fchollet/classifier_from_little_data_script_1.py

## 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')
	'''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')