dev

JenkinsfileNeural

@@ -12,7 +12,7 @@ node {
 
         }
         stage('Clone repo') {
-                    docker.image("karopa/ium:05").inside {
+                    docker.image("karopa/ium:06").inside {
                         stage('Test') {
                             checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
                             copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")

neural_network.py

@@ -1,6 +1,7 @@
 import pandas as pd
 import numpy as np
 from keras import optimizers
+from sklearn.metrics import mean_squared_error
 
 from tensorflow import keras
 
@@ -28,18 +29,20 @@ print(max_val_like)
 
 
 model = keras.Sequential([
-    keras.layers.Dense(32,input_dim = X.shape[1], activation='relu'),
+    keras.layers.Dense(512,input_dim = X.shape[1], activation='relu'),
-    keras.layers.Dense(64, activation='relu'),
+    keras.layers.Dense(256, activation='relu'),
-    keras.layers.Dense(1,activation='relu'),
+    keras.layers.Dense(256, activation='relu'),
+    keras.layers.Dense(128, activation='relu'),
+    keras.layers.Dense(1,activation='linear'),
 ])
 
 model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
 
-model.fit(X, y, epochs=15, validation_split = 0.3)
+model.fit(X, y, epochs=30, validation_split = 0.3)
 
 data = pd.read_csv("data_dev", sep=',', error_bad_lines=False).dropna()
-X_test = data.loc[:,data.columns == "2805317"].astype(int)
+X_test = data.loc[:,data.columns == "440265"].astype(int)
-y_test = data.loc[:,data.columns == "198909"].astype(int)
+y_test = data.loc[:,data.columns == "21629"].astype(int)
 
 min_val_sub = np.min(X_test)
 max_val_sub = np.max(X_test)
@@ -49,13 +52,19 @@ print(max_val_sub)
 
 min_val_like = np.min(y_test)
 max_val_like = np.max(y_test)
-y_test = (y_test - min_val_like) / (max_val_like - min_val_like)
-
 print(min_val_like)
 print(max_val_like)
 
 prediction = model.predict(X_test)
 
+prediction_denormalized = []
+for pred in prediction:
+    denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
+    prediction_denormalized.append(denorm)
+
 f = open("predictions.txt", "w")
-for (pred, test) in zip(prediction, y_test.values):
+for (pred, test) in zip(prediction_denormalized, y_test.values):
-    f.write("predicted: %s expected: %s\n" % (str(pred), str(test)))
+    f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
+
+error = mean_squared_error(y_test, prediction_denormalized)
+print(error)

predictions.txt

@@ -0,0 +1,406 @@
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