Classification_human_or_horse.py

import pickle

import tensorflow as tf

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(
    "../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

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")