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August 3, 2018 08:31
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| from keras.models import load_model | |
| from keras.preprocessing.image import ImageDataGenerator | |
| from keras.models import Sequential | |
| from keras.layers import Conv2D, MaxPooling2D | |
| from keras.layers import Activation, Dropout, Flatten, Dense | |
| from keras import backend as K | |
| import cv2 | |
| import numpy as np | |
| img_width, img_height = 150, 150 | |
| train_data_dir = 'flower_photos/train' | |
| validation_data_dir = 'flower_photos/validation' | |
| nb_train_samples = 2929 | |
| nb_validation_samples = 741 | |
| epochs = 50 | |
| batch_size = 16 | |
| if K.image_data_format() == 'channels_first': | |
| input_shape = (3, img_width, img_height) | |
| else: | |
| input_shape = (img_width, img_height, 3) | |
| def create_model(): | |
| model = Sequential() | |
| model.add(Conv2D(32, (3, 3), input_shape=input_shape)) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Conv2D(32, (3, 3))) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Conv2D(64, (3, 3))) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Flatten()) | |
| model.add(Dense(64)) | |
| model.add(Activation('relu')) | |
| model.add(Dropout(0.5)) | |
| model.add(Dense(5)) | |
| model.add(Activation('sigmoid')) | |
| return model | |
| model = create_model() | |
| model = model.load_weights('first_try.h5') | |
| print(type(model)) | |
| img = cv2.imread('test.jpg') | |
| img = cv2.resize(img,(150,150)) | |
| img = np.reshape(img,[1,150,150,3]) | |
| classes = model.predict_classes(model,img) | |
| print (classes) | |
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