| from tensorflow.keras.models import Sequential |
| from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Dropout, SpatialDropout2D |
| from tensorflow.keras.losses import sparse_categorical_crossentropy, binary_crossentropy |
| from tensorflow.keras.optimizers import Adam |
| from tensorflow.keras.preprocessing.image import ImageDataGenerator |
| import numpy as np |
| from PIL import Image |
|
|
| def gen_labels(): |
| train = 'Data/Train' |
| train_generator = ImageDataGenerator(rescale = 1/255) |
|
|
| train_generator = train_generator.flow_from_directory(train, |
| target_size = (300,300), |
| batch_size = 32, |
| class_mode = 'sparse') |
| labels = (train_generator.class_indices) |
| labels = dict((v,k) for k,v in labels.items()) |
|
|
| return labels |
|
|
| def preprocess(image): |
| image = np.array(image.resize((300, 300), Image.ANTIALIAS)) |
| image = np.array(image, dtype='uint8') |
| image = np.array(image)/255.0 |
|
|
| return image |
|
|
| def model_arc(): |
| model=Sequential() |
|
|
| |
| model.add(Conv2D(32, kernel_size = (3,3), padding='same',input_shape=(300,300,3),activation='relu')) |
| model.add(MaxPooling2D(pool_size=2)) |
|
|
| model.add(Conv2D(64, kernel_size = (3,3), padding='same',activation='relu')) |
| model.add(MaxPooling2D(pool_size=2)) |
|
|
| model.add(Conv2D(32, kernel_size = (3,3), padding='same',activation='relu')) |
| model.add(MaxPooling2D(pool_size=2)) |
|
|
| |
| model.add(Flatten()) |
|
|
| model.add(Dense(64,activation='relu')) |
| model.add(Dropout(0.2)) |
| model.add(Dense(32,activation='relu')) |
|
|
| model.add(Dropout(0.2)) |
| model.add(Dense(6,activation='softmax')) |
|
|
| return model |
|
|
