import tensorflow as tf
from tensorflow import keras
import pickle
model=tf.keras.models.Sequential([tf.keras.layers.Conv2D(16,(3,3),activation='relu',input_shape=(200,200,3)),
                                  tf.keras.layers.MaxPooling2D(2,2),
                                  tf.keras.layers.Conv2D(16,(3,3),activation='relu'),
                                  tf.keras.layers.MaxPooling2D(2,2),
                                  tf.keras.layers.Conv2D(16,(3,3),activation='relu'),
                                  tf.keras.layers.MaxPooling2D(2,2),
                                  tf.keras.layers.Flatten(),
                                  tf.keras.layers.Dense(512,activation='relu'),
                                  tf.keras.layers.Dense(1,activation="sigmoid")
                                  ])
model.summary()
from tensorflow.keras.optimizers import RMSprop
model.compile(optimizer=RMSprop(lr=0.001),loss='binary_crossentropy',metrics=['acc'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen=ImageDataGenerator(rescale=1/255)
train_generator=train_datagen.flow_from_directory('E:/ARYAN/Desktop/python_tensorflow/Classification_human-or-horse',
                                                  target_size=(200,200),
                                                  batch_size=222,
                                                  class_mode='binary')
model.fit_generator(train_generator,steps_per_epoch=6,epochs=1,verbose=1)
filename="myTf1.sav"
pickle.dump(model,open(filename,'wb'))

from tkinter import Tk
from tkinter.filedialog import askopenfilename
from keras.preprocessing import image
import numpy as np
import cv2

Tk().withdraw() 
filename = askopenfilename() 
print(filename)
img = image.load_img(filename, target_size=(200, 200))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict(images, batch_size=10)
print(classes[0])
if classes[0]>0.5:
    print(filename + " is a human")
else:
    print(filename + " is a horse")