2.15 KB from keras import datasets, Sequential from keras.layers import Conv2D, Dense, MaxPooling2D, Flatten import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix import numpy as np from keras.utils import np_utils from keras.datasets import cifar10 import matplotlib.pyplot as plt from tensorflow.keras.optimizers import Adam from keras.preprocessing import image from keras.preprocessing.image import load_img, img_to_array,array_to_img,ImageDataGenerator import numpy as np import os import cv2 as cv dir_folder = '/content/drive/MyDrive/Train/fruits/Train' path_img = [] labels = [] x_train = [] y_train = [] x_test = [] y_test =[] # tạo lí tạo dữ liệu training for i in os.listdir(dir_folder): path = os.path.join(dir_folder, i) labels.append(str(i)) for j in os.listdir(path): path_img.append(os.path.join(path,j)) y_train.append(labels.index(i)) img = image.load_img(os.path.join(path, j), target_size=(100,100)) img = img_to_array(img) img = img.reshape(100,100,3) img = img.astype('float32') img = img/255 x_train.append(img) #xử lí dữ liệu training x_train = np.array(x_train) y_train = np.array(y_train) y_train = np_utils.to_categorical(y_train, 11) #tạo model model = Sequential() model.add(Conv2D(filters=32, kernel_size=(10,10), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(100,100,3), strides=(2,2))) model.add(MaxPooling2D(2,2)) model.add(Conv2D(filters=64, kernel_size=(2,2), activation='relu', kernel_initializer='he_uniform', padding='same')) model.add(MaxPooling2D(2,2)) model.add(Flatten()) model.add(Dense(1028, activation='relu',input_shape=(30000,), name='layer1')) model.add(Dense(1028, activation='relu', name='layer2')) model.add(Dense(512, activation='relu', name='layer3')) model.add(Dense(11, activation='softmax', name='layer4')) model.summary() model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) history = model.fit(x_train,y_train,epochs=10)