Используйте hyperopt (или hyperas) с Keras и ImageDataGenerator "flow_from_directory" - PullRequest
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Используйте hyperopt (или hyperas) с Keras и ImageDataGenerator "flow_from_directory"

0 голосов
/ 03 октября 2019

Я делаю код для обучения CNN (используя Keras ) с поиском гиперпараметров, используя Hyperas . Для обучения модели я использую flow_from_directory функцию ImageDataGenerator .

Я прочитал много постов и документации, которые нашел в интернете, но мой код неработай. Я не понимаю, почему.

Ниже мой код: 

'''
# Installation d'hyperas
!pip install hyperas

# Accès aux fichiers de gDrive
from google.colab import drive
drive.mount('/content/gdrive')

# Copie de la class data_gen.py sur la racine de gColab
!cp '/content/gdrive/My Drive/Deep- 
learning/Projets/CNN_dogs&cats/CNN_cats&dogs_2600_hyperopt_gColab.ipynb' '/content/'

# Importation des librairies
from keras.preprocessing.image import ImageDataGenerator

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

from keras import backend as K

import matplotlib.pyplot as plt

import numpy as np

from keras.optimizers import SGD

from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform

# Variables
img_width, img_height = 150, 150

nb_train_samples = 1855
nb_validation_samples = 745
nb_test_samples = 2750

epochs=20
batch_size=16

test_data_dir = '/content/gdrive/My Drive/Deep-learning/Projets/CNN_dogs&cats/data/PetImages/test'

def data():       
    train_data_dir = '/content/gdrive/My Drive/Deep-learning/Projets/CNN_dogs&cats/data/PetImages_2600/train'
    validation_data_dir = '/content/gdrive/My Drive/Deep- 
learning/Projets/CNN_dogs&cats/data/PetImages_2600/validation'

    # Instanciation des générateurs d'images train
    train_datagen = ImageDataGenerator(rescale=1. / 255)
    validation_datagen = ImageDataGenerator(rescale=1. / 255)

    # Instanciation des générateurs
    train_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary')
    validation_generator = validation_datagen.flow_from_directory(validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary')

    return train_generator, validation_generator

def model(train_generator, validation_generator):
    # Vérification du format des images
    if K.image_data_format() == 'channels_first':
        input_shape = (3, img_width, img_height)
    else:
        input_shape = (img_width, img_height, 3)

    # Construction du modèle
    model = Sequential()
    model.add(Conv2D(32, (3, 3), input_shape=input_shape))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(64))
    model.add(Activation('relu'))
    model.add(Dropout({{choice([0.1, 0.2, 0.3, 0.4, 0.5])}}))
    model.add(Dense(1))
    model.add(Activation({{choice(['relu', 'sigmoid'])}}))

    model.compile(loss='binary_crossentropy', optimizer=SGD(lr={{uniform([0, 1])}}), metrics=['accuracy'])

    # Lancement de la phase d'apprentissage sur la base de train
    model.fit_generator(train_generator, steps_per_epoch=nb_train_samples // batch_size, epochs=epochs, validation_data=validation_generator, validation_steps=nb_validation_samples // batch_size)

    score, acc = model.evaluate_generator(generator=validation_generator, steps=nb_validation_samples // batch_size)

    return {'loss': -acc, 'status': STATUS_OK, 'model': model}

if __name__ == '__main__':

    train_generator, validation_generator = data()

    best_run, best_model = optim.minimize(model=model, data=data, algo=tpe.suggest, max_evals=10, notebook_name='CNN_cats&dogs_2600_hyperopt_gColab', trials=Trials())

    print('Evaluation of best performing model:')

    print(best_model.evaluate(validation_generator))

'' '

На линии: 

best_run, best_model = optim.minimize(model=model, data=data, algo=tpe.suggest, max_evals=10, notebook_name='CNN_cats&dogs_2600_hyperopt_gColab', trials=Trials())

У меня естьэто сообщение об ошибке:

/ usr / local / lib / python3.6 / dist-packages / hyperas / optim.py в retrieve_data_string (data, verbose) 

219     data_string = inspect.getsource(data)

220     first_line = data_string.split("\n")[0]

---> 221     indent_length = len(determine_indent(data_string))

222     data_string = data_string.replace(first_line, "")

223     r = re.compile(r'^\s*return.*'

TypeError: object of type 'NoneType' has no len()
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