Sacred

IUM_7_Sacred.py

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+import tensorflow as tf
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+from keras import regularizers
+import numpy as np
+import pandas as pd
+from datetime import datetime
+from importlib.metadata import version
+
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+
+ex = Experiment("464903", interactive=True)
+ex.observers.append(FileStorageObserver('my_runs'))
+
+@ex.config
+def my_config():
+    num_epochs = 200
+    dropout_layer_value = 0.3  
+
+@ex.capture
+def prepare_info(num_epochs, dropout_layer_value, _run):
+    _run.info["num_epochs"] = num_epochs
+    _run.info["dropout_layer_value"] = dropout_layer_value
+    _run.info["training_ts"] = datetime.now()
+
+@ex.main
+def train_and_evaluate(num_epochs, dropout_layer_value, _run):
+    prepare_info()
+
+    ex.open_resource('./lettuce_dataset_updated.csv', "r")
+
+    dataset = pd.read_csv('./lettuce_dataset_updated.csv', encoding="ISO-8859-1")
+
+    print(version('tensorflow')) 
+    print(version('scikit-learn'))
+    print(version('keras'))
+    print(version('pandas'))
+
+    ph_level = dataset['pH Level'].values.tolist()
+    temp_F = dataset['Temperature (F)'].values.tolist()
+    humid = dataset['Humidity'].values.tolist()
+    days = dataset['Growth Days'].values.tolist()
+    plant_id = dataset['Plant_ID'].values.tolist()
+
+    X = []
+    Y = []
+
+    id = plant_id[0]
+    temp_sum = 0
+    humid_sum = 0
+    ph_level_sum = 0
+    day = 1
+
+    for i in range(0, len(plant_id)):
+        if plant_id[i] == id:
+            temp_sum += temp_F[i]
+            humid_sum += humid[i]
+            ph_level_sum += ph_level[i]
+            day = days[i]
+        else:
+            temp = []
+            temp.append(temp_sum/day)
+            temp.append(humid_sum/day)
+            temp.append(ph_level_sum/day)
+            X.append(temp)
+            Y.append(day)
+            temp_sum = 0
+            humid_sum = 0
+            ph_level_sum = 0
+            day = 1
+            id = plant_id[i]
+
+    scaler = MinMaxScaler()
+    X = scaler.fit_transform(X)
+
+    X = np.array(X)
+    Y = np.array(Y)
+
+    encoder = OneHotEncoder(sparse=False)
+    y_onehot = encoder.fit_transform(Y.reshape(-1,1))
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.4, random_state=42)
+
+    model = Sequential([
+        Dense(8, activation='relu', input_dim=3, kernel_regularizer=regularizers.l2(0.04)),
+        Dropout(dropout_layer_value),
+        Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.04)),
+        Dropout(dropout_layer_value),
+        Dense(4, activation='softmax', kernel_regularizer=regularizers.l2(0.04)),
+    ])
+
+    model.compile(optimizer='sgd',
+                loss='categorical_crossentropy',
+                metrics=['accuracy'])
+
+    history = model.fit(X_train, y_train, epochs=num_epochs, validation_data=(X_test, y_test), verbose=2)
+
+    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=2)
+
+    print(history.history['val_accuracy'])
+
+    _run.log_scalar("train loss", history.history['loss'])
+    _run.log_scalar("train accuracy", history.history['accuracy'])
+    _run.log_scalar("test loss", test_loss)
+    _run.log_scalar("test accuracy", test_accuracy)
+
+    print(f"Dokładność testowa: {test_accuracy:.2%}")
+
+    model.evaluate(X_test, y_test)[1]
+
+    model.save('./model.keras') 
+    ex.add_artifact("./model.keras")
+
+ex.run()
+print(my_config())