machine_learning/neuralNetwork.py

@@ -1,38 +0,0 @@
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Sequential
-from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
-
-train_data_dir = "garbage_photos"
-
-input_shape = (150, 150, 3)
-num_classes = 5
-batch_size = 32
-epochs = 20
-
-train_datagen = ImageDataGenerator(rescale=1./255)
-
-train_generator = train_datagen.flow_from_directory(
-    train_data_dir,
-    target_size=(input_shape[0], input_shape[1]),
-    batch_size=batch_size,
-    class_mode='categorical'
-)
-
-model = Sequential()
-model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Conv2D(64, (3, 3), activation='relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Conv2D(128, (3, 3), activation='relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Flatten())
-model.add(Dense(128, activation='relu'))
-model.add(Dense(num_classes, activation='softmax'))
-
-model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
-
-model.fit(train_generator, epochs=epochs)
-
-classes = train_generator.class_indices
-
-model.save("neuralModel.h5")