diff --git a/Task 3/train_model.py b/Task 3/train_model.py
new file mode 100644
index 0000000..ad5707a
--- /dev/null
+++ b/Task 3/train_model.py	
@@ -0,0 +1,52 @@
+from keras.models import Sequential
+from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense
+
+classifier = Sequential()
+
+classifier.add(Convolution2D(32, (3, 3), input_shape=(64, 64, 1), activation='relu'))
+classifier.add(MaxPooling2D(pool_size=(2, 2)))
+
+classifier.add(Convolution2D(32, (3, 3), activation='relu'))
+classifier.add(MaxPooling2D(pool_size=(2, 2)))
+
+classifier.add(Flatten())
+
+classifier.add(Dense(units=128, activation='relu'))
+classifier.add(Dense(units=6, activation='softmax'))
+
+classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+
+from keras.preprocessing.image import ImageDataGenerator
+
+train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+
+test_datagen = ImageDataGenerator(rescale=1./255) #epoch
+
+training_set = train_datagen.flow_from_directory('data/train',
+                                                 target_size=(64, 64),
+                                                 batch_size=5,
+                                                 color_mode='grayscale',
+                                                 class_mode='categorical')
+
+test_set = test_datagen.flow_from_directory('data/test',
+                                            target_size=(64, 64),
+                                            batch_size=5,
+                                            color_mode='grayscale',
+                                            class_mode='categorical') 
+
+classifier.fit_generator(
+        training_set,
+        epochs=10,
+        validation_data=test_set)
+
+#Saving
+model_json = classifier.to_json()
+with open("model-bw.json", "w") as json_file:
+    json_file.write(model_json)
+classifier.save_weights('model-bw.h5')
+