76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import wget
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import numpy as np
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from sacred.observers import FileStorageObserver
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from sacred import Experiment
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from datetime import datetime
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import os
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ex = Experiment("file_observer", interactive=True)
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ex.observers.append(FileStorageObserver('Zajęcia7/my_runs'))
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@ex.config
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def my_config():
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train_size_param = 0.8
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test_size_param = 0.2
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@ex.capture
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def prepare_model(train_size_param, test_size_param, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
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wget.download(url, out='Zajęcia7/winequality-red.csv', bar=None)
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wine=pd.read_csv('Zajęcia7/winequality-red.csv')
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wine
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y = wine.quality
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y.head()
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x = wine.drop(['quality'], axis= 1)
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x.head()
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x=((x-x.min())/(x.max()-x.min())) #Normalizacja
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=test_size_param, train_size=train_size_param, random_state=21)
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def regression_model():
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model = Sequential()
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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model.save('Zajęcia7/saved_model')
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y_pred = model.predict(x_test)
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y_pred[:5]
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y_pred = np.around(y_pred, decimals=0)
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y_pred[:5]
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print(accuracy_score(y_test, y_pred))
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_run.info["Final Results: "] = classification_report(y_test,y_pred)
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return(classification_report(y_test,y_pred))
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@ex.main
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def my_main(train_size_param, test_size_param):
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print(prepare_model()) ## Nie musimy przekazywać wartości
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r = ex.run()
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ex.add_artifact("Zajęcia7/saved_model/saved_model.pb") |