keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras.preprocessing.image import ImageDataGenerator
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(units=128, activation='sigmoid'))
model.add(Dropout(0.25))
model.add(Dense(units=2, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
training_set = train_datagen.flow_from_directory('data',target_size=(64, 64), batch_size=100, class_mode='categorical')
test_set = test_datagen.flow_from_directory('data',target_size=(64, 64), batch_size=32,class_mode='categorical')
model.fit_generator(training_set, steps_per_epoch=100, epochs=3, validation_data=test_set, validation_steps=200)
model.save("model.h5")
`

import sys
import numpy as np
from keras.models import load_model
from keras.preprocessing import image
model = load_model('model.h5')
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
img = image.load_img(sys.argv[1],target_size=(64,64))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict_classes(images)
print(classes,model.predict_proba(images))