# حفظ عينات التدريب التي ستستخدم لاحقاً np.save('modXtest', X_test) np.save('modytest', y_test) #CNN تصميم شبكة model = Sequential() model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu', input_shape=(width, height, 1), data_format='channels_last', kernel_regularizer=l2(0.01))) model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.5)) model.add(Conv2D(2*num_features, kernel_size=(3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(Conv2D(2*num_features, kernel_size=(3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.5)) model.add(Conv2D(2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(Conv2D(2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.5)) model.add(Conv2D(2*2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(Conv2D(2*2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.5)) model.add(Flatten())
Preview:
downloadDownload PNG
downloadDownload JPEG
downloadDownload SVG
Tip: You can change the style, width & colours of the snippet with the inspect tool before clicking Download!
Click to optimize width for Twitter