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1 files changed, 53 insertions, 0 deletions

diff --git a/backend/microservice/regression.py b/backend/microservice/regression.py
new file mode 100644
index 00000000..f039292c
--- /dev/null
+++ b/backend/microservice/regression.py
@@ -0,0 +1,53 @@
+import numpy as np
+from tensorflow.keras.datasets import boston_housing
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import load_model
+
+x_new = np.random.rand(X_train.shape[1])
+x_new = x_new.reshape(1,-1)
+# x_new.shape
+y_pred = model.predict(x_new)
+if y_pred < 0.5:
+    print('0')
+else:
+    print('1')
+
+(X_train, y_train), (X_test, y_test) = boston_housing.load_data()
+
+X_train.shape
+X_test.shape
+X_train[0]
+y_test
+
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train = scaler.transform(X_train)
+X_test = scaler.transform(X_test)
+
+X_train[0]
+X_test
+
+model = Sequential()
+model.add(Dense(input_dim=X_train.shape[1], units=100, activation='relu'))
+# model.add(Dense(input_dim=X_train.shape[1], units=50, activation='relu'))
+model.add(Dense(units=1))
+model.summary()
+
+model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+history = model.fit(X_train, y_train, batch_size=32, epochs=20, validation_split=0.2)
+
+plt.plot(history.epoch, history.history['loss'])
+plt.plot(history.epoch, history.history['val_loss'])
+
+plt.plot(history.epoch, history.history['mae'])
+plt.plot(history.epoch, history.history['val_mae'])
+
+model.evaluate(X_test, y_test)
+model.save('models/boston.h5')
+old_model = load_model('models/boston.h5')
+
+x_new = np.random.rand(X_train.shape[1])
+x_new = x_new.reshape(1, -1)
+x_new.shape
+# Za nove podatke koristi se predict tako da se dobije predvidjena vrednost za y
+old_model.predict(x_new)
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