loss: 0.1666 - val_accuracy: 0.9275
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||||
138
sacred_results/13/metrics.json
Normal file
138
sacred_results/13/metrics.json
Normal file
@ -0,0 +1,138 @@
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81
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Normal file
81
sacred_results/13/run.json
Normal file
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263
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Normal file
263
sacred_results/14/config.json
Normal file
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0
sacred_results/14/cout.txt
Normal file
0
sacred_results/14/cout.txt
Normal file
138
sacred_results/14/metrics.json
Normal file
138
sacred_results/14/metrics.json
Normal file
@ -0,0 +1,138 @@
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
90
sacred_results/14/run.json
Normal file
90
sacred_results/14/run.json
Normal file
@ -0,0 +1,90 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
" File \"C:\\Users\\JaSzw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n raise e.with_traceback(filtered_tb) from None\n",
|
||||
" File \"C:\\Users\\JaSzw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py\", line 58, in quick_execute\n except TypeError as e:\n",
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
263
sacred_results/15/config.json
Normal file
263
sacred_results/15/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
0
sacred_results/15/cout.txt
Normal file
0
sacred_results/15/cout.txt
Normal file
81
sacred_results/15/run.json
Normal file
81
sacred_results/15/run.json
Normal file
@ -0,0 +1,81 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
263
sacred_results/2/config.json
Normal file
263
sacred_results/2/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
|
||||
"args": {
|
||||
"epochs": 20,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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||||
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||||
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||||
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|
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|
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
0
sacred_results/2/cout.txt
Normal file
0
sacred_results/2/cout.txt
Normal file
1
sacred_results/2/metrics.json
Normal file
1
sacred_results/2/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
83
sacred_results/2/run.json
Normal file
83
sacred_results/2/run.json
Normal file
@ -0,0 +1,83 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
"command": "my_main",
|
||||
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|
||||
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|
||||
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|
||||
"keras==2.12.0",
|
||||
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|
||||
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|
||||
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|
||||
"scikit-learn==1.2.2",
|
||||
"tensorflow-intel==2.12.0"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"fail_trace": [
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\JaSzw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\wrapt\\wrappers.py\", line 578, in __call__\n return self._self_wrapper(self.__wrapped__, self._self_instance,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||||
" File \"C:\\Users\\JaSzw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||||
" File \"E:\\Ium\\ium_487197\\ium_sacred.py\", line 80, in my_main\n print(his['loss'])\n ~~~^^^^^^^^\n",
|
||||
"TypeError: string indices must be integers, not 'str'\n"
|
||||
],
|
||||
"heartbeat": "2023-05-12T01:54:53.653326",
|
||||
"host": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
]
|
||||
},
|
||||
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|
||||
"os": [
|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
263
sacred_results/3/config.json
Normal file
263
sacred_results/3/config.json
Normal file
@ -0,0 +1,263 @@
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0
sacred_results/3/cout.txt
Normal file
0
sacred_results/3/cout.txt
Normal file
1
sacred_results/3/metrics.json
Normal file
1
sacred_results/3/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
83
sacred_results/3/run.json
Normal file
83
sacred_results/3/run.json
Normal file
@ -0,0 +1,83 @@
|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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263
sacred_results/4/config.json
Normal file
263
sacred_results/4/config.json
Normal file
@ -0,0 +1,263 @@
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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||||
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|
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|
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
0
sacred_results/4/cout.txt
Normal file
0
sacred_results/4/cout.txt
Normal file
76
sacred_results/4/run.json
Normal file
76
sacred_results/4/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"sacred==0.8.4",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
[
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"hostname": "AcerNitro5WL",
|
||||
"os": [
|
||||
"Windows",
|
||||
"Windows-10-10.0.22621-SP0"
|
||||
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|
||||
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|
||||
},
|
||||
"meta": {
|
||||
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|
||||
"named_configs": [],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"stop_time": "2023-05-12T01:56:31.839801"
|
||||
}
|
||||
263
sacred_results/5/config.json
Normal file
263
sacred_results/5/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
|
||||
"args": {
|
||||
"epochs": 20,
|
||||
"lr": 0.01,
|
||||
"py/object": "argparse.Namespace",
|
||||
"validation_split": 0.2
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
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||||
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|
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|
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||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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|
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|
||||
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||||
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|
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|
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
"py/type": "argparse._StoreConstAction"
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|
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|
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|
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|
||||
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|
||||
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|
||||
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||||
0
sacred_results/5/cout.txt
Normal file
0
sacred_results/5/cout.txt
Normal file
76
sacred_results/5/run.json
Normal file
76
sacred_results/5/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"keras==2.12.0",
|
||||
"numpy==1.23.5",
|
||||
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|
||||
"sacred==0.8.4",
|
||||
"scikit-learn==1.2.2",
|
||||
"tensorflow-intel==2.12.0"
|
||||
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|
||||
"mainfile": "ium_sacred.py",
|
||||
"name": "s487197-train",
|
||||
"repositories": [],
|
||||
"sources": [
|
||||
[
|
||||
"ium_sacred.py",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
"gpus": [
|
||||
{
|
||||
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|
||||
"persistence_mode": false,
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"hostname": "AcerNitro5WL",
|
||||
"os": [
|
||||
"Windows",
|
||||
"Windows-10-10.0.22621-SP0"
|
||||
],
|
||||
"python_version": "3.11.2"
|
||||
},
|
||||
"meta": {
|
||||
"command": "my_main",
|
||||
"named_configs": [],
|
||||
"options": {
|
||||
"--beat-interval": null,
|
||||
"--capture": null,
|
||||
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|
||||
"--debug": false,
|
||||
"--enforce_clean": false,
|
||||
"--file_storage": null,
|
||||
"--force": false,
|
||||
"--help": false,
|
||||
"--id": null,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"start_time": "2023-05-12T01:57:06.376366",
|
||||
"status": "COMPLETED",
|
||||
"stop_time": "2023-05-12T01:57:12.134024"
|
||||
}
|
||||
263
sacred_results/6/config.json
Normal file
263
sacred_results/6/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
|
||||
"args": {
|
||||
"epochs": 20,
|
||||
"lr": 0.01,
|
||||
"py/object": "argparse.Namespace",
|
||||
"validation_split": 0.2
|
||||
},
|
||||
"epochs": 20,
|
||||
"lr": 0.01,
|
||||
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|
||||
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||||
{
|
||||
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||||
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||||
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||||
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||||
0
sacred_results/6/cout.txt
Normal file
0
sacred_results/6/cout.txt
Normal file
76
sacred_results/6/run.json
Normal file
76
sacred_results/6/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
263
sacred_results/7/config.json
Normal file
263
sacred_results/7/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
0
sacred_results/7/cout.txt
Normal file
0
sacred_results/7/cout.txt
Normal file
1
sacred_results/7/metrics.json
Normal file
1
sacred_results/7/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
83
sacred_results/7/run.json
Normal file
83
sacred_results/7/run.json
Normal file
@ -0,0 +1,83 @@
|
||||
{
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
" File \"E:\\Ium\\ium_487197\\ium_sacred.py\", line 80, in my_main\n print(his['loss'])\n ~~~^^^^^^^^\n",
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
263
sacred_results/8/config.json
Normal file
263
sacred_results/8/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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0
sacred_results/8/cout.txt
Normal file
0
sacred_results/8/cout.txt
Normal file
76
sacred_results/8/run.json
Normal file
76
sacred_results/8/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
[
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
}
|
||||
263
sacred_results/9/config.json
Normal file
263
sacred_results/9/config.json
Normal file
@ -0,0 +1,263 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
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||||
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||||
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|
||||
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||||
{
|
||||
"py/id": 14
|
||||
}
|
||||
],
|
||||
"_has_negative_number_optionals": [],
|
||||
"_mutually_exclusive_groups": [],
|
||||
"_negative_number_matcher": {
|
||||
"pattern": "^-\\d+$|^-\\d*\\.\\d+$",
|
||||
"py/object": "re.Pattern"
|
||||
},
|
||||
"_option_string_actions": {
|
||||
"--help": {
|
||||
"py/id": 6
|
||||
},
|
||||
"-epochs": {
|
||||
"choices": null,
|
||||
"const": null,
|
||||
"container": {
|
||||
"py/id": 8
|
||||
},
|
||||
"default": 20,
|
||||
"dest": "epochs",
|
||||
"help": null,
|
||||
"metavar": null,
|
||||
"nargs": null,
|
||||
"option_strings": [
|
||||
"-epochs"
|
||||
],
|
||||
"py/object": "argparse._StoreAction",
|
||||
"required": false,
|
||||
"type": {
|
||||
"py/type": "builtins.int"
|
||||
}
|
||||
},
|
||||
"-h": {
|
||||
"py/id": 6
|
||||
},
|
||||
"-lr": {
|
||||
"choices": null,
|
||||
"const": null,
|
||||
"container": {
|
||||
"py/id": 8
|
||||
},
|
||||
"default": 0.01,
|
||||
"dest": "lr",
|
||||
"help": null,
|
||||
"metavar": null,
|
||||
"nargs": null,
|
||||
"option_strings": [
|
||||
"-lr"
|
||||
],
|
||||
"py/object": "argparse._StoreAction",
|
||||
"required": false,
|
||||
"type": {
|
||||
"py/type": "builtins.float"
|
||||
}
|
||||
},
|
||||
"-validation_split": {
|
||||
"choices": null,
|
||||
"const": null,
|
||||
"container": {
|
||||
"py/id": 8
|
||||
},
|
||||
"default": 0.2,
|
||||
"dest": "validation_split",
|
||||
"help": null,
|
||||
"metavar": null,
|
||||
"nargs": null,
|
||||
"option_strings": [
|
||||
"-validation_split"
|
||||
],
|
||||
"py/object": "argparse._StoreAction",
|
||||
"required": false,
|
||||
"type": {
|
||||
"py/type": "builtins.float"
|
||||
}
|
||||
}
|
||||
},
|
||||
"_registries": {
|
||||
"py/id": 2
|
||||
},
|
||||
"argument_default": null,
|
||||
"conflict_handler": "error",
|
||||
"description": null,
|
||||
"prefix_chars": "-",
|
||||
"py/object": "argparse._ArgumentGroup",
|
||||
"title": "options"
|
||||
},
|
||||
"default": "==SUPPRESS==",
|
||||
"dest": "help",
|
||||
"help": "show this help message and exit",
|
||||
"metavar": null,
|
||||
"nargs": 0,
|
||||
"option_strings": [
|
||||
"-h",
|
||||
"--help"
|
||||
],
|
||||
"py/object": "argparse._HelpAction",
|
||||
"required": false,
|
||||
"type": null
|
||||
},
|
||||
{
|
||||
"py/id": 10
|
||||
},
|
||||
{
|
||||
"py/id": 12
|
||||
},
|
||||
{
|
||||
"py/id": 14
|
||||
}
|
||||
],
|
||||
"_defaults": {
|
||||
"py/id": 18
|
||||
},
|
||||
"_has_negative_number_optionals": {
|
||||
"py/id": 20
|
||||
},
|
||||
"_mutually_exclusive_groups": {
|
||||
"py/id": 17
|
||||
},
|
||||
"_negative_number_matcher": {
|
||||
"py/id": 19
|
||||
},
|
||||
"_option_string_actions": {
|
||||
"py/id": 9
|
||||
},
|
||||
"_optionals": {
|
||||
"py/id": 8
|
||||
},
|
||||
"_positionals": {
|
||||
"py/id": 23
|
||||
},
|
||||
"_registries": {
|
||||
"action": {
|
||||
"append": {
|
||||
"py/type": "argparse._AppendAction"
|
||||
},
|
||||
"append_const": {
|
||||
"py/type": "argparse._AppendConstAction"
|
||||
},
|
||||
"count": {
|
||||
"py/type": "argparse._CountAction"
|
||||
},
|
||||
"extend": {
|
||||
"py/type": "argparse._ExtendAction"
|
||||
},
|
||||
"help": {
|
||||
"py/type": "argparse._HelpAction"
|
||||
},
|
||||
"json://null": {
|
||||
"py/type": "argparse._StoreAction"
|
||||
},
|
||||
"parsers": {
|
||||
"py/type": "argparse._SubParsersAction"
|
||||
},
|
||||
"store": {
|
||||
"py/type": "argparse._StoreAction"
|
||||
},
|
||||
"store_const": {
|
||||
"py/type": "argparse._StoreConstAction"
|
||||
},
|
||||
"store_false": {
|
||||
"py/type": "argparse._StoreFalseAction"
|
||||
},
|
||||
"store_true": {
|
||||
"py/type": "argparse._StoreTrueAction"
|
||||
},
|
||||
"version": {
|
||||
"py/type": "argparse._VersionAction"
|
||||
}
|
||||
},
|
||||
"type": {
|
||||
"json://null": {
|
||||
"py/function": "argparse.ArgumentParser.__init__.<locals>.identity"
|
||||
}
|
||||
}
|
||||
},
|
||||
"_subparsers": null,
|
||||
"add_help": true,
|
||||
"allow_abbrev": true,
|
||||
"argument_default": null,
|
||||
"conflict_handler": "error",
|
||||
"description": "Train",
|
||||
"epilog": null,
|
||||
"exit_on_error": true,
|
||||
"formatter_class": {
|
||||
"py/type": "argparse.HelpFormatter"
|
||||
},
|
||||
"fromfile_prefix_chars": null,
|
||||
"prefix_chars": "-",
|
||||
"prog": "ium_sacred.py",
|
||||
"py/object": "argparse.ArgumentParser",
|
||||
"usage": null
|
||||
},
|
||||
"seed": 840512587,
|
||||
"validation_split": 0.2
|
||||
}
|
||||
0
sacred_results/9/cout.txt
Normal file
0
sacred_results/9/cout.txt
Normal file
76
sacred_results/9/run.json
Normal file
76
sacred_results/9/run.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
"command": "my_main",
|
||||
"experiment": {
|
||||
"base_dir": "E:\\Ium\\ium_487197",
|
||||
"dependencies": [
|
||||
"keras==2.12.0",
|
||||
"numpy==1.23.5",
|
||||
"pandas==1.5.3",
|
||||
"sacred==0.8.4",
|
||||
"scikit-learn==1.2.2",
|
||||
"tensorflow-intel==2.12.0"
|
||||
],
|
||||
"mainfile": "ium_sacred.py",
|
||||
"name": "s487197-train",
|
||||
"repositories": [],
|
||||
"sources": [
|
||||
[
|
||||
"ium_sacred.py",
|
||||
"_sources\\ium_sacred_e7097f8c1264234798bc616052caa420.py"
|
||||
]
|
||||
]
|
||||
},
|
||||
"heartbeat": null,
|
||||
"host": {
|
||||
"ENV": {},
|
||||
"cpu": "AMD Ryzen 9 5900HX with Radeon Graphics",
|
||||
"gpus": {
|
||||
"driver_version": "512.74",
|
||||
"gpus": [
|
||||
{
|
||||
"model": "NVIDIA GeForce RTX 3080 Laptop GPU",
|
||||
"persistence_mode": false,
|
||||
"total_memory": 8192
|
||||
}
|
||||
]
|
||||
},
|
||||
"hostname": "AcerNitro5WL",
|
||||
"os": [
|
||||
"Windows",
|
||||
"Windows-10-10.0.22621-SP0"
|
||||
],
|
||||
"python_version": "3.11.2"
|
||||
},
|
||||
"meta": {
|
||||
"command": "my_main",
|
||||
"named_configs": [],
|
||||
"options": {
|
||||
"--beat-interval": null,
|
||||
"--capture": null,
|
||||
"--comment": null,
|
||||
"--debug": false,
|
||||
"--enforce_clean": false,
|
||||
"--file_storage": null,
|
||||
"--force": false,
|
||||
"--help": false,
|
||||
"--id": null,
|
||||
"--loglevel": null,
|
||||
"--mongo_db": null,
|
||||
"--name": null,
|
||||
"--pdb": false,
|
||||
"--print-config": false,
|
||||
"--priority": null,
|
||||
"--queue": false,
|
||||
"--s3": null,
|
||||
"--sql": null,
|
||||
"--tiny_db": null,
|
||||
"--unobserved": false
|
||||
}
|
||||
},
|
||||
"resources": [],
|
||||
"result": null,
|
||||
"start_time": "2023-05-12T02:00:21.881935",
|
||||
"status": "COMPLETED",
|
||||
"stop_time": "2023-05-12T02:00:28.146176"
|
||||
}
|
||||
@ -0,0 +1,110 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
print(hist)
|
||||
return 0
|
||||
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
|
||||
ex.add_artifact('baltimore_model')
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
print(hist)
|
||||
for his in history.history:
|
||||
print(his)
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
# print(hist)
|
||||
for his in hist.iterrows():
|
||||
print(his[1])
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
print(hist)
|
||||
for his in hist.iterrows():
|
||||
print(his)
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
print(hist)
|
||||
for his in hist:
|
||||
print(his)
|
||||
_run.log_scalar('training.loss', his['loss'])
|
||||
_run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
# print(hist)
|
||||
for his in hist.iterrows():
|
||||
print(his['loss'])
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
# print(hist)
|
||||
for his in hist.iterrows():
|
||||
print(his[1]['loss'])
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,114 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
for his in hist.iterrows():
|
||||
_run.log_scalar('training.loss', his[1]['loss'])
|
||||
_run.log_scalar('accuracy', his[1]['accuracy'])
|
||||
model.save('baltimore_model')
|
||||
ex.add_artifact('baltimore_model')
|
||||
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
ex.run_commandline()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
print(hist)
|
||||
for his in hist:
|
||||
print(his['loss'])
|
||||
_run.log_scalar('training.loss', his['loss'])
|
||||
_run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
print(hist)
|
||||
for his in hist[1:]:
|
||||
print(his)
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,113 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.automain
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
for his in hist.iterrows():
|
||||
_run.log_scalar('training.loss', his[1]['loss'])
|
||||
_run.log_scalar('accuracy', his[1]['accuracy'])
|
||||
model.save('baltimore_model')
|
||||
ex.add_artifact('baltimore_model')
|
||||
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
#ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
# print(hist)
|
||||
for his in hist.iterrows():
|
||||
print(his)
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,113 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
for his in hist.iterrows():
|
||||
_run.log_scalar('training.loss', his[1]['loss'])
|
||||
_run.log_scalar('accuracy', his[1]['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
@ -0,0 +1,115 @@
|
||||
from keras.models import Sequential, load_model
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import Adam
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import argparse
|
||||
import shutil
|
||||
from sacred.observers import FileStorageObserver, MongoObserver
|
||||
from sacred import Experiment
|
||||
from sklearn import metrics
|
||||
import math
|
||||
|
||||
ex = Experiment('s487197-train', save_git_info=False,interactive=True)
|
||||
ex.observers.append(FileStorageObserver('sacred_results'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
|
||||
def write_list(names):
|
||||
with open('listfile.txt', 'w') as fp:
|
||||
fp.write("\n".join(str(item) for item in names))
|
||||
|
||||
def get_x_y(data):
|
||||
lb = LabelEncoder()
|
||||
data = data.drop(["Location 1"], axis=1)
|
||||
data = data.drop(
|
||||
columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
|
||||
"CrimeDate", "Inside/Outside"], axis=1)
|
||||
for column_name in data.columns:
|
||||
data[column_name] = lb.fit_transform(data[column_name])
|
||||
x = data.drop('Weapon', axis=1)
|
||||
y = data['Weapon']
|
||||
|
||||
return data, x, y
|
||||
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
parser = argparse.ArgumentParser(description='Train')
|
||||
|
||||
parser.add_argument('-epochs', type=int, default=20)
|
||||
parser.add_argument('-lr', type=float, default=0.01)
|
||||
parser.add_argument('-validation_split', type=float, default=0.2)
|
||||
args = parser.parse_args()
|
||||
epochs = args.epochs
|
||||
lr = args.lr
|
||||
validation_split = args.validation_split
|
||||
|
||||
@ex.capture
|
||||
def prepare_message(epochs, lr, validation_split):
|
||||
return "{0} {1} {2}!".format(epochs, lr, validation_split)
|
||||
|
||||
@ex.main
|
||||
def my_main(epochs, lr, validation_split, _run):
|
||||
train = pd.read_csv('baltimore_train.csv')
|
||||
|
||||
data_train, x_train, y_train = get_x_y(train)
|
||||
normalizer = tf.keras.layers.Normalization(axis=1)
|
||||
normalizer.adapt(np.array(x_train))
|
||||
model = Sequential(normalizer)
|
||||
model.add(Dense(64, activation="relu"))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(10, activation='relu'))
|
||||
model.add(Dense(5, activation="softmax"))
|
||||
model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
history = model.fit(
|
||||
x_train,
|
||||
y_train,
|
||||
epochs=epochs,
|
||||
validation_split=validation_split)
|
||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
|
||||
# print(hist)
|
||||
for his in hist.iterrows():
|
||||
print(his[0])
|
||||
# _run.log_scalar('training.loss', his['loss'])
|
||||
# _run.log_scalar('accuracy', his['accuracy'])
|
||||
ex.add_artifact('baltimore_model')
|
||||
"""
|
||||
baltimore_data_test =pd.read_csv('baltimore_test.csv')
|
||||
baltimore_data_test.columns = train.columns
|
||||
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
|
||||
scores = model.evaluate(x_test, y_test)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
y_predicted = model.predict(x_test)
|
||||
y_predicted = np.argmax(y_predicted, axis=1)
|
||||
test_results = {}
|
||||
test_results['Weapon'] = model.evaluate(
|
||||
x_test,
|
||||
y_test, verbose=0)
|
||||
write_list(y_predicted)
|
||||
print('Accuracy : ', scores[1] * 100)
|
||||
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
||||
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
||||
|
||||
data = {
|
||||
'mse': metrics.mean_squared_error(y_test, y_predicted),
|
||||
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
|
||||
'accuracy': scores[1] * 100
|
||||
}
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
_run.log_scalar('rmse', data['rmse'])
|
||||
_run.log_scalar('accuracy', data['accuracy'])
|
||||
"""
|
||||
|
||||
|
||||
|
||||
ex.run()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user