horse_and_human_classification.py

!wget --no-check-certificate \
    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \
    -O /tmp/horse-or-human.zip

import os
import zipfile

local_zip = '/tmp/horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/horse-or-human')
zip_ref.close()

# Directory with our training horse pictures
train_horse_dir = os.path.join('/tmp/horse-or-human/horses')

# Directory with our training human pictures
train_human_dir = os.path.join('/tmp/horse-or-human/humans')

train_horse_names = os.listdir(train_horse_dir)
print(train_horse_names[:10])

train_human_names = os.listdir(train_human_dir)
print(train_human_names[:10])

print('total training horse images:', len(os.listdir(train_horse_dir)))
print('total training human images:', len(os.listdir(train_human_dir)))

import tensorflow as tf

model = tf.keras.models.Sequential([
    # This is the first convolution
    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    # The second convolution
    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    # The third convolution
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    # The fourth convolution
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    # The fifth convolution
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    # Flatten the results to feed into a DNN
    tf.keras.layers.Flatten(),
    # 512 neuron hidden layer
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.summary()

from tensorflow.keras.optimizers import RMSprop

model.compile(loss='binary_crossentropy',
              optimizer=RMSprop(lr=0.001),
              metrics=['acc'])

from tensorflow.keras.preprocessing.image import ImageDataGenerator


train_datagen = ImageDataGenerator(rescale=1/255)

train_generator = train_datagen.flow_from_directory(
        '/tmp/horse-or-human/',  
        target_size=(300, 300),  
        batch_size=128,
        class_mode='binary')

history = model.fit_generator(
      train_generator,
      steps_per_epoch=8,  
      epochs=12,
      verbose=1)

import numpy as np
from google.colab import files
from keras.preprocessing import image

uploaded = files.upload()

for fn in uploaded.keys():
  path = '/content/' + fn
  img = image.load_img(path, target_size=(300, 300))
  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(fn + " is a human")
  else:
    print(fn + " is a horse")