120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
import tensorflow as tf
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
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from keras.models import Sequential
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from keras.layers import Dense, Dropout
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from keras import regularizers
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import numpy as np
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import pandas as pd
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from datetime import datetime
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from importlib.metadata import version
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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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ex = Experiment("464903", interactive=True)
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def my_config():
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num_epochs = 100
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dropout_layer_value = 0.4
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@ex.capture
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def prepare_info(num_epochs, dropout_layer_value, _run):
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_run.info["num_epochs"] = num_epochs
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_run.info["dropout_layer_value"] = dropout_layer_value
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_run.info["training_ts"] = datetime.now()
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@ex.main
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def train_and_evaluate(num_epochs, dropout_layer_value, _run):
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prepare_info()
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ex.open_resource('./lettuce_dataset_updated.csv', "r")
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dataset = pd.read_csv('./lettuce_dataset_updated.csv', encoding="ISO-8859-1")
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print(version('tensorflow'))
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print(version('scikit-learn'))
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print(version('keras'))
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print(version('numpy'))
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print(version('pandas'))
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ph_level = dataset['pH Level'].values.tolist()
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temp_F = dataset['Temperature (F)'].values.tolist()
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humid = dataset['Humidity'].values.tolist()
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days = dataset['Growth Days'].values.tolist()
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plant_id = dataset['Plant_ID'].values.tolist()
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X = []
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Y = []
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id = plant_id[0]
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temp_sum = 0
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humid_sum = 0
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ph_level_sum = 0
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day = 1
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for i in range(0, len(plant_id)):
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if plant_id[i] == id:
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temp_sum += temp_F[i]
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humid_sum += humid[i]
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ph_level_sum += ph_level[i]
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day = days[i]
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else:
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temp = []
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temp.append(temp_sum/day)
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temp.append(humid_sum/day)
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temp.append(ph_level_sum/day)
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X.append(temp)
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Y.append(day)
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temp_sum = 0
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humid_sum = 0
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ph_level_sum = 0
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day = 1
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id = plant_id[i]
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scaler = MinMaxScaler()
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X = scaler.fit_transform(X)
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X = np.array(X)
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Y = np.array(Y)
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encoder = OneHotEncoder(sparse=False)
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y_onehot = encoder.fit_transform(Y.reshape(-1,1))
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X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.4, random_state=42)
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model = Sequential([
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Dense(8, activation='relu', input_dim=3, kernel_regularizer=regularizers.l2(0.04)),
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Dropout(dropout_layer_value),
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Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.04)),
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Dropout(dropout_layer_value),
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Dense(4, activation='softmax', kernel_regularizer=regularizers.l2(0.04)),
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])
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model.compile(optimizer='sgd',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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history = model.fit(X_train, y_train, epochs=num_epochs, validation_data=(X_test, y_test), verbose=2)
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test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=2)
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print(history.history['val_accuracy'])
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_run.log_scalar("train loss", history.history['loss'])
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_run.log_scalar("train accuracy", history.history['accuracy'])
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_run.log_scalar("test loss", test_loss)
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_run.log_scalar("test accuracy", test_accuracy)
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print(f"Dokładność testowa: {test_accuracy:.2%}")
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model.evaluate(X_test, y_test)[1]
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model.save('./model.keras')
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ex.add_artifact("./model.keras")
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ex.run()
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print(my_config()) |