aug_test.py

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)