takahish

from keras.callbacks import ModelCheckpoint

def train_model(model, train_tensors, train_targets, valid_tensors, valid_targets, save_filepath, epochs=20, batch_size=20):
    """Fit model to train dataset, and check accuracy for valid dataset.
    
    Args:
        model: complied keras model
        train_tensors: train datatset
        train_targets: train targets
        valid_tensors: valid dataset

takahish

from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential

model = Sequential()

model.add(Conv2D(32, 3, padding='same', activation='relu', input_shape=(224, 224, 3)))
model.add(Conv2D(32, 3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))

takahish

human_files_short = human_files[:100]
dog_files_short = train_files[:100]

print('human_files: ' + str(sum([1 for human_file in human_files_short if dog_detector(human_file) == True])) + '%')
print('dog_files: ' + str(sum([1 for dog_file in dog_files_short if dog_detector(dog_file) == True])) + '%')

takahish

### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    """The function returns True if a dog is detected in an image (and False if not).
    In order to check to see if an image is predicted to contain a dog by the
    pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels
    function above returns a value between 151 and 268 (inclusive).
    
    Args:
        img_path: string. a file path to a color images.
    

takahish

from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    """This is accomplished with the predict method, which returns an array
    whose i-th entry is the model's predicted probability that the image
    belongs to the i-th ImageNet category.
    
    Args:
        img_path: string. a file path to a color images.
    

takahish

from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    """The path_to_tensor function below takes a string-valued file path to a color
    image as input and returns a 4D tensor suitable for supplying to a Keras CNN.
    
    Args:
        img_path: string. a file path to a color images.
    

takahish

from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

takahish

human_files_short = human_files[:100]
dog_files_short = train_files[:100]

print("human_files: " + str(sum([1 for human_file in human_files_short if face_detector(human_file) == True])) + "%")
print("dog_files: " + str(sum([1 for dog_file in dog_files_short if face_detector(dog_file) == True])) + "%")

takahish

# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    """Returns True if a human face is detected in an image and False otherwise.
    
    Args:
        img_path: string. a file path to a color images.
    
    Returns:
        boolean, images show a human or not.
    """

takahish

import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)