test_model.py

from keras.applications.inception_v3 import InceptionV3
# from keras.applications.resnet50 import ResNet50
from keras.applications.mobilenet import MobileNet
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
from keras import backend as K
from keras.models import load_model

import numpy as np
import cv2
# import matplotlib.pyplot as plt

img_size = 150
num_channels = 3

# create the base pre-trained model
# base_model = ResNet50(weights='imagenet', include_top=False)
# base_model = MobileNet(alpha=0.5, include_top=False, input_shape=(img_size, img_size, 3))

# # add a global spatial average pooling layer
# x = base_model.output
# x = GlobalAveragePooling2D()(x)
# # let's add a fully-connected layer
# x = Dense(1024, activation='relu')(x)
# # and a logistic layer -- let's say we have 200 classes
# predictions = Dense(2, activation='softmax')(x)

# # this is the model we will train
# model = Model(inputs=base_model.input, outputs=predictions)

# model.load_weights("./weights-improvement-03-0.86.hdf5")
model = load_model("./weights-improvement-03-0.86.hdf5")
model.summary()

img_path = "./data/test1/1.jpg"

img = cv2.imread(img_path)
img = cv2.cvtColor(img, color=cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_CUBIC)
img = img / 255.0
img = np.reshape(img, (1, img_size, img_size, num_channels))

y = model.predict(img)
print(y)