Updated to the Keras 2.0 API.

classifier_from_little_data_script_1.py

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
- put the cat pictures index 1000-1400 in data/validation/cats
- put the dogs pictures index 12500-13499 in data/train/dogs
- put the dog pictures index 13500-13900 in data/validation/dogs
So that we have 1000 training examples for each class, and 400 validation examples for each class.
In summary, this is our directory structure:
```
data/
    train/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
    validation/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
```
'''

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


# dimensions of our images.
img_width, img_height = 150, 150

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

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(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)

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 = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

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)

model.save_weights('first_try.h5')

Damn the Getúlio Vargas Foundation for demanding this example in a high-level public tender

https://conhecimento.fgv.br/sites/default/files/concursos/auditor-fiscal-da-receita-estadual-tecnologia-da-informacaocns003-tipo-1.pdf

page 7

we will never forget it...

I got this error, please give a bit detail how to solve this problem:
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
:70: UserWarning: Model.fit_generator is deprecated and will be removed in a future version. Please use Model.fit, which supports generators.
model.fit_generator(

ValueError Traceback (most recent call last)
in <cell line: 70>()
68 class_mode='binary')
69
---> 70 model.fit_generator(
71 train_generator,
72 steps_per_epoch=nb_train_samples // batch_size,

2 frames
/usr/local/lib/python3.9/dist-packages/keras/preprocessing/image.py in getitem(self, idx)
101 def getitem(self, idx):
102 if idx >= len(self):
--> 103 raise ValueError(
104 "Asked to retrieve element {idx}, "
105 "but the Sequence "

ValueError: Asked to retrieve element 0, but the Sequence has length 0

RuntimeError Traceback (most recent call last)
in <cell line: 54>()
52 model = Sequential()
53
---> 54 model.fit(
55 train_generator,
56 steps_per_epoch=2000,

1 frames
/usr/local/lib/python3.9/dist-packages/keras/engine/training.py in _assert_compile_was_called(self)
3683 # (i.e. whether the model is built and its inputs/outputs are set).
3684 if not self._is_compiled:
-> 3685 raise RuntimeError(
3686 "You must compile your model before "
3687 "training/testing. "

RuntimeError: You must compile your model before training/testing. Use model.compile(optimizer, loss).