loadmodel.py

import os,cv2
import numpy as np
import matplotlib.pyplot as plt

from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
import keras
from keras import backend as K
K.set_image_dim_ordering('th')

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import SGD,RMSprop,adam
from keras.models import model_from_json
from scipy.misc import imread,imresize
from PIL import Image

num_channel=1

json_file = open('model1.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model1.h5")
print("Loaded model from disk")

#load the pretrained model and compile it
loaded_model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])
#importing input image
test_image = imread('n183.jpg',flatten=True)
print(test_image.shape)
test_image=cv2.resize(test_image,(120,120))
test_image = np.array(test_image)
test_image = test_image.astype('float32')
test_image /= 255
print (test_image.shape)
   
if num_channel==1:
	if K.image_dim_ordering()=='th':
		test_image= np.expand_dims(test_image, axis=0)
		test_image= np.expand_dims(test_image, axis=0)
		print (test_image.shape)
	else:
		test_image= np.expand_dims(test_image, axis=3) 
		test_image= np.expand_dims(test_image, axis=0)
		print (test_image.shape)



print((loaded_model.predict(test_image)))
print(loaded_model.predict_classes(test_image))