ahmedbilal · June 18, 2019 09:57 · Jun 18, 2019
diff --git a/nn_training_testing.py b/nn_training_testing.py
@@ -0,0 +1,54 @@
+import keras
+import numpy as np
+from os.path import isfile as is_file_exists
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+from keras.optimizers import Adam
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+
+
+def get_data(filename):
+    _list = []
+    with open(filename, "r") as f:
+        for line in f.readlines():
+            _list.append([int(line) for line in line.split(",")])
+    return np.array(_list)
+
+def get_label(filename):
+    _list = []
+    with open(filename, "r") as f:
+        for label in f.readlines():
+            _list.append(int(label))
+    return np.array(_list)
+
+
+model = Sequential()
+
+# Layers
+model.add(Dense(24,activation='relu'))
+model.add(Dense(7,activation='softmax'))
+
+model.compile(optimizer='adam',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'],
+              batch_size=16)
+
+data = get_data("data.txt")
+labels = get_label("data_labels.txt")
+
+X_train, X_test, y_train, y_test = train_test_split(data, labels,
+                                                    test_size=0.40, random_state=42)
+training_labels = keras.utils.to_categorical(y_train, num_classes=7)
+testing_labels = keras.utils.to_categorical(y_test, num_classes=7)
+
+if not is_file_exists("trained_model"):
+    model.fit(X_train,training_labels,epochs=7)
+    model.save('trained_model')
+else:
+    model = keras.models.load_model('trained_model')
+
+test_loss, test_accuracy = model.evaluate(X_test, testing_labels)
+print("Test Loss", test_loss)
+print("Test Accuracy", test_accuracy)
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